business intelligence

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pages: 233 words: 67,596

Competing on Analytics: The New Science of Winning by Thomas H. Davenport, Jeanne G. Harris

always be closing, Apollo 13, big data - Walmart - Pop Tarts, business intelligence, business logic, business process, call centre, commoditize, data acquisition, digital map, en.wikipedia.org, fulfillment center, global supply chain, Great Leap Forward, high net worth, if you build it, they will come, intangible asset, inventory management, iterative process, Jeff Bezos, job satisfaction, knapsack problem, late fees, linear programming, Moneyball by Michael Lewis explains big data, Netflix Prize, new economy, performance metric, personalized medicine, quantitative hedge fund, quantitative trading / quantitative finance, recommendation engine, RFID, search inside the book, shareholder value, six sigma, statistical model, supply-chain management, text mining, The future is already here, the long tail, the scientific method, traveling salesman, yield management

Smaller data warehouses were called data marts. Today, as we mentioned, the entire field is often referred to with the term business intelligence and incorporates the collection, management, and reporting of decision-oriented data as well as the analytical techniques and computing approaches that are performed on the data. Business intelligence overall is a broad and popular field within the IT industry—in fact, a 2006 Gartner survey of 1,400 chief information officers suggests that business intelligence is the number one technology priority for IT organizations.8 Two studies of large organizations using ERP systems that we did in 2002 and 2006 revealed that better decision making was the primary benefit sought, and (in 2006) analytics were the technology most sought to take advantage of the ERP data.

For example, in a 2005 survey of 220 organizations’ approaches to the management of business intelligence (which, remember, also includes some nonanalytical activities, such as reporting), only 45 percent said that their use of business intelligence was either “organizational” or “global,” with 53 percent responding “in my department,” “departmental,” “regional,” or “individual.” In the same survey, only 22 percent of firms reported a formal needs assessment process across the enterprise; 29 percent did no needs assessment at all; and 43 percent assessed business intelligence needs at the divisional or departmental level.3 The reasons for this decentralization are easy to understand.

To make sure the IT environment fully addresses an organization’s needs at each stage of analytical competition, companies must incorporate analytics and other business intelligence technologies into their overall IT architecture. (Refer to the box “Data and IT Capability by Stage of Analytical Competition.”) As we pointed out in chapter 1, technologists use the term business intelligence (often shortened as BI) to encompass not only analytics—the use of data to analyze, forecast, predict, optimize, and so on—but also the processes and technologies used for collecting, managing, and reporting decision-oriented data. The business intelligence architecture (a subset of the overall IT architecture) is an umbrella term for an enterprise-wide set of systems, applications, and governance processes that enable sophisticated analytics, by allowing data, content, and analyses to flow to those who need it, when they need it.


pages: 227 words: 32,306

Using Open Source Platforms for Business Intelligence: Avoid Pitfalls and Maximize Roi by Lyndsay Wise

barriers to entry, business intelligence, business process, call centre, cloud computing, commoditize, different worldview, en.wikipedia.org, Just-in-time delivery, knowledge worker, Richard Stallman, Salesforce, software as a service, statistical model, supply-chain management, the market place

Once an organization determines this, they can narrow their search to the solutions that meet these needs. Although the other delivery methods are outside the scope of this book, your company may still want to consider a mix of solutions. Therefore, it makes sense to look at how business intelligence and OS intersect. The components of business intelligence Before we expand to look at the OS market, let’s look a little deeper at how business intelligence pieces fit together by taking a closer look at Figure 1-1 above. The data warehouse represents where all of the information for later use is stored. Figure 1-2 shows this on a high level. The data marts so valued by both Inmon and Kimball are what make up the whole of the data warehouse.

Using Open Source Platforms for Business Intelligence This page intentionally left blank Using Open Source Platforms for Business Intelligence Avoid Pitfalls and Maximize ROI Lyndsay Wise AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Morgan Kaufmann is an imprint of Elsevier Acquiring Editor: Andrea Dierna Development Editor: Robyn Day Project Manager: Paul Gottehrer Designer: Alisa Andreola Morgan Kaufmann is an imprint of Elsevier 225 Wyman Street, Waltham, MA 02451, USA Copyright r 2012 Elsevier Inc. All rights reserved No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

I would like to thank the following people and the companies they work for, for either helping to organize interviews and/or take the time to educate me further on specific products, open source outlooks, OSBI use, and general insights: Katie Hutchison, Jessica Swain, Greg Wood, Ketan Karia, Stefano Scamuzzo, Steve Sarsfield, Jill Hara, Susan Davis, Don DeLoach, Yves de Montcheuil, Mike Boyarski, Kim Leadley, Eduardo Paredes, Gerardo Macias, William McKnight, John Kearney, Zibby Keaton, Bruce Belvin, Rebecca Shomair, Kerstin Stephan, Holger Wegstein, and Benjamin Hohmann. In addition, there are several more companies that participated in case studies by sharing their experiences and outlook on open source deployments and their use of business intelligence without their input it would not have been possible to provide insight into the real world applications of open source business intelligence. I would also like to thank Cindi Howson, Howard Dresner, David Loshin, Shawn Rogers, and Wayne Eckerson for sharing their experiences and advice unrestrictedly. And lastly, I would like to thank Gail Wise, for providing advice, support and lending an ear throughout the writing and reviewing process.


pages: 133 words: 42,254

Big Data Analytics: Turning Big Data Into Big Money by Frank J. Ohlhorst

algorithmic trading, bioinformatics, business intelligence, business logic, business process, call centre, cloud computing, create, read, update, delete, data acquisition, data science, DevOps, extractivism, fault tolerance, information security, Large Hadron Collider, linked data, machine readable, natural language processing, Network effects, pattern recognition, performance metric, personalized medicine, RFID, sentiment analysis, six sigma, smart meter, statistical model, supply-chain management, warehouse automation, Watson beat the top human players on Jeopardy!, web application

How Retailers Engage Consumers with Social Media and Mobility by Bernie Brennan and Lori Schafer Business Analytics for Customer Intelligence by Gert Laursen Business Analytics for Managers: Taking Business Intelligence beyond Reporting by Gert Laursen and Jesper Thorlund The Business Forecasting Deal: Exposing Bad Practices and Providing Practical Solutions by Michael Gilliland Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy by Olivia Parr Rud CIO Best Practices: Enabling Strategic Value with Information Technology, Second Edition by Joe Stenzel Connecting Organizational Silos: Taking Knowledge Flow Management to the Next Level with Social Media by Frank Leistner Credit Risk Assessment: The New Lending System for Borrowers, Lenders, and Investors by Clark Abrahams and Mingyuan Zhang Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring by Naeem Siddiqi The Data Asset: How Smart Companies Govern Their Data for Business Success by Tony Fisher Demand-Driven Forecasting: A Structured Approach to Forecasting by Charles Chase Executive’s Guide to Solvency II by David Buckham, Jason Wahl, and Stuart Rose The Executive’s Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business by David Thomas and Mike Barlow Fair Lending Compliance: Intelligence and Implications for Credit Risk Management by Clark R.

Library of Congress Cataloging-in-Publication Data: Ohlhorst, Frank, 1964– Big data analytics : turning big data into big money / Frank Ohlhorst. p. cm. — (Wiley & SAS business series) Includes index. ISBN 978-1-118-14759-7 (cloth) — ISBN 978-1-118-22582-0 (ePDF) — ISBN 978-1-118-26380-8 (Mobi) — ISBN 978-1-118-23904-9 (ePub) 1. Business intelligence. 2. Data mining. I. Title. HD38.7.O36 2013 658.4'72—dc23 2012030191 Preface What are data? This seems like a simple enough question; however, depending on the interpretation, the definition of data can be anything from “something recorded” to “everything under the sun.” Data can be summed up as everything that is experienced, whether it is a machine recording information from sensors, an individual taking pictures, or a cosmic event recorded by a scientist.

Fortunately, those who have come up with the technologies to mitigate these storage concerns have also come up with a way to derive value from what many see as a burden. It is a process called Big Data analytics. The concepts behind Big Data analytics are actually nothing new. Businesses have been using business intelligence tools for many decades, and scientists have been studying data sets to uncover the secrets of the universe for many years. However, the scale of data collection is changing, and the more data you have available, the more information you can extrapolate from them. The challenge today is to find the value of the data and to explore data sources in more interesting and applicable ways to develop intelligence that can drive decisions, find relationships, solve problems, and increase profits, productivity, and even the quality of life.


pages: 319 words: 89,192

Spooked: The Trump Dossier, Black Cube, and the Rise of Private Spies by Barry Meier

Airbnb, business intelligence, citizen journalism, Citizen Lab, commoditize, coronavirus, corporate raider, COVID-19, digital map, disinformation, Donald Trump, fake news, false flag, forensic accounting, global pandemic, Global Witness, index card, Jeffrey Epstein, Julian Assange, Londongrad, medical malpractice, NSO Group, offshore financial centre, opioid epidemic / opioid crisis, Ponzi scheme, Ronald Reagan, Russian election interference, Silicon Valley, Silicon Valley startup, Skype, SoftBank, sovereign wealth fund, Steve Jobs, WikiLeaks

Meanwhile, lawsuits were starting to pile up against Fusion GPS and Orbis Business Intelligence. In 2017, the three founders of Alfa Bank sued Fusion GPS and Orbis, charging that the information Steele had reported about them was defamatory. Separately, BuzzFeed and Steele’s firm were sued by the Russian owner of several internet service providers who was described in a final memo Steele wrote after the 2016 election as a participant in Moscow’s clandestine cyber attack against Hillary Clinton’s campaign. The lawsuit against BuzzFeed was dismissed. And to defend themselves against the Alfa Bank–connected actions, Fusion GPS and Orbis Business Intelligence, which both denied any wrongdoing, employed the same strategy that Simpson had used in 2013 when Frank VanderSloot, the Mitt Romney donor, came after him.

Steele was outraged by his depiction in the Horowitz report. Orbis Business Intelligence put out a statement saying the report contained “numerous inaccuracies and misleading statements” and minor changes were made to it. But those fixes were a small victory because the Horowitz report also suggested that Kremlin agents might have locked on to Steele’s sources when they started inquiring about Donald Trump—the scenario Scott Shane, the Times reporter, had painted at the Spy Museum. Russian intelligence operatives were likely monitoring the activities of Orbis Business Intelligence and Steele’s allegations about Michael Cohen’s trip to Prague could have been a plant, the report said.

In the fall of 2019, Simpson and his partner at Fusion GPS, Peter Fritsch, who was also a former Wall Street Journal reporter, published a book titled Crime in Progress, which they described as telling the “inside story” of the dossier. Christopher Steele, who co-owned an investigative firm in London called Orbis Business Intelligence, was enjoying the limelight, too. A Hollywood production company owned by the actor George Clooney had bought the rights to his story, and in 2019, he attended a celebrity-filled event at a trendy London restaurant. The party was held to honor a new editor of Vanity Fair magazine, and guests included Colin Firth, the well-known actor, and Monica Lewinsky, the former White House intern with whom Bill Clinton had an affair and who now wrote for Vanity Fair.


Big Data at Work: Dispelling the Myths, Uncovering the Opportunities by Thomas H. Davenport

Automated Insights, autonomous vehicles, bioinformatics, business intelligence, business process, call centre, chief data officer, cloud computing, commoditize, data acquisition, data science, disruptive innovation, Edward Snowden, Erik Brynjolfsson, intermodal, Internet of things, Jeff Bezos, knowledge worker, lifelogging, Mark Zuckerberg, move fast and break things, Narrative Science, natural language processing, Netflix Prize, New Journalism, recommendation engine, RFID, self-driving car, sentiment analysis, Silicon Valley, smart grid, smart meter, social graph, sorting algorithm, statistical model, Tesla Model S, text mining, Thomas Davenport, three-martini lunch

If there are intermediaries between data scientists and decision makers, the decision makers may not understand all the important data and analytics issues involved in a key decision. There is some evidence that these skills are important. A Gartner study found that “between 70 percent and 80 percent of c­ orporate business intelligence projects fail” owing to “a ­c ombination of poor communications between IT and the business, the failure to ask the right questions or to think about the real needs of the ­business.”3 And granted, business intelligence projects usually involve small rather than big data. However, while the specific percentage of projects that fail is questionable, there is no doubt that lack of communication in small and big data projects causes big problems.

What Most Large Companies Do Today The classic analytics environment at most big companies includes the operational systems that serve as the sources for data; a data warehouse or collection of federated data marts that house and—­ideally— integrate the data for a range of analysis functions; and a set of business intelligence and analytics tools that enable decisions from the use of ad hoc queries, dashboards, and data mining. Figure 5-4 illustrates the typical big company data warehouse ecosystem. Indeed, big companies have invested tens of millions of dollars in ­hardware platforms, databases, ETL (extract, transform, and load) ­software, BI (business intelligence) dashboards, advanced analytics tools, maintenance contracts, upgrades, middleware, and storage systems that comprise robust, enterprise-class data warehouse environments.

Jake Porway, “You Can’t Just Hack Your Way to Social Change,” Harvard Business Review blog post, March 7, 2013, http://blogs.hbr.org/cs/2013/03/you_cant_ just_hack_your_way_to.html. 3. Bill Goodwin, “Poor Communication to Blame for Business Intelligence Failure, Says Gartner,” ComputerWeekly.com, January 10, 2011, http://www .computerweekly.com/news/1280094776/Poor-communication-toblame-for-­ business-intelligence-failure-says-Gartner. 4. See Thomas H. Davenport and Jinho Kim, Keeping Up with the Quants (Boston: Harvard Business Review Press, 2013). 5. Sinan Aral with visualization by Nikolaos Hanselmann, “To Go from Big Data to Big Insight, Start with a Visual,” August 27, 2013, Harvard Business Review blog post, http://blogs.hbr.org/2013/08/visualizing-how-online-word-of/. 6.


Digital Accounting: The Effects of the Internet and Erp on Accounting by Ashutosh Deshmukh

accounting loophole / creative accounting, AltaVista, book value, business continuity plan, business intelligence, business logic, business process, call centre, computer age, conceptual framework, corporate governance, currency risk, data acquisition, disinformation, dumpster diving, fixed income, hypertext link, information security, interest rate swap, inventory management, iterative process, late fees, machine readable, money market fund, new economy, New Journalism, optical character recognition, packet switching, performance metric, profit maximization, semantic web, shareholder value, six sigma, statistical model, supply chain finance, supply-chain management, supply-chain management software, telemarketer, transaction costs, value at risk, vertical integration, warehouse automation, web application, Y2K

Data mining software and embedded tools are quite user friendly, and if you need to get a deeper understanding of, say, customer behavior and profitability, then you should be capable of specifying the required models. The basic idea is to understand what is possible using these tools. Accountants need to understand the possibilities, or they may fail to exploit the tremendous power of these tools. Exhibit 11. SAP business intelligence Exchange infrastructure SAP R/3 ERP Knowledge warehouse Business information warehouse MySAP business intelligenceBusiness intelligence platform •Business intelligence tools •Measurement and management Packaged BI solutions •Financial insight •Sales insight •Procurement insight Enterprise portal Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 278 Deshmukh Planning and Budgeting Budgeting has been used as a control tool for many decades.

These databases and SQL are used by many accounting departments in small- and mid-sized organizations. Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 270 Deshmukh Exhibit 6. Business intelligence tools •Data extraction •Data transformation •Data load Business information warehouse ERP system Reports •Key performance measures •Ad-hoc queries •Business intelligence metadata •OLAP metadata Business intelligence tools OLAP Analysis •Business logic •Mathematical/statistical models •Data mining Executive dashboards Management dashboards Executive information systems Pre-packaged solutions •Planning and budgeting •Consolidations •Financial analytics •Abc/abm •Balanced scorecard •Corporate performance management These tools were soon superceded by specialized report writing tools and analytical tools, which now have evolved to a new category of Business Intelligence (BI) tools; Crystal Reports/Business Objects and Cognos are examples of leading software vendors in this area.

The effects of the Internet and e-commerce on business Meta Issues •Organizational Models •Business Strategies •Hardware and Software Infrastructure •Integration with ERP Systems Customers Demand Chain Management • Customer Relationship Management • Demand Forecasting • Order Management • Product and Brand Information Management • Channel Management • Customer Services • Business Intelligence Business Finance and Accounting •Financial Reporting •Internal Controls and Audit •Cost Accounting •Treasury Functions Human Resources •Payroll Accounting •Benefits Management •Personnel Management Production •Product Design •Product Development Other Business Processes •Document Storage and Retrieval •Workflows Suppliers Supply Chain Management • Supplier Relationship Management • Production Planning • Materials Management • Transportation and Distribution • Business Intelligence The effects of e-commerce, as can be seen, cut across various industries; industry intermediaries; and, the ultimate, consumers; and also within the industry itself.


The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling by Ralph Kimball, Margy Ross

active measures, Albert Einstein, book value, business intelligence, business process, call centre, cloud computing, data acquisition, data science, discrete time, false flag, inventory management, iterative process, job automation, knowledge worker, performance metric, platform as a service, side project, zero-sum game

Table of Contents Introduction Intended Audience Chapter Preview Website Resources Summary Chapter 1: Data Warehousing, Business Intelligence, and Dimensional Modeling Primer Different Worlds of Data Capture and Data Analysis Goals of Data Warehousing and Business Intelligence Dimensional Modeling Introduction Kimball's DW/BI Architecture Alternative DW/BI Architectures Dimensional Modeling Myths More Reasons to Think Dimensionally Agile Considerations Summary Chapter 2: Kimball Dimensional Modeling Techniques Overview Fundamental Concepts Basic Fact Table Techniques Basic Dimension Table Techniques Integration via Conformed Dimensions Dealing with Slowly Changing Dimension Attributes Dealing with Dimension Hierarchies Advanced Fact Table Techniques Advanced Dimension Techniques Special Purpose Schemas Chapter 3: Retail Sales Four-Step Dimensional Design Process Retail Case Study Dimension Table Details Retail Schema in Action Retail Schema Extensibility Factless Fact Tables Dimension and Fact Table Keys Resisting Normalization Urges Summary Chapter 4: Inventory Value Chain Introduction Inventory Models Fact Table Types Value Chain Integration Enterprise Data Warehouse Bus Architecture Conformed Dimensions Conformed Facts Summary Chapter 5: Procurement Procurement Case Study Procurement Transactions and Bus Matrix Slowly Changing Dimension Basics Hybrid Slowly Changing Dimension Techniques Slowly Changing Dimension Recap Summary Chapter 6: Order Management Order Management Bus Matrix Order Transactions Invoice Transactions Accumulating Snapshot for Order Fulfillment Pipeline Summary Chapter 7: Accounting Accounting Case Study and Bus Matrix General Ledger Data Budgeting Process Dimension Attribute Hierarchies Consolidated Fact Tables Role of OLAP and Packaged Analytic Solutions Summary Chapter 8: Customer Relationship Management CRM Overview Customer Dimension Attributes Bridge Tables for Multivalued Dimensions Complex Customer Behavior Customer Data Integration Approaches Low Latency Reality Check Summary Chapter 9: Human Resources Management Employee Profile Tracking Headcount Periodic Snapshot Bus Matrix for HR Processes Packaged Analytic Solutions and Data Models Recursive Employee Hierarchies Multivalued Skill Keyword Attributes Survey Questionnaire Data Summary Chapter 10: Financial Services Banking Case Study and Bus Matrix Dimension Triage to Avoid Too Few Dimensions Supertype and Subtype Schemas for Heterogeneous Products Hot Swappable Dimensions Summary Chapter 11: Telecommunications Telecommunications Case Study and Bus Matrix General Design Review Considerations Design Review Guidelines Draft Design Exercise Discussion Remodeling Existing Data Structures Geographic Location Dimension Summary Chapter 12: Transportation Airline Case Study and Bus Matrix Extensions to Other Industries Combining Correlated Dimensions More Date and Time Considerations Localization Recap Summary Chapter 13: Education University Case Study and Bus Matrix Accumulating Snapshot Fact Tables Factless Fact Tables More Educational Analytic Opportunities Summary Chapter 14: Healthcare Healthcare Case Study and Bus Matrix Claims Billing and Payments Electronic Medical Records Facility/Equipment Inventory Utilization Dealing with Retroactive Changes Summary Chapter 15: Electronic Commerce Clickstream Source Data Clickstream Dimensional Models Integrating Clickstream into Web Retailer's Bus Matrix Profitability Across Channels Including Web Summary Chapter 16: Insurance Insurance Case Study Policy Transactions Premium Periodic Snapshot More Insurance Case Study Background Claim Transactions Claim Accumulating Snapshot Policy/Claim Consolidated Periodic Snapshot Factless Accident Events Common Dimensional Modeling Mistakes to Avoid Summary Chapter 17: Kimball DW/BI Lifecycle Overview Lifecycle Roadmap Lifecycle Launch Activities Lifecycle Technology Track Lifecycle Data Track Lifecycle BI Applications Track Lifecycle Wrap-up Activities Common Pitfalls to Avoid Summary Chapter 18: Dimensional Modeling Process and Tasks Modeling Process Overview Get Organized Design the Dimensional Model Summary Chapter 19: ETL Subsystems and Techniques Round Up the Requirements The 34 Subsystems of ETL Extracting: Getting Data into the Data Warehouse Cleaning and Conforming Data Delivering: Prepare for Presentation Managing the ETL Environment Summary Chapter 20: ETL System Design and Development Process and Tasks ETL Process Overview Develop the ETL Plan Develop One-Time Historic Load Processing Develop Incremental ETL Processing Real-Time Implications Summary Chapter 21: Big Data Analytics Big Data Overview Recommended Best Practices for Big Data Summary Index Advertisement Introduction The data warehousing and business intelligence (DW/BI) industry certainly has matured since Ralph Kimball published the first edition of The Data Warehouse Toolkit (Wiley) in 1996.

You'll miss out on updates to fundamental concepts if you skip ahead too quickly. Note This book is laced with tips (like this note), key concept listings, and chapter pointers to make it more useful and easily referenced in the future. Chapter 1: Data Warehousing, Business Intelligence, and Dimensional Modeling Primer The book begins with a primer on data warehousing, business intelligence, and dimensional modeling. We explore the components of the overall DW/BI architecture and establish the core vocabulary used during the remainder of the book. Some of the myths and misconceptions about dimensional modeling are dispelled.

We'll begin with a primer on DW/BI and dimensional modeling in Chapter 1 to ensure that everyone is on the same page regarding key terminology and architectural concepts. Chapter 1 Data Warehousing, Business Intelligence, and Dimensional Modeling Primer This first chapter lays the groundwork for the following chapters. We begin by considering data warehousing and business intelligence (DW/BI) systems from a high-level perspective. You may be disappointed to learn that we don't start with technology and tools—first and foremost, the DW/BI system must consider the needs of the business. With the business needs firmly in hand, we work backwards through the logical and then physical designs, along with decisions about technology and tools.


pages: 314 words: 94,600

Business Metadata: Capturing Enterprise Knowledge by William H. Inmon, Bonnie K. O'Neil, Lowell Fryman

affirmative action, bioinformatics, business cycle, business intelligence, business process, call centre, carbon-based life, continuous integration, corporate governance, create, read, update, delete, database schema, en.wikipedia.org, folksonomy, informal economy, knowledge economy, knowledge worker, semantic web, tacit knowledge, The Wisdom of Crowds, web application

Some DBMS vendors don’t supply an interface but instead supply utilities and application programming interfaces (APIs) so that you can write your own tools to extract metadata. Cautions that were expressed earlier in the section concerning ERP vendors also apply here: notably, when the vendor changes the system catalog structure, you must modify your tools accordingly. 7.2.6 Business Intelligence Tools Another good source for metadata is the Business Intelligence Tools (BI) environment—the user view of data served up by the data warehouse and data marts. The BI environment includes On-Line Analytical Processing (OLAP) tools. Such products produce cubes or pivot tables that enable drill-down and rollup analysis. Usually, OLAP multidimensional technology is found in the data mart environment.

Appendix Metadata System of Record Example Table 1 Metadata object system of record Meta Object Entity Name Entity Type Entity Definition Entity Scope Entity Active Ind Entity Logical Business Rule Logical Application Name Entity Synonym Name Logical Attribute Logical Attribute Definition Attribute Logical Business Rule Attribute Logical FK Ind Attribute Business Area Logical Business Function Data Subject Area Physical Column Name Physical Column Data Type Physical Column Length Physical Column Precision Strategic Modeling Tool Tactical Modeling Tool C C C C C U U C U DBMS Tool Data Integration Tool Reporting Tool U C C C C U C U C C U U C C U U C U C U C U C U (Cont.) 283 284 Appendix Table 1 Metadata object system of record (continued) Meta Object Strategic Modeling Tool Physical Column Decimal places Physical Column Default Value Physical Column Nullable Ind Physical Column Comment Physical Column Primary Key Ind Physical Column Foreign Key Ind Table Name Table Owner Table Type Table Comments Physical Table Name Physical Column Name Physical View Name Physical Database Name Physical Schema Name ETL Object Name Source Table Source Column Target Table Target Column ETL Job Name ETL Transformation Rule ETL Job Run Date ETL Job Execution Time ETL Job Row Count ETL Job Status Report Name Report Element Name Report Table Name Report Database Name Report DB Sequence Report Element Business Rule Tactical Modeling Tool DBMS Tool C U C U C U C U C C C C C U U U U U C C C C C Data Integration Tool Reporting Tool C R R R R C C C C C C C R R R R C Appendix 285 Metadata Usage Matrix Example The following table summarizes the metadata objects and the anticipated usage of each object in the following functions: Table 1 Summary of metadata objects usage Source – Metadata Object Entity Name Entity Type Entity Definition Entity Scope Entity Container Entity Active Ind Entity Logical Business Rule Logical Application Name Entity Synonym Name Logical Attribute Logical Attribute Definition Attribute Logical Business Rule Attribute Logical FK Ind Attribute Business Area Logical Business Function Data Subject Area Physical Column Name Physical Column Data Type Physical Column Length Physical Column Precision Physical Column Decimal Places Physical Column Default Value Physical Column Nullable Ind Data Lineage Impact Analysis Definition and or Glossary Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y (Cont.) 286 Appendix Table 1 Summary of metadata objects usage (continued) Source – Metadata Object Physical Column Comment Physical Column Primary Key Ind Physical Column Foreign Key Ind Table Name Table Owner Table Type Table Comments Physical Table Name Physical Column Name Physical View Name Physical Database Name Physical Schema Name ETL Object Name Source Table Source Column Target Table Target Column ETL Job Name ETL Transformation Rule ETL Job Run Date ETL Job Execution Time ETL Job Row Count ETL Job Status Report Name Report Element Name Report Table Name Report Database Name Report DB Sequence Report Element Business Rule Data Lineage Impact Analysis Definition and or Glossary Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Index Abstraction, linking structured and unstructured data, 230–231 Accuracy, metadata information, 33–34 Administration, infrastructure issues functionality requirements, 169 history keeping, 169–170 BI, see Business intelligence Broader term, definition component, 61, 63–64 Business Glossary features, 111–112 integrated technical and business metadata delivery, 152–153 Business intelligence (BI), business metadata delivery infrastructure, 163 Business metadata capture, see Capture, business metadata components, 13 definition, 38 delivery, see Delivery, business metadata funding, see Funding, business metadata historical perspective, 3–11 importance, 274–275 locations corporate forms, 15–16 reports, 14, 29–31 screens, 13–14 origins, 19–20 repository construction, see Metadata project resources, 281–282 technical metadata comparison, 12–13 conversion, 135–136, 182–186 infrastructure for integration, 165–166 separation, 140 tracking over time, 20–21 types, 158–160 Business rules business management, 238–239 business metadata, 237, 245 capturing rationale, 238–239 definition, 235–236 maintenance, 242 management, 243 metadata repository, 244–245 ruleflow, 242–243 sources, 237–238 systems, 239, 242–243 Call center volume, search problem quantification, 70 Capability Maturity Model Integration (CMMI), note-taking as asset producing, 265 Capture, business metadata barriers, 275 corporate knowledge base, 93–94 culture, 95–96 editing automation, 128–129 expansion of definition and descriptions, 129–131 granularization, 129 homonym resolution, 132–134 manual editing, 134–135 staging area, 134 synonym resolution, 131–132 Governance Lite™, 107–109, 111 individual documentation problem, 114–115 knowledge socialization, see Knowledge socialization metadata sources comparison of sources, 127 data warehouse, 126 database management system system catalogs, 124 documents, 123 enterprise resource planning applications, 122 extract-transform-load, 124–125 legacy systems, 125–126 on-line analytical processing tools, 124 on-line transaction processing, 125–126 reports, 122–123 spreadsheets, 123 principles, 95–96 publicity, 112–113 rationale, 90–93 technical metadata conversion to business metadata, 135–136 287 288 Index Capture, business metadata (Continued) technology search, 109–111 Web 2.0 folksonomy, 118–119 mashups, 115–116 overview, 115 Card catalog, see Library card catalog CDC, see Centers for Disease Control Centers for Disease Control (CDC), linking structured and unstructured data, 231 CIF, see Corporate Information Factory C-map, see Concept Map CMMI, see Capability Maturity Model Integration Collective intelligence, knowledge socialization, 97 Communications audits, 251 clarity problems bad business decisions, 57 English language limitations, 59 everyday communications, 56–57 faulty rollups, 57–58 units of measure differences, 59 classification, see Taxonomy definitions components, 61–62 guidelines, 60–61 importance, 59–60 miscellaneous guidelines, 64 usage notes, 62–64 historic library creation, 253 human/computer communication problem, 276–278 screening, 251–253 search problem information and knowledge workers, 65–66 information provider guidelines, 71–72 quantification, 67–70 search techniques, 71 tracking down information, 66–67 Compliance communications audits, 251 historic library creation, 253 screening, 251–253 data profiling, 254–256 financial audit metadata utility, 250 transaction background activities, 250–251 prospects, 280 Sarbanes-Oxley Act provisions, 248–240 types, 249–251 Concept Map (C-map), semantic framework, 205, 209 Conceptual model, semantic framework, 204 Controlled vocabulary (CV), semantic framework, 200–201 Corporate forms, business metadata content, 15–16 Corporate Information Factory (CIF), implementation, 81–82 Corporate knowledge base, components, 93–94 Create Read Update Delete (CRUD), conflict resolution, 167 CRM, see Customer relationship management Cross selling, business metadata capture rationale, 92 CRUD, see Create Read Update Delete Customer relationship management (CRM), business metadata capture rationale, 92 Customer, definition, 56 CV, see Controlled vocabulary DASD, see Direct access storage device Data, definition, 176 Database management system (DBMS) historical perspective, 5 metadata storage, 19 system catalog as metadata resource, 124 Data Flux, data quality presentation, 190–191 Data Governance Council, metadata stewardship, 42–43 Data quality continuum, 190 Data Warehousing report, 49 definition, 177 presentation, 189–190 Data Stewardship Council, metadata stewardship, 43–44 Data warehouse historical perspective, 7–9 infrastructure, 160–161 metadata resource, 126 metadata warehouse features, 161–162 DBMS, see Database management system Decision table, business rule representation, 239–242 Decision tree, business rule representation, 239, 241 Index Definition components, 61–62 dictionary role in information quality, 186 guidelines, 60–61 importance, 59–60 miscellaneous guidelines, 64 usage notes, 62–64 Delivery, business metadata examples corporate dictionary, 147–148 integrated technical and business metadata delivery, 152–153 mashups, 149–150 technical use, 151–152 training, 148–149 visual analytic techniques, 150–151 indirect usage accessibility from multiple places, 142–143 application access, 147 interactive reports, 145–146 overview, 141–142 Web delivery, 143–144 information quality business metadata, 188–190 infrastructure considerations business intelligence environments, 163 graphical affinity, 163–164 legacy environment, 162–163 mashups, 164 principles, 140–141 prospects, 280 Description Logics (DL), semantic framework, 206 Dictionary, see Glossary Direct access storage device (DASD), historical perspective, 4–6 Disk storage, historical perspective, 4–6 DL, see Description Logics Documents, metadata resource, 123 Editing, metadata automation, 128–129 expansion of definition and descriptions, 129–131 granularization, 129 homonym resolution, 132–134 manual editing, 134–135 staging area, 134 synonym resolution, 131–132 Employee turnover, business metadata capture rationale, 91–92 289 Enterprise resource planning (ERP), metadata resource, 85, 122 Entity/relationship (ER) model, semantic framework, 203–204, 208, 210–211 ER model, see Entity/relationship model ERP, see Enterprise resource planning ETL, see Extract-transform-load Extract-transform-load (ETL), metadata resource, 85, 124–125 Federated metadata, integrated metadata management, 168 Financial audit metadata utility, 250 transaction background activities, 250–251 First-order logic (FOL), semantic framework, 206 FOL, see First-order logic Folksonomy knowledge capture, 118–119 self-organizing tags, 77 Forms, see Corporate forms Fourth generation language historical perspective, 6–7 metadata handling, 11 Funding, business metadata advantages and disadvantages of approaches, 52–53 centralized implementation, 51 localized implementation, 51–52 overview, 50–51 Glossary business functions, 60 information quality role, 186 semantic framework, 201 Governance Lite™, knowledge capture, 107–109, 111 Granularization, metadata, 129 Graphical affinity, business metadata delivery infrastructure, 163–164 Grid, metadata representation, 18 Groupware, knowledge socialization, 100–103, 279 Homonyms, resolution, 132–134 Industrial recognition, text, 227 Information quality business and technical metadata interaction, 177–186 business metadata delivery, 188–190 290 Index Information quality (Continued) definition, 177 dictionary role, 186 methodology, 187–188 Information Technology (IT) department challenges, 278–279 metadata responsibility, 38–39 Information, definition, 177 IT department, see Information Technology department KB, see Knowledge base KM, see Knowledge management Knowledge base (KB) definition, 267 building, 267 Knowledge management (KM) business metadata intersection artifact generation, 262–263 corporate dictionary example, 263 definition, 260 goals, 260–261 importance, 261 social issues graying work force, 269–270 socialization effect on knowledge, 270 tacit knowledge, see Tacit knowledge techniques, 267–268 Knowledge socialization collective intelligence, 97 experts, 97–98 groupware, 100–103, 279 knowledge management, 268, 270 technology fostering portal and collaboration servers, 100–103 social networking, 99 wikis, 103–106 Knowledge worker metadata capture, 94 search problem, 65–66 Legacy systems business metadata delivery infrastructure, 162–163 metadata resource, 125–126 Library card catalog, metadata analogy, 27–29 Life cycle, metadata, 45–48 Magnetic tape data storage, 4 languages for data reading, 10 Mashup business metadata delivery, 149–150, 164 knowledge capture, 115–116 Master data management (MDM) conflict resolution, 167 overview, 22 MDM, see Master data management Metadata definition, 9, 26–27 examples, 9 grid representation, 18 management importance, 32 metamodel, 158–160 system of record example, 283–284 usage matrix example, 285–286 Metadata project business metadata versus technical metadata, 83–84 buying versus building, 170–172 classification, 82–83 funding, see Funding, business metadata iterations of development, 84 local metadata tools, 85 metadata sources, 86–87 preexisting repositories, 172–173 rationale, 80–82 scope defining, 85–87 Metadata Stewardship Council, responsibilities, 44–45 Narrower term, definition component, 61, 63–64 National Cancer Institute (NCI), semantic vocabulary implementation, 214–216 NCI, see National Cancer Institute Null, data profiling, 183, 185–186 ODS, see Operational data store OLAP, see Online analytical processing On-line analytical processing, metadata resource, 85124 On-line transaction processing, metadata resource, 125–126 Ontology, semantic framework, 207 Operational data store (ODS), data warehousing, 8 Opportunity cost, search problem quantification, 69 OWL, see Web Ontology Language Ownership, definition, 40 Patterns, identification, 183–184, 186 PC, see Personal computer Index Personal computer (PC) historical perspective, 7 metadata handling, 11 Preferred term, definition component, 62 Punch cards historical perspective, 4 metadata, 9–10 Quality, see Data quality; Information quality Range of values, data profiling, 183, 186 RDF, see Resource Definition Framework Reference file overview, 21–22 updating, 22 Regulations, see Compliance Related term, definition component, 61 Reports interactive reports, 145–146 metadata resource, 14, 29–31, 122–123 Repository, see Metadata project Resource Definition Framework (RDF), semantic framework, 204–205 Reuse, metadata, 32–33 Sales, search problem quantification, 70 Sarbanes-Oxley Act, provisions, 248–240 Screen, business metadata content, 13–14 Search problem enterprise search, 279 information and knowledge workers, 65–66 information provider guidelines, 71–72 quantification, 67–70 search techniques, 71 tracking down information, 66–67 Self-organizing map, linking structured and unstructured data, 233 Self-organizing tags, taxonomy, 77 Semantics business metadata delivering definitions and relationships, 208–209 exposing semantics to business, 210–211 expression, 209–210 overview, 207–208 context sensitivity, 197–199 framework Concept Map, 205, 209 conceptual model, 204 controlled vocabulary, 200–201 Description Logics, 206 291 entity/relationship model, 203–204, 208, 210–211 first-order logic, 206 glossary, 201 ontology, 207 Resource Definition Framework, 204–205 taxonomy, 202 thesauri, 203 topic map, 205 UML, 205–206 Web Ontology Language, 204–205, 210 human/computer concept, 199–200, 278 importance, 196–197 practical issues integration, 211–212 National Cancer Institute semantic vocabulary implementation, 214–216 service-oriented architecture, 214 Web Services, 212–213 prospects for integration and discovery, 280 semantic Web, 195–196 spectrum, 200–201, 208 Semistructured data, examples and technologies, 222–223 Serial transfer, knowledge management, 267–268 Service-oriented architecture (SOA) integrated metadata management, 168 semantics integration, 214 SOA, see Service-oriented architecture SOAP, semantic interface, 212 Socialization, see Knowledge socialization Social networking, knowledge socialization, 99 Spreadsheets, metadata resource, 123 Stemmed words, text distillation, 225 Stewardship business metadata approaches, 44–45 artifacts, 44 Data Governance Council, 42–43 Data Stewardship Council, 43–44 historical perspective, 41–42 Metadata Stewardship Council, 44–45 definition, 40–41 Structured metadata characteristics, 16–18 examples and technologies, 219–220 linking structured and unstructured data abstraction, 230–231 examples, 231, 233 292 Index Structured metadata (Continued) integration, 230 unstructured data comparison, 221 Synonyms, resolution, 131–132 Tacit knowledge definition, 94, 264 note-taking as asset producing, 265–266 transfer nurturing, 266 Taxonomy basic rules, 73, 75 document categorization, 76 governance and taxonomy, 77 language and vocabulary, 75–76 lowest common denominator, 75 overview, 72 self-organizing tags, 77 semantic framework, 202 simplicity, 76 Team Room, knowledge socialization, 100–103 Technical metadata business metadata comparison, 12–13 conversion, 135–136 infrastructure for integration, 165–166 separation, 140 categories, 2 Technical metadata conversion to business metadata, 135–136, 182–186 profiling, 179–182 Text business metadata terms, 227–228 communications audits, 252–253 distillation, 224–229 extraneous words, 225 industrial recognition, 227 pulling, 223 relationship recognition, 228–229 stemmed words, 225 word counting, 226 Thesauri, semantic framework, 203 Topic map, semantic framework, 205 UML, semantic framework, 205–206 Unstructured metadata characteristics, 16–18 examples and technologies, 220–221 mining prospects, 281 structured data comparison, 221 text business metadata terms, 227–228 distillation, 224–229 extraneous words, 225 industrial recognition, 227 pulling, 223 relationship recognition, 228–229 stemmed words, 225 word counting, 226 Value/frequency report, data profiling, 181, 184–186 Web 2.0 knowledge capture folksonomy, 118–119 mashups, 115–116 overview, 115 semantic Web, 195–196 Web Ontology Language (OWL), semantic framework, 204–205, 210 Web Services, semantics interface, 212–213 Wiki governance, 106 knowledge capture, 104 limitations, 105–106 portal collaboration comparison, 105 wikinomics, 104 Wikipedia, 103–104, 212 Words, see Text

Business Metadata Praise for Business Metadata “Despite the presence of some excellent books on what is essentially “technical” metadata, up until now there has been a dearth of wellpresented material to help address the growing need for interaction at the conceptual and semantic levels between data professionals and the business clients they support. In Business Metadata, Bill, Bonnie, and Lowell provide the means for bridging the gap between the sometimes “fuzzy” human perception of data that fuels business processes and the rigid information management models used by business applications. Look to the future: next generation business intelligence, enterprise content management and search, the semantic web all will depend on business metadata. Read this book!” —David Loshin, President, Knowledge Integrity Incorporated These authors have written a book that ventures into new territory for data and information management. There are several books about metadata, but this is the first to offer in-depth discussion of the important topic of business metadata.


pages: 239 words: 70,206

Data-Ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else by Steve Lohr

"World Economic Forum" Davos, 23andMe, Abraham Maslow, Affordable Care Act / Obamacare, Albert Einstein, Alvin Toffler, Bear Stearns, behavioural economics, big data - Walmart - Pop Tarts, bioinformatics, business cycle, business intelligence, call centre, Carl Icahn, classic study, cloud computing, computer age, conceptual framework, Credit Default Swap, crowdsourcing, Daniel Kahneman / Amos Tversky, Danny Hillis, data is the new oil, data science, David Brooks, driverless car, East Village, Edward Snowden, Emanuel Derman, Erik Brynjolfsson, everywhere but in the productivity statistics, financial engineering, Frederick Winslow Taylor, Future Shock, Google Glasses, Ida Tarbell, impulse control, income inequality, indoor plumbing, industrial robot, informal economy, Internet of things, invention of writing, Johannes Kepler, John Markoff, John von Neumann, lifelogging, machine translation, Mark Zuckerberg, market bubble, meta-analysis, money market fund, natural language processing, obamacare, pattern recognition, payday loans, personalized medicine, planned obsolescence, precision agriculture, pre–internet, Productivity paradox, RAND corporation, rising living standards, Robert Gordon, Robert Solow, Salesforce, scientific management, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, SimCity, six sigma, skunkworks, speech recognition, statistical model, Steve Jobs, Steven Levy, The Design of Experiments, the scientific method, Thomas Kuhn: the structure of scientific revolutions, Tony Fadell, unbanked and underbanked, underbanked, Von Neumann architecture, Watson beat the top human players on Jeopardy!, yottabyte

The notion that computers should let you do something smart with data has been around since the dawn of computing. More recently, software programs for using data to make better-informed decisions were given the label “business intelligence.” It is a predecessor term to big data, and one still in use. Business intelligence tends to focus on collection, reporting, and basic analysis but not on the predictive or experimental features of data science. The concept dates back to 1958, presented in a paper titled, “A Business Intelligence System,” by Hans Peter Luhn, a computer scientist at IBM. Such a system, according to Luhn, was needed to cope with the postwar data boom in business, government, and the sciences.

“Special equipment,” he conceded, would be required. Luhn closed with a prescient bit of qualified optimism. “Perhaps the techniques which ultimately find greatest use will bear little resemblance to those now envisioned,” he wrote, “but some form of automation will ultimately provide an effective answer to business intelligence problems.” IBM’s headquarters is so nestled in woods and a rocky ravine that you don’t see it until well after you have entered the corporate grounds. The lazy Z–shaped building looks deceptively small from the outside. It is a striking contrast to the headquarters it replaced, a suburban corporate big house on a hill, opened in 1963.

But the family-owned Denihan is an example of a conventional company that has made real progress toward using technology to make more of its decisions aided by data. And the hotel company has been at it seriously for more than a decade. Indeed, Denihan was cited as one of a few examples in a 2009 book, Profiles in Performance: Business Intelligence Journeys and the Roadmap for Change, by Howard Dresner, a respected business and technology consultant. The Denihan experience combines leadership at the top, focused goals, and close cooperation between a small data team and the people managing hotels day in and day out. The investment wasn’t huge, and the progress has come in measured steps.


pages: 374 words: 94,508

Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage by Douglas B. Laney

3D printing, Affordable Care Act / Obamacare, banking crisis, behavioural economics, blockchain, book value, business climate, business intelligence, business logic, business process, call centre, carbon credits, chief data officer, Claude Shannon: information theory, commoditize, conceptual framework, crowdsourcing, dark matter, data acquisition, data science, deep learning, digital rights, digital twin, discounted cash flows, disintermediation, diversification, en.wikipedia.org, endowment effect, Erik Brynjolfsson, full employment, hype cycle, informal economy, information security, intangible asset, Internet of things, it's over 9,000, linked data, Lyft, Nash equilibrium, Neil Armstrong, Network effects, new economy, obamacare, performance metric, profit motive, recommendation engine, RFID, Salesforce, semantic web, single source of truth, smart meter, Snapchat, software as a service, source of truth, supply-chain management, tacit knowledge, technological determinism, text mining, uber lyft, Y2K, yield curve

And even when licensing information, most organizations will add value to it by generating and selling the insights or analysis instead of or alongside the raw information itself. However, evolving from traditional business intelligence (BI), represented by enterprise reporting or end user query tools, has been slow to materialize in many organizations. Not only have lagging organizations lost out on the opportunity to understand their businesses and markets better, but they have squandered opportunities to generate measurable economic benefits from (i.e., monetize) their information assets. In this chapter, we will explore the case for reaching beyond business intelligence, and how embracing these ideas can lead to improved economic benefits for your organization.

Title: Infonomics : how to monetize, manage, and measure information as an asset for competitive advantage / Douglas B. Laney. Description: New York, NY : Routledge, 2018. | Includes bibliographical references and index. Identifiers: LCCN 2017011754 (print) | LCCN 2017032587 (ebook) | ISBN 9781315108650 (ebook) | ISBN 9781138090385 (hardback : alk. paper) Subjects: LCSH: Business intelligence. | Commercial statistics. | Information technology. Classification: LCC HD38.7 (ebook) | LCC HD38.7 .L347 2018 (print) | DDC 658.4/038—dc23 LC record available at https://lccn.loc.gov/2017011754 Visit the Taylor & Francis Web site at www.taylorandfrancis.com To Susan and Ethan Contents Acknowledgments Foreword Introduction Part I Monetizing Information as an Asset Chapter 1 Why Monetize Information Chapter 2 Prime Ways to Monetize Information Chapter 3 Methods for Monetizing Information Chapter 4 Analytics: The Engine of Information Monetization Part II Managing Information as an Asset Chapter 5 Information Management Maturity and Principles Chapter 6 Information Supply Chains and Ecosystems Chapter 7 Leveraging Information Asset Management Standards and Approaches Chapter 8 Applied Asset Management for Improved Information Maturity Part III Measuring Information as an Asset Chapter 9 Is Information an Asset?

Federal Reserve had released the results of the second phase of its annual Comprehensive Capital Analysis and Review (CCAR) stress tests on major banks.5 Citigroup had passed with flying colors—the cleanest test of top U.S. banks—by correlating and analyzing 2,600 macroeconomic variables with revenue streams from dozens of business units with the help of machine intelligence technology from Ayasdi.6 They had uncovered variable permutations which were difficult to identify using basic business intelligence approaches, and reduced this process from three months to two weeks. In using information to demonstrably reduce risk and improve compliance, Citigroup had added billions in market value. Or consider how the Carolinas-centered mid-range upscale department store chain Belk is monetizing information to measurably optimize merchandising, marketing, and real estate investments.


Beautiful Data: The Stories Behind Elegant Data Solutions by Toby Segaran, Jeff Hammerbacher

23andMe, airport security, Amazon Mechanical Turk, bioinformatics, Black Swan, business intelligence, card file, cloud computing, computer vision, correlation coefficient, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, data acquisition, data science, database schema, double helix, en.wikipedia.org, epigenetics, fault tolerance, Firefox, Gregor Mendel, Hans Rosling, housing crisis, information retrieval, lake wobegon effect, Large Hadron Collider, longitudinal study, machine readable, machine translation, Mars Rover, natural language processing, openstreetmap, Paradox of Choice, power law, prediction markets, profit motive, semantic web, sentiment analysis, Simon Singh, social bookmarking, social graph, SPARQL, sparse data, speech recognition, statistical model, supply-chain management, systematic bias, TED Talk, text mining, the long tail, Vernor Vinge, web application

I didn’t realize it at the time, but with our ETL framework, Data Warehouse, and internal dashboard, we had built a simple “Business Intelligence” system. A Business Intelligence System In a 1958 paper in the IBM Systems Journal, Hans Peter Luhn describes a system for “selective dissemination” of documents to “action points” based on the “interest profiles” of the individual action points. The author demonstrates shocking prescience. The title of the paper is “A Business Intelligence System,” and it appears to be the first use of the term “Business Intelligence” in its modern context. In addition to the dissemination of information in real time, the system was to allow for “information retrieval”—search—to be conducted over the entire document collection.

He also proposes “reporters” to periodically sift the data and selectively move information to action points as needed. INFORMATION PLATFORMS AND THE RISE OF THE DATA SCIENTIST Download at Boykma.Com 75 The field of Business Intelligence has evolved over the five decades since Luhn’s paper was published, and the term has come to be more closely associated with the management of structured data. Today, a typical business intelligence system consists of an ETL framework pulling data on a regular basis from an array of data sources into a Data Warehouse, on top of which sits a Business Intelligence tool used by business analysts to generate reports for internal consumption. How did we go from Luhn’s vision to the current state of affairs?

CONTRIBUTORS Download at Boykma.Com 355 Download at Boykma.Com INDEX A accessibility, data collection considerations for, 19, 23 accuracy of data, 21, 29 ACID model of database transactions, 58 action points, 75 action research, 78 Amazon Dynamo system, 69 analysis of data (see data analysis) anchoring, fallacy involving, 217 Apache Hadoop project (see Hadoop system) Argus portal, 81 asymmetry of risk-taking, 208 asynchronous data collection, 4 author identification of corpus data, 239 Autonomy Corporation, 78 Azure SDS, 70 B base rate fallacy, 215 Bay Area housing market analysis (see housing market analysis) “best-effort” approach to database transactions, 58 biases in interpretation of data, 205, 217 Biewald, Lukas (author), 279–301 BigTable system, 68 binary data, 41 BitTorrent, 121 Blackburne, Ben (author), 243–258 blind URLs, 131 books and publications Building the Data Warehouse (Inmon), 76 “A Business Intelligence System” (Luhn), 75 The Code Book (Singh), 230 The Data Warehouse Toolkit (Kimball), 76 CHAPTER 0 The Fifth Discipline (Senge), 78 Secret Code Breaker (Raynard), 233 Bradley, Jean-Claude (author), 259–277 brains, as Information Platforms, 73 Brants, Thorsten (trillion-word data set published by), 219 browser compatibility, testing for, 24 Building the Data Warehouse (Inmon), 76 Business Intelligence system, 75 “A Business Intelligence System” (Luhn), 75 C Caesar ciphers, 228 cameras (imagers) for Phoenix Mars Lander system, 38, 53 Campaingr software, 3 cancer’s effects on DNA, 246 cartography, 86 Cassandra system, 70, 81 causality, not related to correlation, 210 CCD (charge-coupled device) imagers, 37 Census data website, 336 census data, project using (see sense.us website) Center for Embedded Networked Sensing at UCLA, 2 Center for Responsible Politics website, 336 charge-coupled device (CCD) imagers, 37 Cheetah system, 79 chemical data for research (see raw data, providing to users) ChemSpider, 266 Chicago Crime project, 168 CIELab color model, 95 cloud system, 56, 70 (see also PNUTS system) The Code Book (Singh), 230 code examples in this book, using, xiv 357 Download at Boykma.Com collecting data (see data collection) collective reconciliation, 344–348 color schemes in data visualization for customer survey project, 23 for Geograph archive, 93, 95 for PEIR system, 9, 10 for sense.us website, 184, 191 conditional probability, definition of, 220 confirmation bias, 208 consistency of data after updates, 57–64 consumer price index (CPI), 307 contact information for this book, xiv context-less directories, 113 Cooper, Brian F.


pages: 1,409 words: 205,237

Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale by Jan Kunigk, Ian Buss, Paul Wilkinson, Lars George

Amazon Web Services, barriers to entry, bitcoin, business intelligence, business logic, business process, cloud computing, commoditize, computer vision, continuous integration, create, read, update, delete, data science, database schema, Debian, deep learning, DevOps, domain-specific language, fault tolerance, Firefox, FOSDEM, functional programming, Google Chrome, Induced demand, information security, Infrastructure as a Service, Internet of things, job automation, Kickstarter, Kubernetes, level 1 cache, loose coupling, microservices, natural language processing, Network effects, platform as a service, single source of truth, source of truth, statistical model, vertical integration, web application

RAM, RAM server CPUs and RAM, Server CPUs and RAM-Threads and cores in Hadoop decoupling of, Multiple Clusters and Independent Storage and Compute erasure coding vs. replicationcomparison, summary of, Read performance deciding when to use erasure coding, Guidance locality optimization, Locality optimization network performance, Discussion read performance, Read performance GCP quotas for, Caveats and service limits Hadoop and the Linux storage stack, Hadoop and the Linux Storage Stack-Filesystemsblock-level filesystems, Filesystems erasure coding vs. replication, Erasure Coding Versus Replication-Low-Level Storage Linux page cache, The Linux Page Cache system calls, Important System Calls user space in Linux, User Space low-level storage, Low-Level Storage-Disk cachedisk layer, Disk Layer-Disk cache storage controllers, Storage Controllers-Guidelines server form factors, Server Form Factors-Guidancecomparison of, Form Factor Comparison guidelines for choosing, Guidance virtualization, The Traditional Approach workload profiles, Workload Profiles compute resources, availability in public cloud, Compute availability compute virtualization, Compute Virtualization-Anti-Affinity Groups compute-heavy instances, Instances compute-optimized VMs (Azure), Azure instance types computer room air conditioning units (CRAC units), Cooling confidentiality controls, Access Security configuration management software, Useful Tools(see also software configuration management (SCM) tools) configuration-only edge nodes, Interaction Patterns configurationslong-lived clusters in the cloud, Configuration and Templatingenvironment configuration, Environment configuration operating systems, Operating Systems operating systems for Hadoop, OS Configuration for Hadoop-OS Configuration for Hadoopautomated configuration example, Automated Configuration Example consensus, Apache ZooKeeper, Consensus, Consensusdistributed, Quorums consistencyblocked consistency operations for synchronous replication, Quorum spanning with two datacenters consistent view in EMRFS, Amazon Elastic MapReduce of data in backups, Consistency container groups (see cgroups) containerization, Basics of Virtualization for Hadoop containers, YARN, Installation Choicesblob storage in Azure, Azure storage options OpenShift container-based platform, OpenShift-Summary running in YARN, YARN running user notebook sessions in, Notebooks control plane, A Tour of the Landscape, Network Virtualization Conventional Shared Storage mode, HA configurations cooling (datacenters), Coolingfailure scenarios, Rack Awareness and Rack Failures coordinator daemons (Impala), Impala daemonslimiting number of coordinator nodes, Architecting for HA copy-on-write, Snapshots cores, Threads and cores in Hadoopcore count per CPU in Hadoop, CPU Specifications corporate KDCintegration with cluster KDC, Local cluster KDC and corporate user KDC using for service and user principals, Corporate KDC CPUscommodity servers with more than two, Commodity Servers CPU density, Server Form Factors CPU-heavy cloud instances, CPU-heavy instances server CPUs and RAM, Server CPUs and RAMcores and threads in Hadoop, Threads and cores in Hadoop role of x86 architecture, The role of the x86 architecture specifications for Hadoop, CPU Specifications validating, CPU Credentials Provider API, Temporary security credentials criminal access to cloud infrastructure, Environmental Risks cross-realm trusts, Cross-realm trusts, Option A: Cloud-Only Self-Contained ID Servicesone-way trust between local cluster KDC and corporate KDC, Local cluster KDC and corporate user KDC setting up, Setting up cross-realm trusts-One-way trusts between MIT KDCsone-way trust between MIT KDC and AD, One-way trust between MIT KDC and AD two-way trust between KDCs in different clusters, Local cluster KDC cubes, Analyst custom machine types (GCP), Instance types customer data keys, Encryption in AWS customer master keys, Encryption in AWS customer-managed keys, Encryption in GCP customer-supplied keys, Encryption in GCP D daemons (Impala), Impala daemonsclient protocols supported, Architecting for HA dataavailability in public cloud solutions, Data availability user data persisted in storage systems, Data Types data analysts (see analysts) data context, Multiple Clusters and Independent Storage and Compute Data Definition Language (DDL), Required Databasessynchronous DDL queries in Impala, Architecting for HA data encryption key (DEK), Encryption in Microsoft Azure data lakes, Commoditized Storage Meets the Enterprise, Azure storage options(see also Azure Data Lake Store) data link layer (Layer 2), Layer 2 Recommendations data plane, A Tour of the Landscape, Network Virtualization data plane authorization, Microsoft Azure data replication (see replication) data scientists, Revised Team Setup for Hadoop in the Enterprise, Data scientist, Summaryskill sets, Data scientist data sources, Suitable Data Sources data transfers, Data Transfers-Shufflesreplication, Replication shuffles, Shuffles data types needing backup, Data Types data warehouses, Case Study: A Typical Business Intelligence Projectin business intelligence solution with Hadoop, Solution Overview with Hadoop in traditional business intelligence project approach, The Traditional Approach scaling, The Traditional Approach databasesavailability in public cloud solutions, Databases backing up Hive Metastore, Hive Metastore backups, Databases benchmarks for evaluating relational database engines, Validating Other Components Cloudera Manager, supported options, Cloudera Manager database backup in Oozie backup workflow, Subflow: Database deciding which database to use, Database Considerations for Hadoop services, Service Databases-Database Integration Optionsintegration options, Database Integration Options required databases, Required Databases high availability, Database HA-Supported databasesclustering software for, Clustering replication, Replication supported databases, Supported databases managed relational databases from public cloud providers, Network Architecture Oozie storage of state in, Oozie pluggable and embedded, Many Distributed Systems datacenters, Datacenter Considerations-Summarybasic concepts, Basic Datacenter Concepts-Failure Domain Alignmentcooling, Cooling networks, Network power, Power rack awareness and rack failures, Rack Awareness and Rack Failures typical datacenter setup, Basic Datacenter Concepts cluster spanning, Cluster Spanning-Summarybandwidth impairment with, Bandwidth impairment quorum spanning with three datacenters, Quorum spanning with three datacenters quorum spanning with two datacenters, Quorum spanning with two datacenters rack awareness and, Nonstandard use of rack awareness failure of, Failure Scenarios Hadoop's difference from other workloads, Why Does It Matter ?

Hadoop services, for example, scale horizontally across many servers but vertically integrate hardware, system-level software, and middleware infrastructure across all these servers into a single system. This requires an understanding of the cluster as a whole, rather than focusing on individual components. Case Study: A Typical Business Intelligence Project The type of organizational change that Hadoop drives is best described by an example. In the following, we compare the team setup of a business intelligence (BI) application on top of state-of-the-art data warehouse infrastructure against the proposed team setup when implementing the same solution on Hadoop. The comparison and technical description will be very high level so that we can focus on the organizational factors.

skill profile, Big data engineer split responsibilities with other team roles, Split Responsibilities bigdata-interop project (Google), Hadoop integration binary releases of Hadoop, Installation Choices bind (LDAP), LDAP Authentication BitLocker, Encryption in Microsoft Azure bits per second (bps), translating to bytes per second (B/s), Measuring throughput blob storage (Azure), Azure storage options, Azure storage options, Blob storageencryption in, Encryption in Microsoft Azure in Hadoop, Azure storage options integration with Hadoop, Azure Blob storage block blobs (Azure), Azure storage options block encryption key (BEK), Encryption in Microsoft Azure block I/O prioritization, Linux kernel cgroups and, Requirements for Multitenancy block locality, Erasure Coding Versus Replicationlocality opitmization, Locality optimization block reports, HDFS blocks, HDFSblock size, The Linux Page Cache block-level filesystems, Filesystems-Filesystems different meanings of, Sequential I/O performance mlocked by the DataNode, Short-Circuit and Zero-Copy Reads placement of, using replication, Erasure Coding Versus Replication replication of, Replication bring your own key (BYOK), Options for Encryption in the Cloud, BYOK, Recommendations and Summary for Cloud Encryption brokers (Kafka), Kafka bucket policies (Amazon S3), Amazon Simple Storage Service bucketsin Amazon S3, AWS storage options, Caveats and service limits in Google Cloud Storage (GCS), Storage options business continuity team, Policies and Objectives business intelligence project case study, Case Study: A Typical Business Intelligence Project-Do I Need a Center of Excellence/Competence?center for excellence/competence in solution with Hadoop, Do I Need a Center of Excellence/Competence? DevOps and solution with Hadoop, Do I Need DevOps? new team setup for solution with Hadoop, New Team Setup solution overview with Hadoop, Solution Overview with Hadoop split responsibilities in solution with Hadoop, Split Responsibilities traditional solution approach, The Traditional Approach-The Traditional Approach typical team setup, Typical Team Setup-Systems engineer C C++, Apache Impala, Everything Is Java, Web UIsImpala and Kudu implementations, The role of the x86 architecture server certificate verification, Application Integration X.509 certificate format, Converting Certificates cablingcross-cabling racks, Network in stacked networks, Stacked network cabling considerations using high-speed cables in network Layer 1, Layer 1 Recommendations caches, Commodity Serverscache coherence, Commodity Servers disk cache, Disk cacheenabled and disabled, throughput testing, Disk cache HDFS, implementation of, Important System Calls Kerberos tickets stored in, Kerberos Clients L3 cache size and core count, CPU Specifications Linux page cache, The Linux Page Cache Linux, access for filesystem I/O, User Space simulating a cache miss, Sequential I/O performance storage controller cache, Controller cacheguidelines for, Guidelines read-ahead caching, Read-ahead caching throughput testing, Disk cache write-back caching, Write-back caching caching, Hadoop and the Linux Storage Stackenabling name service caching, OS Configuration for Hadoop HDFS, Short-Circuit and Zero-Copy Readscache administration commands, Short-Circuit and Zero-Copy Reads instructing Linux to minimize, The Linux Page Cache of Sentry roles and permissions by services, Impala Canonical Name (CNAME) records (DNS), DNS round robin CAP theorem, Quorum spanning with two datacenters catalog, Impala daemons catalog server, Catalog server categories (cable), Layer 1 Recommendations center of excellence or competence, Do I Need a Center of Excellence/Competence?


pages: 161 words: 39,526

Applied Artificial Intelligence: A Handbook for Business Leaders by Mariya Yao, Adelyn Zhou, Marlene Jia

Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, artificial general intelligence, autonomous vehicles, backpropagation, business intelligence, business process, call centre, chief data officer, cognitive load, computer vision, conceptual framework, data science, deep learning, DeepMind, en.wikipedia.org, fake news, future of work, Geoffrey Hinton, industrial robot, information security, Internet of things, iterative process, Jeff Bezos, job automation, machine translation, Marc Andreessen, natural language processing, new economy, OpenAI, pattern recognition, performance metric, price discrimination, randomized controlled trial, recommendation engine, robotic process automation, Salesforce, self-driving car, sentiment analysis, Silicon Valley, single source of truth, skunkworks, software is eating the world, source of truth, sparse data, speech recognition, statistical model, strong AI, subscription business, technological singularity, The future is already here

As with the hiring process, the repetitive nature of these administrative tasks is well suited to automation. Companies like Talla use AI to take over the servicing of administrative questions and requests. Employees get answers to routine questions more quickly, while HR specialists get more time and mental firepower to devote to creating high-value deliverables. 15. Business Intelligence and Analytics Business intelligence (BI) creates meaning from data that your company collected. The goal is to leverage that meaning to guide future business decisions. For example, your company may want to know whether a specific product is selling well, and knowing that the 18- to 25-year-old demographic loves your product will affect how that product will be marketed in the future.

General and Administrative Finance and Accounting Legal and Compliance Records Maintenance General Operations 14. Human Resources and Talent Matching Candidates to Positions Managing the Interview Process Intelligent Scheduling Career Planning and Retention Risk Analysis Administrative Functions 15. Business Intelligence and Analytics Data Wrangling Data Architecture Analytics 16. Software Development 17. Marketing Digital Ad Optimization Recommendations and Personalization 18. Sales Customer Segmentation Lead Qualification and Scoring Sales Development Sales Analytics 19.

The last section of our book, “AI For Enterprise Functions,” highlights popular AI applications for common business functions. Chapter 12 summarizes some of the challenges of adopting AI solutions for enterprises. Chapters 13 and 14 introduce common AI applications in essential administrative functions like finance, legal, and HR, while Chapters 15 and 16 describe how machine learning can dramatically improve business intelligence, analytics, and software development. Chapters 17, 18, and 19 focus on the revenue-generating functions of sales, marketing, and customer service. Finally, Chapter 20 emphasizes the ethical responsibility that you, as business and technology leaders, have towards your workforce as well as towards ensuring that any technologies that you build have a benevolent impact on your customers, employees, and society as a whole.


pages: 721 words: 197,134

Data Mining: Concepts, Models, Methods, and Algorithms by Mehmed Kantardzić

Albert Einstein, algorithmic bias, backpropagation, bioinformatics, business cycle, business intelligence, business process, butter production in bangladesh, combinatorial explosion, computer vision, conceptual framework, correlation coefficient, correlation does not imply causation, data acquisition, discrete time, El Camino Real, fault tolerance, finite state, Gini coefficient, information retrieval, Internet Archive, inventory management, iterative process, knowledge worker, linked data, loose coupling, Menlo Park, natural language processing, Netflix Prize, NP-complete, PageRank, pattern recognition, peer-to-peer, phenotype, random walk, RFID, semantic web, speech recognition, statistical model, Telecommunications Act of 1996, telemarketer, text mining, traveling salesman, web application

The company concentrates on automatic intelligent analyses on a large-scale base, that is, for large amounts of structured data-like database systems and unstructured data-like texts. The open-source data-mining specialist Rapid-I enables other companies to use leading-edge technologies for data mining and business intelligence. The discovery and leverage of unused business intelligence from existing data enables better informed decisions and allows for process optimization. SIPNA Publisher: http://eric.univ-lyon2.fr/∼ricco/sipina.html Sipina-W is publicly available software that includes different traditional data-mining techniques such as CART, Elisee, ID3, C4.5, and some new methods for generating decision trees.

Oracle Data Mining Vendor: Oracle (www.oracle.com) Oracle Data Mining (ODM)—an option to Oracle Database 11 g Enterprise Edition—enables customers to produce actionable predictive information and build integrated business intelligence applications. Using data-mining functionality embedded in Oracle Database 11 g, customers can find patterns and insights hidden in their data. Application developers can quickly automate the discovery and distribution of new business intelligence—predictions, patterns and discoveries—throughout their organization. Optimus RP Vendor: Golden Helix Inc. (www.goldenhelix.com) Optimus RP, uses Formal Inference-based Recursive Modeling (recursive partitioning based on dynamic programming) to find complex relationships in data and to build highly accurate predictive and segmentation models.

FastStats™ Vendor: APTECO Limited (www.apteco.com) FastStats Suite, marketing analysis products, including data mining, customer profiling, and campaign management. IBM Intelligent Miner Vendor: IBM (www.ibm.com) DB2 Data Warehouse Edition (DWE) is a suite of products that combines the strength of DB2 Universal Database™ (DB2 UDB) with the powerful business intelligence infrastructure from IBM®. DB2 Data Warehouse Edition provides a comprehensive business intelligence platform with the tools your enterprise and partners need to deploy and build next generation analytic solutions. KnowledgeMiner Vendor: KnowledgeMiner Software (www.knowledgeminer.com) KnowledgeMiner, a self-organizing modeling tool that uses GMDH neural nets and AI to easily extract knowledge from data.


Data Mining: Concepts and Techniques: Concepts and Techniques by Jiawei Han, Micheline Kamber, Jian Pei

backpropagation, bioinformatics, business intelligence, business process, Claude Shannon: information theory, cloud computing, computer vision, correlation coefficient, cyber-physical system, database schema, discrete time, disinformation, distributed generation, finite state, industrial research laboratory, information retrieval, information security, iterative process, knowledge worker, linked data, machine readable, natural language processing, Netflix Prize, Occam's razor, pattern recognition, performance metric, phenotype, power law, random walk, recommendation engine, RFID, search costs, semantic web, seminal paper, sentiment analysis, sparse data, speech recognition, statistical model, stochastic process, supply-chain management, text mining, thinkpad, Thomas Bayes, web application

To demonstrate the importance of applications as a major dimension in data mining research and development, we briefly discuss two highly successful and popular application examples of data mining: business intelligence and search engines. 1.6.1. Business Intelligence It is critical for businesses to acquire a better understanding of the commercial context of their organization, such as their customers, the market, supply and resources, and competitors. Business intelligence (BI) technologies provide historical, current, and predictive views of business operations. Examples include reporting, online analytical processing, business performance management, competitive intelligence, benchmarking, and predictive analytics. “How important is business intelligence?” Without data mining, many businesses may not be able to perform effective market analysis, compare customer feedback on similar products, discover the strengths and weaknesses of their competitors, retain highly valuable customers, and make smart business decisions.

Without data mining, many businesses may not be able to perform effective market analysis, compare customer feedback on similar products, discover the strengths and weaknesses of their competitors, retain highly valuable customers, and make smart business decisions. Clearly, data mining is the core of business intelligence. Online analytical processing tools in business intelligence rely on data warehousing and multidimensional data mining. Classification and prediction techniques are the core of predictive analytics in business intelligence, for which there are many applications in analyzing markets, supplies, and sales. Moreover, clustering plays a central role in customer relationship management, which groups customers based on their similarities.

The partitioning is not performed by humans, but by the clustering algorithm. Hence, clustering is useful in that it can lead to the discovery of previously unknown groups within the data. Cluster analysis has been widely used in many applications such as business intelligence, image pattern recognition, Web search, biology, and security. In business intelligence, clustering can be used to organize a large number of customers into groups, where customers within a group share strong similar characteristics. This facilitates the development of business strategies for enhanced customer relationship management.


pages: 204 words: 58,565

Keeping Up With the Quants: Your Guide to Understanding and Using Analytics by Thomas H. Davenport, Jinho Kim

behavioural economics, Black-Scholes formula, business intelligence, business process, call centre, computer age, correlation coefficient, correlation does not imply causation, Credit Default Swap, data science, en.wikipedia.org, feminist movement, Florence Nightingale: pie chart, forensic accounting, global supply chain, Gregor Mendel, Hans Rosling, hypertext link, invention of the telescope, inventory management, Jeff Bezos, Johannes Kepler, longitudinal study, margin call, Moneyball by Michael Lewis explains big data, Myron Scholes, Netflix Prize, p-value, performance metric, publish or perish, quantitative hedge fund, random walk, Renaissance Technologies, Robert Shiller, self-driving car, sentiment analysis, six sigma, Skype, statistical model, supply-chain management, TED Talk, text mining, the scientific method, Thomas Davenport

But whether or not you’re actually crunching the numbers yourself, it’s useful to know something about the three steps involved in solving the problem. * * * How to Find a Quant If you need a quantitative analyst to help you solve your problem, here are some ways to do it: If you work for a large company, you probably have some—so look in places like Market Research, Business Intelligence, or Operations Research. If you don’t have any, there are plenty of consultants to whom you can turn. Do an Internet search for “business analytics consultants” or look at the helpful list from KDnuggets (http://www .kdnuggets.com/companies/consulting.html). If you want some offshore analytics consultants, the best are to be found in India; check out such firms as Mu Sigma, Fractal Analytics, and Genpact.

In “Key Software Vendors for Different Analysis Types,” we list some of the key vendors for reporting software. 5. Data analysis * * * Key Software Vendors for Different Analysis Types (listed alphabetically) REPORTING SOFTWARE BOARD International IBM Cognos Information Builders WebFOCUS Oracle Business Intelligence (including Hyperion) Microsoft Excel/SQL Server/SharePoint MicroStrategy Panorama SAP BusinessObjects INTERACTIVE VISUAL ANALYTICS QlikTech QlikView Tableau TIBCO Spotfire QUANTITATIVE OR STATISTICAL MODELING IBM SPSS R (an open-source software package) SAS * * * While all of the listed reporting software vendors also have capabilities for graphical display, some vendors focus specifically on interactive visual analytics, or the use of visual representations of data and reporting.

SAS has sponsored new programs at Louisiana State and Texas A&M. The University of San Francisco has created a West Coast program as well, and New York University has begun one in New York. One recent survey found that fifty-nine universities were offering either degrees or majors in business analytics or business intelligence—thirty-seven at the masters level and twenty-two undergraduate programs.13 Schools are also beginning to offer courses in data science, and degree programs will follow soon. Quantitative Habits Attitudes are important, but so are habits. It has often been said that it is easier to act your way into a new way of thinking than to think your way into a new way of acting.


pages: 457 words: 112,439

Zero History by William Gibson

augmented reality, business intelligence, dark matter, edge city, hive mind, invisible hand, messenger bag, new economy, pattern recognition, Pepto Bismol, placebo effect, Ponzi scheme, RFID, too big to fail

We can sit here …” He led them to an L-shaped bench of dull aluminum mesh, in the shadow of a hanging stairway, the sort of place that would have been a smoking-nest, when people smoked in office buildings. “You recall the Amsterdam dealer we bought your jacket from? His mysterious picker?” “Vaguely.” “We’ve gone back to that. Or, rather, a strategic business intelligence unit I’ve hired in the Hague has. An example of Sleight pushing me out of my comfort zone. I’ve never trusted private security firms, private investigators, private intelligence firms, at all. In this case, though, they have no idea who they’re working for.” “And?” Hollis, seated now, Milgrim beside her, was watching Bigend closely.

They aren’t very fast, but if people see them, their first thought is that they’re hallucinating.” Milgrim nodded. “He’s coming,” he said. “Gracie.” “To London?” “She said he’ll be here soon.” “He has Sleight,” Bigend said, “so he knows that having a look at his pants was simply basic strategic business intelligence. It isn’t as though we’ve done anything to harm him. Or ‘Foley’ either, for that matter.” Milgrim looked from Bigend to Hollis, eyes wide. “A friend of mine has been in a traffic accident,” Hollis said. “I have to stay in town until I know how he is.” Bigend frowned. “Anyone I know?” “No,” said Hollis.

“No,” said Bigend, “there isn’t.” “What she most particularly wanted to convey to you,” Milgrim said, “Winnie Tung Whitaker, is that Gracie believes you’re his competitor. Which means, to him, that you’re his enemy.” “I’m not his enemy,” said Bigend. “You had me steal the design of his pants.” “ ‘Business intelligence.’ If you hadn’t thrown Foley under some random Russians, this would all be much easier. And it wouldn’t be distracting me from more important things. I am, however, glad that we had this opportunity to discuss the matter in greater detail, privately.” “Bent cops are one thing,” said Milgrim.


pages: 176 words: 55,819

The Start-Up of You: Adapt to the Future, Invest in Yourself, and Transform Your Career by Reid Hoffman, Ben Casnocha

Airbnb, Andy Kessler, Apollo 13, Benchmark Capital, Black Swan, business intelligence, Cal Newport, Clayton Christensen, commoditize, David Brooks, Donald Trump, Dunbar number, en.wikipedia.org, fear of failure, follow your passion, future of work, game design, independent contractor, information security, Jeff Bezos, job automation, Joi Ito, late fees, lateral thinking, Marc Andreessen, Mark Zuckerberg, Max Levchin, Menlo Park, out of africa, PalmPilot, Paul Graham, paypal mafia, Peter Thiel, public intellectual, recommendation engine, Richard Bolles, risk tolerance, rolodex, Salesforce, shareholder value, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley startup, social web, Steve Jobs, Steve Wozniak, the strength of weak ties, Tony Hsieh, transaction costs, Tyler Cowen

NAVIGATE PROFESSIONAL CHALLENGES WITH NETWORK INTELLIGENCE Entrepreneurs navigate the day-to-day issues of running a company by gathering intelligence: actionable, timely information on all facets of their business, including industry trends, opportunities, competitors’ activities, customer sentiment, promising young talent, and sales trends. In a business, intelligence serves as a GPS device. You need good intelligence to run the start-up of you. The preceding chapters should have prompted questions in your mind like: How desirable are my skills in the changing market? How do I know when I should pivot into a new industry niche? What are the best job opportunities and how do I exploit them? These are not easy questions. They’re certainly not questions you can answer by merely reflecting for a few minutes or filling out a worksheet. You, too, need business intelligence to navigate these challenges. You get it by talking to people in your network.

They take stock of their assets, aspirations, and the market realities to develop a competitive advantage. They craft flexible, iterative plans. They build a network of relationships throughout their industy that outlives their start-up. They aggressively seek and create breakout opportunities that involve focused risk, and actively manage that risk. They tap their network for the business intelligence to navigate tough challenges. And, they do these things from the moment they hatch that nascent idea to every day after that—even as the companies go from being run out of a garage to occupying floors of office space. To succeed professionally in today’s world, you need to adopt these same entrepreneurial strategies.


Industry 4.0: The Industrial Internet of Things by Alasdair Gilchrist

3D printing, additive manufacturing, air gap, AlphaGo, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, business intelligence, business logic, business process, chief data officer, cloud computing, connected car, cyber-physical system, data science, deep learning, DeepMind, deindustrialization, DevOps, digital twin, fault tolerance, fulfillment center, global value chain, Google Glasses, hiring and firing, industrial robot, inflight wifi, Infrastructure as a Service, Internet of things, inventory management, job automation, low cost airline, low skilled workers, microservices, millennium bug, OSI model, pattern recognition, peer-to-peer, platform as a service, pre–internet, race to the bottom, RFID, Salesforce, Skype, smart cities, smart grid, smart meter, smart transportation, software as a service, stealth mode startup, supply-chain management, The future is already here, trade route, undersea cable, vertical integration, warehouse robotics, web application, WebRTC, Y2K

It will be possible to manage threats proactively by moving cargo from air to sea or vice versa to mitigate the threats of strike action. Similarly, urgent cargo routes can be altered in real-time if predicative analysis of all the global data shows a high risk of civil unrest or bad weather on route, which could seriously delay delivery. Predictive analysis through Big Data is becoming a required tool for business intelligence analysis; it is believed that over 80% of businesses will adopt it in one form or another in 2016. The promise that predictive analysis holds for global logistics is that they will be able to take proactive action to mitigate potential threats to their operations and keep their freight moving to the destination.

Now that they have the template, what is stopping them from running off 2 million extras to sell on the black market? 55 56 Chapter 3 |TheTechnical and Business Innovators of the Industrial Internet The big point about Big Data is that it requires vast amounts of intellect to distil business value from it. Creating data lakes will not automatically facilitate business intelligence. If you do not know the correct question to ask of the data, how you can expect a sensible answer? This is where we have to understand how or if machines think and collaborate. M2M Learning and Artificial Intelligence Big Data empowers M2M learning and artificial intelligence, the larger the pool of data the more trustworthy the forecasts—or so it would seem.

Previously, interest in advanced algorithms had been limited to certain business sectors such as insurance, marketing, and finance where risk and opportunity were the key drivers. However, with the advent of Big Data and the cloud resources to leverage it, has come interest from many other sectors in industry, in search of ways to improve decisionmaking, reduce risk, and optimize business outcomes. This has brought about a seismic shift away from traditional business intelligence, which focuses more on descriptive analysis that uses historical data to determine what has happened. Instead, business leaders are looking to advanced analytics, which complements descriptive analysis by taking the analysis further via diagnostic analysis by asking, not just what happened, but why it happened.


pages: 481 words: 121,669

The Invisible Web: Uncovering Information Sources Search Engines Can't See by Gary Price, Chris Sherman, Danny Sullivan

AltaVista, American Society of Civil Engineers: Report Card, Bill Atkinson, bioinformatics, Brewster Kahle, business intelligence, dark matter, Donald Davies, Douglas Engelbart, Douglas Engelbart, full text search, HyperCard, hypertext link, information retrieval, Internet Archive, it's over 9,000, joint-stock company, knowledge worker, machine readable, machine translation, natural language processing, pre–internet, profit motive, Project Xanadu, publish or perish, search engine result page, side project, Silicon Valley, speech recognition, stealth mode startup, Ted Nelson, Vannevar Bush, web application

This book introduces readers to the basics of interface design and explains why a design evaluation should be tied to the use and purchase of information resources. 1999/224 pp/softbound/ISBN 0-910965-31-5 $29.95 1999/224 pp/hardbound/ISBN 0-910965-39-0 $39.95 397 448 Internet Business Intelligence How to Build a Big Company System on a Small Company Budget By David Vine According to author David Vine, business success in the competitive, global marketplace of the 21st century will depend on a firm’s ability to use information effectively—and the most successful firms will be those that harness the Internet to create and maintain a powerful information edge. In Internet Business Intelligence, Vine explains how any company can build a complete, low-cost, Internet-based business intelligence system that really works. If you’re fed up with Internet hype and wondering “Where’s the beef?

This book helps readers identify overseas buyers, find foreign suppliers, investigate potential partners and competitors, uncover international market research and industry analysis, and much more. 2001/380 pp/softbound/ISBN 0-910965-46-3 $29.95 442 Millennium Intelligence Understanding and Conducting Competitive Intelligence in the Digital Age By Jerry P. Miller and the Business Intelligence Braintrust With contributions from 12 of the world’s leading business intelligence practitioners, here is a tremendously informative and practical look at the CI process, how it is changing, and how it can be managed effectively in the Digital Age. Loaded with case studies, tips, and techniques. 2000/276 pp/softbound/ISBN 0-910965-28-5 $29.95 Internet Prophets The Complete Guide to Enlightened E-Business Strategies By Mary Diffley Since the bursting of the dot.com balloon, companies are approaching e-business with a new wariness—and rightly so, according to author and entrepreneur Mary Diffley.


Demystifying Smart Cities by Anders Lisdorf

3D printing, artificial general intelligence, autonomous vehicles, backpropagation, behavioural economics, Big Tech, bike sharing, bitcoin, business intelligence, business logic, business process, chief data officer, circular economy, clean tech, clean water, cloud computing, computer vision, Computing Machinery and Intelligence, congestion pricing, continuous integration, crowdsourcing, data is the new oil, data science, deep learning, digital rights, digital twin, distributed ledger, don't be evil, Elon Musk, en.wikipedia.org, facts on the ground, Google Glasses, hydroponic farming, income inequality, information security, Infrastructure as a Service, Internet of things, Large Hadron Collider, Masdar, microservices, Minecraft, OSI model, platform as a service, pneumatic tube, ransomware, RFID, ride hailing / ride sharing, risk tolerance, Salesforce, self-driving car, smart cities, smart meter, software as a service, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Stuxnet, Thomas Bayes, Turing test, urban sprawl, zero-sum game

There are different tools, both commercial and open source, but they all work on precompiled files that are being exposed to end users for download or browsing in a tabular format on the platform. It is usually possible to search for descriptions and tags in order to find the data. The data portals are typically used for external users. Business intelligence – The business intelligence (BI) tools have been a mainstay of reporting for decades already. These are being used internally and also run on prepared data. But BI tools usually connect to a Data Warehouse, which is a relational database, where data has been optimized for particular reporting needs. Today the end user often has a great degree of flexibility in how data can be viewed and filtered in the BI tool.

Similarly, if one consumption style dominates, like APIs, a different solution is preferable. The major benefit of the store once/open consumption architecture is that data consistency is improved, and data lineage transparency is higher. Persist data in lowest granularity – In traditional business intelligence, attention has been given to finding the right grain for data. This is still important, but first data should be stored in the absolute lowest granularity. If a higher grain is needed, it can easily be aggregated by a subsequent process. The reason for this is that whatever is the current need, the future may require lower granularity, and if this is not even stored, it will be impossible to get to without rebuilding the solution.


pages: 471 words: 127,852

Londongrad: From Russia With Cash; The Inside Story of the Oligarchs by Mark Hollingsworth, Stewart Lansley

"World Economic Forum" Davos, Berlin Wall, Big bang: deregulation of the City of London, Bob Geldof, Bullingdon Club, business intelligence, company town, Cornelius Vanderbilt, corporate governance, corporate raider, credit crunch, crony capitalism, Donald Trump, energy security, Etonian, F. W. de Klerk, Global Witness, income inequality, kremlinology, Larry Ellison, Londongrad, mass immigration, mega-rich, Mikhail Gorbachev, offshore financial centre, paper trading, plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, power law, rent-seeking, Ronald Reagan, Skype, Sloane Ranger

It appeared as if Russian paranoia had been caught by their British minders. Curtis relied on ISC Global heavily for his own security. The lawyer not only switched pay-as-you-go mobile phones on a regular basis, but the Park Lane office was swept for bugs every day. And for good reason. Despite his infrequent visits to London, he was under surveillance by business intelligence agencies working for commercial competitors to Yukos. In just one day in 2003 there were 7,000 attempts to hack into the computers at Curtis & Co. Bugging of meeting rooms was also a concern. ‘The problem with any listening device is the battery,’ said one top professional. ‘The power is the key to any successful bug.

One of the litigants was Kenneth Dart, who had invested heavily in Yukos subsidiaries - and lost equally heavily - and who was now leading a class action lawsuit against the company on behalf of minority shareholders. In 2003 Dart hired a private security firm in the US to investigate Curtis - the firm’s first move was to approach UK business intelligence agencies. One such company that it initially approached was ISC itself - unaware that it was owned by Curtis. They offered ISC $1 million to unravel the restructuring of Yukos and the ultimate beneficiaries. The US security firm then turned to private investigators, tasking their operatives and Russian journalists to track Curtis’s movements, as well as those of his staff.

To some extent, the oligarch encouraged him, but he did not trust the former FSB officer because of his incapability of telling the truth and his indiscretion. ‘If he had any information, he would leak it within three seconds,’ one former Berezovsky aide said. As Litvinenko become alienated from Berezovsky, the more he sought a new role in the murky world of corporate espionage and business intelligence. He began to boast about his expertise in investigating Russian organized crime and started consulting for London-based security and commercial intelligence companies specializing in investigating Russian businessmen. Their clients were international companies, law firms, and banks that needed due diligence to be carried out on Russian individuals and the Kremlin.


RDF Database Systems: Triples Storage and SPARQL Query Processing by Olivier Cure, Guillaume Blin

Amazon Web Services, bioinformatics, business intelligence, cloud computing, database schema, fault tolerance, folksonomy, full text search, functional programming, information retrieval, Internet Archive, Internet of things, linked data, machine readable, NP-complete, peer-to-peer, performance metric, power law, random walk, recommendation engine, RFID, semantic web, Silicon Valley, social intelligence, software as a service, SPARQL, sparse data, web application

See Basically available, soft state, eventually consistent (BASE) Basically available, soft state, eventually consistent (BASE), 28 BerkeleyDB, 29 Berlin SPARQL benchmark (BSBM), 77 BGP abstraction, 151 BI. See Business intelligence (BI) Bigdata, the system, 1, 6, 119, 146, 149 federation for, 178 HAJournalServer, 178 variety, 2 velocity, 2, 215 veracity, 2 volume, 1 BigTable database, 23, 28, 33, 34 Binary JSON (BSON), 31 Binary tuple (BT) index, 136 BitMat system, 6, 121, 156 Bit vectors, 95 Bnodes, 45 BRAHMS, 112 BSBM. See Berlin SPARQL benchmark (BSBM) B-trees, 14 B+tree secondary-memory data structure, 113 Burrows-Wheeler transform (BWT), 91 Business intelligence (BI), 17 BWT. See Burrows-Wheeler transform (BWT) C Cascade style sheets (CSS), 4 Cassandra database, 23, 28, 34, 138 Cassandra Query Language (CQL), 34 Chord, 174 CIA world factbook, 5 Cisco, 3 CLEAR operations, 59 Closed World Assumption (CWA), 193 Clustered index, 14 Clustrix, 38 CMSs.

A ubiquitous example using OLTP databases is e-commerce applications. The goal of OLAP, together with data mining, is to analyze the data contained in a database such that decisions and predictions can be taken by end-users or computer agents. These systems are implemented in so-called data warehouses and are frequently used in fields such as business intelligence (BI). In general, transactions are rare in OLAP (e.g., write operations are not frequent), but when some are performed they usually involve a very large number of operations. The execution of a million rows on a weekly or monthly basis is not rare. Therefore, the state of a database is more stable because it changes less frequently than in the OLTP context.

Some of their requisites concern the integration of new features: ­declarative Database Management Systems query languages, solutions for defining schemata, the ability to select different consistency characteristics (e.g., strong or eventual), and integrating integrity constraints to enhance data quality and business intelligence processing.The most successful NoSQL stores are all going this way. For instance, an important work has been conducted by the team at DataStax (the main contributor on the Cassandra database) on designing a declarative, SQL-influenced query language, namely CQL. Note that this language does not just provide a Data Manipulation Language (DML) but also a Data Definition Language (DDL) that enables us to create/drop keyspaces (i.e., databases), tables, and indexes.


pages: 502 words: 107,657

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel

Alan Greenspan, Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apollo 11, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, butter production in bangladesh, call centre, Charles Lindbergh, commoditize, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, data science, driverless car, en.wikipedia.org, Erik Brynjolfsson, Everything should be made as simple as possible, experimental subject, Google Glasses, happiness index / gross national happiness, information security, job satisfaction, Johann Wolfgang von Goethe, lifelogging, machine readable, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mass immigration, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, Norbert Wiener, personalized medicine, placebo effect, prediction markets, Ray Kurzweil, recommendation engine, risk-adjusted returns, Ronald Coase, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, Shai Danziger, software as a service, SpaceShipOne, speech recognition, statistical model, Steven Levy, supply chain finance, text mining, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra, zero-sum game

—Dennis R. Mortensen, CEO of Visual Revenue, former Director of Data Insights at Yahoo! “This book is an invaluable contribution to predictive analytics. Eric’s explanation of how to anticipate future events is thought provoking and a great read for everyone.” —Jean Paul Isson, Global VP Business Intelligence and Predictive Analytics, Monster Worldwide; coauthor, Win with Advanced Business Analytics: Creating Business Value from Your Data “Eric Siegel’s book succeeds where others have failed—by demystifying big data and providing real-world examples of how organizations are leveraging the power of predictive analytics to drive measurable change.”

—Jared Waxman, Web Marketer at LegalZoom, previously at Adobe, Amazon, and Intuit “Siegel covers predictive analytics from start to finish, bringing it to life and leaving you wanting more.” —Brian Seeley, Manager, Risk Analytics, Paychex, Inc. “A wonderful look into the world of predictive analytics from the perspective of a true practitioner.” —Shawn Hushman, VP, Analytic Insights, Kelley Blue Book “An excellent exposition on the next generation of business intelligence—it’s really mankind’s latest quest for artificial intelligence.” —Christopher Hornick, President and CEO, HBSC Strategic Services “A must—Predictive Analytics provides an amazing view of the analytical models that predict and influence our lives on a daily basis. Siegel makes it a breeze to understand, for all readers.”

Whereas forecasting estimates the total number of ice cream cones to be purchased next month in Nebraska, predictive technology tells you which individual Nebraskans are most likely to be seen with cone in hand. PA leads within the growing trend to make decisions more “data driven,” relying less on one’s “gut” and more on hard, empirical evidence. Enter this fact-based domain and you’ll be attacked by buzzwords, including analytics, big data, business intelligence, and data science. While PA fits underneath each of these umbrellas, these evocative terms refer more to the culture and general skill sets of technologists who do an assortment of creative, innovative things with data, rather than alluding to any specific technology or method. These areas are broad; in some cases, they refer simply to standard Excel reports—that is, to things that are important and require a great deal of craft, but may not rely on science or sophisticated math.


pages: 353 words: 104,146

European Founders at Work by Pedro Gairifo Santos

business intelligence, clean tech, cloud computing, crowdsourcing, deal flow, do what you love, fail fast, fear of failure, full text search, Hacker News, hockey-stick growth, information retrieval, inventory management, iterative process, Jeff Bezos, Joi Ito, Lean Startup, Mark Zuckerberg, Multics, natural language processing, pattern recognition, pre–internet, recommendation engine, Richard Stallman, Salesforce, Silicon Valley, Skype, slashdot, SoftBank, Steve Jobs, Steve Wozniak, subscription business, technology bubble, TED Talk, web application, Y Combinator

At that time, the company's management team was in Paris but I decided to move our senior executives, along with a new CFO, to California to be closer to our customers and the US financial community. I also restructured the software development process, created a brand new internet-based business intelligence product and, over the next year and a half, we fixed the buggy product line which, in the end, was very attractive to customers. We started to grow again and over two years, our market cap went from $100 million to almost $5 billion. 1997 to 1999 was an amazing time for the company as we expanded into more and more countries.

Liautaud: No, we managed to go through with the European flotation because our business was fairly untouched, relatively speaking. We continued to grow at thirty to forty percent a year, even through the downturn. Through that downturn however, we changed how we positioned our offerings to suit our customers' changing the requirements. Everyone was in some sort of cost-cutting mode and with the business intelligence that Business Objects offered, customers could optimize their costs and see where the inefficiencies lay. So, we switched our value proposition from “make your business better, add visibility and increase revenue” to “make your business more efficient, see where you waste money and cut your costs.”

Santos: So, from that period on, to the actual period that Business Objects was sold, can you walk us through a bit of that period? Liautaud: We acquired Crystal in 2003, integrated the business in 2004, and reached a billion dollars in revenue in 2005. We continued to expand and bought a few small businesses. By 2007 we saw about $1.5 billion in revenue. Business intelligence was becoming more and more important to the IT industry and a number of the industry's biggest companies realized that they needed to be in it. Oracle decided to buy Hyperion in 2007. Actually Oracle had approached us before, but with price we didn't like. This acquisition triggered a wave of interest from the likes of IBM and SAP and in the summer of 2007, we had discussions with both.


pages: 283 words: 78,705

Principles of Web API Design: Delivering Value with APIs and Microservices by James Higginbotham

Amazon Web Services, anti-pattern, business intelligence, business logic, business process, Clayton Christensen, cognitive dissonance, cognitive load, collaborative editing, continuous integration, create, read, update, delete, database schema, DevOps, fallacies of distributed computing, fault tolerance, index card, Internet of things, inventory management, Kubernetes, linked data, loose coupling, machine readable, Metcalfe’s law, microservices, recommendation engine, semantic web, side project, single page application, Snapchat, software as a service, SQL injection, web application, WebSocket

Ask the following questions to assess the value that each API brings: ■ Does the API help provide a competitive advantage over other market offerings? ■ Does the API reduce the cost of doing business, perhaps by reducing manual processes? ■ Does the API create a new revenue stream or improve an existing revenue stream? ■ Is the API producing business intelligence, market insights, or decisioning factors? ■ Does the API automate repetitive tasks that free the organization for more critical business functions? If the answer to all the questions is ‘no’, then the value produced is low. Answering yes to one or more questions results in the API offering value to the business or marketplace.

Automated Security Testing Each week, a new headline appears that indicates a company has been hacked and private information exposed. Security is a process, not a product, and a continual one at that. Security testing aims to answer the following questions: ■ Is the API protected against attacks? ■ Does the API offer opportunities for sensitive data to be leaked? ■ Is someone scraping my API and compromising business intelligence through data? While not typically associated with automated testing, security testing is an active process that includes design time review processes, development time static and dynamic code analysis, and run time monitoring. Design time and development time security testing is often made up of policies and tools that are designed to prevent leaking sensitive data through design reviews that identify potential concerns.

This is common for many API vendors that consider an undocumented API as secure, such as SnapChat ■ Exposing the exact location, by latitude and longitude, of users because a previously private Tinder API was opened for end-users. A thorough security review prior to opening the API to developers would have identified that the mobile app, not the API, was responsible for hiding the actual physical location of their users These recent breaches span from low-reward results, such as disclosing business intelligence as a competitive advantage, to high-reward results that can disclose extremely sensitive data. One even jeopardized the safety of individuals by disclosing their exact location! Unfortunately, some API providers may take shortcuts in securing their internal APIs. Perhaps they mistakenly think that if they do not document the potential access to the API, no one will go looking for it.


pages: 138 words: 40,787

The Silent Intelligence: The Internet of Things by Daniel Kellmereit, Daniel Obodovski

Airbnb, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, business intelligence, call centre, Clayton Christensen, cloud computing, commoditize, connected car, crowdsourcing, data acquisition, driverless car, en.wikipedia.org, Erik Brynjolfsson, first square of the chessboard, first square of the chessboard / second half of the chessboard, Freestyle chess, Google X / Alphabet X, Internet of things, lifelogging, Metcalfe’s law, Network effects, Paul Graham, Ray Kurzweil, RFID, Robert Metcalfe, Salesforce, self-driving car, Silicon Valley, smart cities, smart grid, software as a service, Steve Jobs, The future is already here, the long tail, Tony Fadell, vertical integration, web application, Y Combinator, yield management

I think on the software business analytics, it becomes easier and easier to build applications. To me, at the end of the day, M2M is really about data. You’re getting data off devices or sensors that have been hard to get, but once it’s in the cloud, it’s data. So all the data tools, if it’s big data or any kind of business intelligence software, all that stuff is applicable to it. So I think just the same tools and technologies, like Google or Facebook, really do a lot of analytics on their users, and the amount of data that they have, they’re very applicable into the M2M space. When deciding on which networks to run an M2M solution, we would like to point out the ubiquity versus granularity problem.

Since investment in the Internet of Things has until now been more of a futuristic topic, and the understanding and definition of the market varies notably, forecasts are all over the place. For example, IDC, a market research firm, estimates the value of intelligent systems at $1.7 trillion already, growing to $2.4 trillion by 2017.30 It’s interesting if you look at the high-growth markets that are currently developing around cloud computing, big data, and business intelligence. These markets are in the double-digit billions, and are often not counted toward the M2M market. This shows how blurry the borders are, and that we can expect a number of additional growth segments that we do not see or envision today. The question many investors raise is: How does this growth come about?


pages: 340 words: 91,745

Duped: Double Lives, False Identities, and the Con Man I Almost Married by Abby Ellin

Bernie Madoff, bitcoin, Burning Man, business intelligence, Charles Lindbergh, cognitive dissonance, cognitive load, content marketing, dark triade / dark tetrad, Donald Trump, double helix, dumpster diving, East Village, fake news, feminist movement, forensic accounting, fudge factor, hiring and firing, Internet Archive, John Darwin disappearance case, longitudinal study, Lyft, mandatory minimum, meta-analysis, pink-collar, Ponzi scheme, post-truth, Robert Hanssen: Double agent, Ronald Reagan, Silicon Valley, Skype, Snapchat, TED Talk, telemarketer, theory of mind, Thomas Kuhn: the structure of scientific revolutions

Then a silver-haired man of medium build in a suit and tie stood before us: Phil Houston, the Wizard himself, a twenty-five-year veteran of the CIA, a master polygrapher, and the coauthor of two best-sellers, Spy the Lie and Get the Truth. He’s also QVerity’s CEO.4 In 2001, after leaving “the Agency,” he cofounded Business Intelligence Advisors. BIA employed former and current CIA officers with the Agency’s approval. Apparently the CIA allowed moonlighting—everyone needs an extra buck now and then, even spies.5 Hedge funds, law firms, and Fortune 500 companies hired BIA to train investment analysts how to identify deception with their investment targets, and BIA charged them about $25,000 a day.6 BIA’s agents would sometimes attend investment conferences with their clients where publicly traded companies were presenting.

., 24 Ashley Madison, 35 Asperger’s, 60 attention deficit disorder, 60 Ault, Amber, 123–125 Barber, Maggie, 94–95, 112 Batali, Mario, 23 Baumeister, Roy, 145 behavior patterns, 215–216 The Believing Brain (Shermer), 148 Berg, A. Scott, 66 Bergman, Ingrid, 33 betrayal blindness, 156 bond, 131 gender and, 174–175 self-deception and, 130 self-trust from, 216 treachery and, 41–42 Betz-Hamilton, Axton, 172 Beyond Good and Evil (Nietzsche), 209 BIA. See Business Intelligence Advisors bias, 144, 148 the Bible, 39, 86 bin Laden, Osama, 1, 4–5, 118 Blair, Jayson, 150 Blink (Gladwell), 31 Blood Will Out (Kirn), 150 Bond, Charles F., 195 Bond, James, 37–39 Bouteuil, Astrid, 66 Bowers, Kathryn, 82 brain amygdala, 84, 125–126, 213 cognition areas and modularity, 105–108 cognitive dissonance and, 147–148 lies and, 84, 193 love and, 145–146 PTSD and, 123–126, 130, 184 trauma and, 213 Truthful Brain, 202 Brain Injury, 67 broapp.net, 41 Bronson, Po, 81 Brown, Jerry, 133 Brown, Sandra, 213, 214–215 Bruckheimer, Jerry, 19 Buddhism, 107 Bulger, Whitey, 40 Bundy, Ted, 203 Burstyn, Ellen, 64–65 Business Intelligence Advisors (BIA), 189–190 career, fraudulent credentials with, 41 CareerExcuse.com, 41 Carnal Abuse by Deceit: Why Lying to Get Laid Is a Crime (Short, J.), 131–132 Carver, Raymond, 35 Celebrity Sex Pass, 65 CEOs, psychopathy and, 74 chaos, truth and, 98–99 character disturbance, 78 supertraits, 213–215 trust and, 146, 160 Charlie Brown (fictional character), 148–149, 216 Chernow, Ron, 210 children, 101, 148 brains of, 84 double lives influencing, 129 as identity fraud victims, 172 lies and, 80–81 trust and, 146–147 Church, Angelica Schuyler, 210 CIA, 5, 24, 29, 37, 101 as paid liars, 22 spies, 109, 151 Cleckley, Hervey, 74 the Cliché, 137–143 Clinton, Bill, 97 Clinton, Hillary, 21, 87, 105 clusters of actions, 192 coercive control, 187–188 cognition areas, of brain, 105–108 cognitive dissonance, 147–148 coincidence of opposites (coniunctio oppositorum), 86 Collins, Diane, 178–184, 217 collusion, complicity and, 155–158 the Commander, 139, 155, 210–213 double life of, 1–10, 15–30, 92–93, 97–98, 115–121 exposed, 120–121, 218–219 investigative research into, 24–25 reasons for lies, 87–88 supertraits, 214 commission, lies of, 160 communication, nonverbal, 191–192 compartmentalization denial and, 149–150 double lives and, 96–97, 142 lies and, 99–100 relationships and, 110–113 self-integration, 103–104 undercover work and, 102 complicity, collusion and, 155–158 compulsive liars, 24, 27, 74–75 The Confidence Game (Konnikova), 143 coniunctio oppositorum (coincidence of opposites), 86 conscientiousness, 214, 215 ConsentAwareness.net, 130 control coercive, 187–188 question, 199 Converus, 202–203 corporations CEOs and psychopathy, 74 hypocrisy and, 106 with lies, 196 women and, 176–177 cowardice, fear and, 62 crime, 131–132, 135 coercive control as, 188 men and, 176 women and, 176–177 Crundwell, Rita, 171–172, 177 Cyr, Joseph, 72 Daniels, Stormy, 198 Dante Alighieri, 42, 105 “Dark Tetrad,” 73 Darville, Helen, 67–68 Darwin, Anne, 99–100 Darwin, John, 99–100 David, Larry, 139, 142 Dear Evan Hansen (play), 40 deaths, fake, 99–100 debt, in marriage, 31 Demara, Ferdinand Waldo, Jr., 71–73, 98 Demidenko, Helena, 67 Democratic National Convention (2008), 42 denial compartmentalization and, 149–150 of truth, 13, 128 DePaulo, Bella M., 59, 172–173, 195 depression, 124, 157 Derailed (Stapel), 34–35 Desai, Sonia, 123 detection, of lies language and, 193–195 methods for, 197, 202–203 polygraph, 198–202, 206 with unconscious mind, 204–205 Devine, Jack, 151 Diagnostic and Statistical Manual of Mental Disorders (DSM), 74, 124 Diana (Princess), 6 DiCaprio, Leonardo, 35 Dickens, Charles, 86 Dirks, Kurt T., 149 disorders attention deficit, 60 DSM, 74, 124 personality, 73, 74, 79, 123–124 PTSD, 123–126, 130, 184 disorientation, gaslighting and, 25, 119 Dolezal, Rachel, 68 domestic violence, 129, 130 Domingo, Plácido, 16 doppelgänger (double walker), 86 Dostoevsky, Fyodor, 86 double lives, 67–69 of Alvarez, 75–78, 80, 89 of the Commander, 1–10, 15–30, 92–93, 97–98, 115–121 compartmentalization and, 96–97, 142 of Demara, 71–73 escapism and, 63–64 families and, 108, 122–123, 126–130, 175–176 in marriage, 65–66, 126–130, 137–143, 163–171, 184–185, 209–210 double walker (doppelgänger), 86 The Double (Dostoevsky), 86 The Double Life of Charles A.

., 195 Bond, James, 37–39 Bouteuil, Astrid, 66 Bowers, Kathryn, 82 brain amygdala, 84, 125–126, 213 cognition areas and modularity, 105–108 cognitive dissonance and, 147–148 lies and, 84, 193 love and, 145–146 PTSD and, 123–126, 130, 184 trauma and, 213 Truthful Brain, 202 Brain Injury, 67 broapp.net, 41 Bronson, Po, 81 Brown, Jerry, 133 Brown, Sandra, 213, 214–215 Bruckheimer, Jerry, 19 Buddhism, 107 Bulger, Whitey, 40 Bundy, Ted, 203 Burstyn, Ellen, 64–65 Business Intelligence Advisors (BIA), 189–190 career, fraudulent credentials with, 41 CareerExcuse.com, 41 Carnal Abuse by Deceit: Why Lying to Get Laid Is a Crime (Short, J.), 131–132 Carver, Raymond, 35 Celebrity Sex Pass, 65 CEOs, psychopathy and, 74 chaos, truth and, 98–99 character disturbance, 78 supertraits, 213–215 trust and, 146, 160 Charlie Brown (fictional character), 148–149, 216 Chernow, Ron, 210 children, 101, 148 brains of, 84 double lives influencing, 129 as identity fraud victims, 172 lies and, 80–81 trust and, 146–147 Church, Angelica Schuyler, 210 CIA, 5, 24, 29, 37, 101 as paid liars, 22 spies, 109, 151 Cleckley, Hervey, 74 the Cliché, 137–143 Clinton, Bill, 97 Clinton, Hillary, 21, 87, 105 clusters of actions, 192 coercive control, 187–188 cognition areas, of brain, 105–108 cognitive dissonance, 147–148 coincidence of opposites (coniunctio oppositorum), 86 Collins, Diane, 178–184, 217 collusion, complicity and, 155–158 the Commander, 139, 155, 210–213 double life of, 1–10, 15–30, 92–93, 97–98, 115–121 exposed, 120–121, 218–219 investigative research into, 24–25 reasons for lies, 87–88 supertraits, 214 commission, lies of, 160 communication, nonverbal, 191–192 compartmentalization denial and, 149–150 double lives and, 96–97, 142 lies and, 99–100 relationships and, 110–113 self-integration, 103–104 undercover work and, 102 complicity, collusion and, 155–158 compulsive liars, 24, 27, 74–75 The Confidence Game (Konnikova), 143 coniunctio oppositorum (coincidence of opposites), 86 conscientiousness, 214, 215 ConsentAwareness.net, 130 control coercive, 187–188 question, 199 Converus, 202–203 corporations CEOs and psychopathy, 74 hypocrisy and, 106 with lies, 196 women and, 176–177 cowardice, fear and, 62 crime, 131–132, 135 coercive control as, 188 men and, 176 women and, 176–177 Crundwell, Rita, 171–172, 177 Cyr, Joseph, 72 Daniels, Stormy, 198 Dante Alighieri, 42, 105 “Dark Tetrad,” 73 Darville, Helen, 67–68 Darwin, Anne, 99–100 Darwin, John, 99–100 David, Larry, 139, 142 Dear Evan Hansen (play), 40 deaths, fake, 99–100 debt, in marriage, 31 Demara, Ferdinand Waldo, Jr., 71–73, 98 Demidenko, Helena, 67 Democratic National Convention (2008), 42 denial compartmentalization and, 149–150 of truth, 13, 128 DePaulo, Bella M., 59, 172–173, 195 depression, 124, 157 Derailed (Stapel), 34–35 Desai, Sonia, 123 detection, of lies language and, 193–195 methods for, 197, 202–203 polygraph, 198–202, 206 with unconscious mind, 204–205 Devine, Jack, 151 Diagnostic and Statistical Manual of Mental Disorders (DSM), 74, 124 Diana (Princess), 6 DiCaprio, Leonardo, 35 Dickens, Charles, 86 Dirks, Kurt T., 149 disorders attention deficit, 60 DSM, 74, 124 personality, 73, 74, 79, 123–124 PTSD, 123–126, 130, 184 disorientation, gaslighting and, 25, 119 Dolezal, Rachel, 68 domestic violence, 129, 130 Domingo, Plácido, 16 doppelgänger (double walker), 86 Dostoevsky, Fyodor, 86 double lives, 67–69 of Alvarez, 75–78, 80, 89 of the Commander, 1–10, 15–30, 92–93, 97–98, 115–121 compartmentalization and, 96–97, 142 of Demara, 71–73 escapism and, 63–64 families and, 108, 122–123, 126–130, 175–176 in marriage, 65–66, 126–130, 137–143, 163–171, 184–185, 209–210 double walker (doppelgänger), 86 The Double (Dostoevsky), 86 The Double Life of Charles A.


pages: 347 words: 97,721

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines by Thomas H. Davenport, Julia Kirby

"World Economic Forum" Davos, AI winter, Amazon Robotics, Andy Kessler, Apollo Guidance Computer, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, basic income, Baxter: Rethink Robotics, behavioural economics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, commoditize, conceptual framework, content marketing, dark matter, data science, David Brooks, deep learning, deliberate practice, deskilling, digital map, disruptive innovation, Douglas Engelbart, driverless car, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, financial engineering, fixed income, flying shuttle, follow your passion, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, game design, general-purpose programming language, global pandemic, Google Glasses, Hans Lippershey, haute cuisine, income inequality, independent contractor, index fund, industrial robot, information retrieval, intermodal, Internet of things, inventory management, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joi Ito, Khan Academy, Kiva Systems, knowledge worker, labor-force participation, lifelogging, longitudinal study, loss aversion, machine translation, Mark Zuckerberg, Narrative Science, natural language processing, Nick Bostrom, Norbert Wiener, nuclear winter, off-the-grid, pattern recognition, performance metric, Peter Thiel, precariat, quantitative trading / quantitative finance, Ray Kurzweil, Richard Feynman, risk tolerance, Robert Shiller, robo advisor, robotic process automation, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, six sigma, Skype, social intelligence, speech recognition, spinning jenny, statistical model, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, superintelligent machines, supply-chain management, tacit knowledge, tech worker, TED Talk, the long tail, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, Works Progress Administration, Zipcar

That’s why, when you look at the matrix in Figure 3.1 you see in the upper-left corner a cell that represents the intersection of “human support” and “analyzing numbers.” When we map the progression of smart machines across the decades, the story starts here. Chances are, if you are a decision-maker in a large organization, you have found yourself working with business intelligence software, data visualization tools, and hypothesis-driven analytics. In the overall pattern by which automation comes to knowledge work, such tools represent square one. Unfolding from that point to the squares farthest from it on the horizontal is the history and future of smart machines, moving from the least to the greatest of their increasingly unnerving capabilities.

For health insurance giant UnitedHealthcare, she began to step up—not just looking at individual insurance policies, but forecasting the impact of new technologies and macroeconomic conditions on health-care costs. At Humana, another large firm (recently acquired by Aetna), Tourville continued her focus on modeling external trends, but she also led the company’s focus on analytics and data-driven decision-making. She was responsible there for the Business Intelligence and Informatics Competency Center, and had responsibility for $50 million of analytics-oriented projects and systems. At her current employer, Anthem—the second-largest company in the U.S. health insurance industry—she’s still stepping in and stepping up. She’s responsible for the “Commercial Health Care Economics” function, analyzing medical expense trends and understanding their movements, as well as the systems that report on such topics.

Aaron Kechley at DataXu told us that improving reporting has been a big deal for his company for several years now. He said, “We learned the hard way that you can’t preconceive what people will want in a report—people want to process information in different ways.” As a result, DataXu has been investing heavily in the latest “business intelligence” tools—new metrics, custom dashboards, and tailored reports. In fact, one of DataXu’s primary offerings these days is a tool that actually shows marketers whether their digital ads are working or not. This approach, which goes beyond reporting, has been done for a while with traditional statistical analysis.


pages: 344 words: 96,020

Hacking Growth: How Today's Fastest-Growing Companies Drive Breakout Success by Sean Ellis, Morgan Brown

Airbnb, Amazon Web Services, barriers to entry, behavioural economics, Ben Horowitz, bounce rate, business intelligence, business process, content marketing, correlation does not imply causation, crowdsourcing, dark pattern, data science, DevOps, disruptive innovation, Elon Musk, game design, gamification, Google Glasses, growth hacking, Internet of things, inventory management, iterative process, Jeff Bezos, Khan Academy, Kickstarter, Lean Startup, Lyft, Mark Zuckerberg, market design, minimum viable product, multi-armed bandit, Network effects, Paul Graham, Peter Thiel, Ponzi scheme, recommendation engine, ride hailing / ride sharing, Salesforce, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley startup, Skype, Snapchat, software as a service, Steve Jobs, Steve Jurvetson, subscription business, TED Talk, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, working poor, Y Combinator, young professional

At early stage start-ups, avoiding these silos from the start is advised, but as a start-up grows, more traditional marketing groups can be established alongside a dedicated growth team. And at larger, established firms, teams can complement the existing product, marketing, engineering, and business intelligence groups, collaborating with them and helping to open up more effective communication across them. As Sean’s experience with Dropbox shows, the process can be implemented by even the smallest of teams, which for many start-ups, especially in the early growth phase, should be run by the founder and comprise the entire company.

For example, at Netflix, by examining the movies and shows that customers were watching, the company found that Kevin Spacey films and political drama series were both hugely popular with their customers. That insight gave the company confidence to green-light the development of House of Cards, which became not only a huge hit, but also a must-have experience for many subscribers.27 Similarly, at RJMetrics, a business intelligence company, the team found that users who edited a chart in the software during their free trial period were twice as likely to convert to paying customers as those who didn’t and that that number went up even higher when a trial user edited two charts. So what did RJMetrics do? They made the editing of a chart a key step in their new user orientation.28 PIVOTING TO THE UNEXPECTED These distinctive behaviors and preferences can be hard to uncover, in part because sometimes they are so unexpected; paradoxically, you often don’t know what you’re looking for until you find it.

All Amazon shoppers in effect see their own version of Amazon with a unique experience tailored to their preferences. Some recommendation engines, such as Amazon’s, as well as those deployed by Google and Netflix, are incredibly complex, but many are based on relatively simple math. As Colin Zima, the chief analytics officer at Looker, a business intelligence software, explains, it can be relatively easy to generate recommendations based on a simple formula called a Jaccard index, or Jaccard similarity coefficient, which determines how similar two products are to each other. This helps to recommend additional items that a customer might want to buy because the software has calculated that the items, when purchased, are often purchased together.


pages: 285 words: 58,517

The Network Imperative: How to Survive and Grow in the Age of Digital Business Models by Barry Libert, Megan Beck

active measures, Airbnb, Amazon Web Services, asset allocation, asset light, autonomous vehicles, big data - Walmart - Pop Tarts, business intelligence, call centre, Clayton Christensen, cloud computing, commoditize, crowdsourcing, data science, disintermediation, diversification, Douglas Engelbart, Douglas Engelbart, future of work, Google Glasses, Google X / Alphabet X, independent contractor, Infrastructure as a Service, intangible asset, Internet of things, invention of writing, inventory management, iterative process, Jeff Bezos, job satisfaction, John Zimmer (Lyft cofounder), Kevin Kelly, Kickstarter, Larry Ellison, late fees, Lyft, Mark Zuckerberg, Mary Meeker, Oculus Rift, pirate software, ride hailing / ride sharing, Salesforce, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, six sigma, software as a service, software patent, Steve Jobs, subscription business, systems thinking, TaskRabbit, Travis Kalanick, uber lyft, Wall-E, women in the workforce, Zipcar

Big data, for our purposes, is nothing more than large sets of information that can be analyzed to understand useful patterns, often, but not always, related to human behavior. Husband-and-wife team Roland Dickey (CEO) and Laura Dickey (CIO) run Dickey’s Barbecue Pit, with 514 restaurants across the United States. They wanted to bring big data to barbecue, so they partnered with an external business intelligence firm to provide and develop a custom solution they call Smoke Stack.1 Smoke Stack gathers and analyzes data from a range of sources, including point-of-sale systems, loyalty programs, customer surveys, and inventory systems, to provide a nearly real-time dashboard of sales and performance information.

It developed a strong omnichannel platform that includes real-time inventory; in-store pickup for online purchases; a mobile app that integrates payment, loyalty programs, and local store inventories; and lightning-fast delivery options. Macy’s has recently partnered with Li & Fung to explore retailing in China. L2, a research firm that delivers business intelligence related to digital technology, rates Macy’s as a “genius” in its Digital IQ Index.14 Start with What You’re Missing As you begin the journey toward a leadership team that represents the interests, passions, and expectations of your networks, we encourage you to think about the networks themselves.


pages: 58 words: 12,386

Big Data Glossary by Pete Warden

business intelligence, business logic, crowdsourcing, fault tolerance, functional programming, information retrieval, linked data, machine readable, natural language processing, recommendation engine, web application

It allows you to update the search index with much lower latency, has a more minimal REST/JSON-based interface and configuration options, and scales horizontally in a more seamless way. It doesn’t yet have the community or number of contributors of the more established project, though, and it is missing some of the broader features that Solr offers, so it’s worth evaluating both. Datameer Though it’s aimed at the well-known business intelligence market, Datameer is interesting because it uses Hadoop to power its processing. It offers a simplified programming environment for its operators to specify the kind of analysis they want, and then handles converting that into MapReduce jobs behind the scenes. It also has some user-friendly data importing tools, as well as visualization options.


The Data Journalism Handbook by Jonathan Gray, Lucy Chambers, Liliana Bounegru

Amazon Web Services, barriers to entry, bioinformatics, business intelligence, carbon footprint, citizen journalism, correlation does not imply causation, crowdsourcing, data science, David Heinemeier Hansson, eurozone crisis, fail fast, Firefox, Florence Nightingale: pie chart, game design, Google Earth, Hans Rosling, high-speed rail, information asymmetry, Internet Archive, John Snow's cholera map, Julian Assange, linked data, machine readable, moral hazard, MVC pattern, New Journalism, openstreetmap, Ronald Reagan, Ruby on Rails, Silicon Valley, social graph, Solyndra, SPARQL, text mining, Wayback Machine, web application, WikiLeaks

Many journalists seem to be unaware of the size of the revenue that is already generated through data collection, data analytics, and visualization. This is the business of information refinement. With data tools and technologies, it is increasingly possible to shed light on highly complex issues, be this international finance, debt, demography, education, and so on. The term “business intelligence” describes a variety of IT concepts that aim to provide a clear view on what is happening in commercial corporations. The big and profitable companies of our time, including McDonalds, Zara, and H&M, rely on constant data tracking to turn out a profit. And it works pretty well for them. What is changing right now is that the tools developed for this space are now becoming available for other domains, including the media.

— Brian Boyer, Chicago Tribune At La Nacion we use: Excel for cleaning, organizing and analyzing data; Google Spreadsheets for publishing and connecting with services such as Google Fusion Tables and the Junar Open Data Platform; Junar for sharing our data and embedding it in our articles and blog posts; Tableau Public for our interactive data visualizations; Qlikview, a very fast business intelligence tool to analyze and filter large datasets; NitroPDF for converting PDFs to text and Excel files; and Google Fusion Tables for map visualizations. — Angélica Peralta Ramos, La Nacion (Argentina) As a grassroots community without any technical bias, we at Transparency Hackers use a lot of different tools and programming languages.


pages: 382 words: 120,064

Bank 3.0: Why Banking Is No Longer Somewhere You Go but Something You Do by Brett King

3D printing, Abraham Maslow, additive manufacturing, Airbus A320, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apollo 11, Apollo 13, Apollo Guidance Computer, asset-backed security, augmented reality, barriers to entry, behavioural economics, bitcoin, bounce rate, business intelligence, business process, business process outsourcing, call centre, capital controls, citizen journalism, Clayton Christensen, cloud computing, credit crunch, crowdsourcing, disintermediation, en.wikipedia.org, fixed income, George Gilder, Google Glasses, high net worth, I think there is a world market for maybe five computers, Infrastructure as a Service, invention of the printing press, Jeff Bezos, jimmy wales, Kickstarter, London Interbank Offered Rate, low interest rates, M-Pesa, Mark Zuckerberg, mass affluent, Metcalfe’s law, microcredit, mobile money, more computing power than Apollo, Northern Rock, Occupy movement, operational security, optical character recognition, peer-to-peer, performance metric, Pingit, platform as a service, QR code, QWERTY keyboard, Ray Kurzweil, recommendation engine, RFID, risk tolerance, Robert Metcalfe, self-driving car, Skype, speech recognition, stem cell, telepresence, the long tail, Tim Cook: Apple, transaction costs, underbanked, US Airways Flight 1549, web application, world market for maybe five computers

Maslow, “A Theory of Human Motivation”, Psychological Review 50 (1943): 370–96. 17 Countrywide.com 18 Matt Coffin, “The next generation of mortgage lead generation”, LowerMyBills.com. Additional sources: Forrester Research Inc, Federal Trade Commission 19 “Online mortgage sites offer net gains”, Australasian Business Intelligence, 18 September 2006 20 Mortgagebot’s Benchmarks 2011 Report 21 Google Finance Australia 22 See http://en.wikipedia.org/wiki/Apple_II 23 The likes of Forbes have measured mass market adoption or critical mass by the benchmark of 25 per cent of the population for developed economies such as the United States, the United Kingdom, France, Germany, Australia, etc. or 100 million persons globally (See also http://photos1.blogger.com/blogger/4015/329/1600/technology_adoption_11.jpg).

As effectiveness has reduced, you increase the offer of the month to perhaps three or five different products that the staff member can choose from on the fly, depending on what he knows about the customer, but it is still hit and miss. You need to have intelligence built into the system in respect of the next best offer or the next best action for the customer. This requires the implementation of customer analytics, business intelligence and offering management solutions to take a range of offers each month, and match the right offer with the right client. It also requires each of the product teams to come up with a range of offers each month that can be triggered by key data points for specific customers. Most product teams are used to running just one acquisition-type campaign on a new product three or four times a year—and it takes a great deal of work with creative agencies and so forth to pull it off.

You have to think about this and provide relevant offers. So don’t pitch a Gold credit card to someone who already has a Platinum card. Don’t pitch term deposits to a customer who is already a Premier account holder with a managed fund. Don’t pitch retirement plans to a student, etc. For this you need business intelligence and segmentation that create compelling, targeted offers that appeal. There are some other issues with selling behind the login, though—issues that have come as a result of marketers picking up some bad habits along the way. Combating banner blindness In a fairly brilliant piece of early usability testing, Jan Benway and David Lane of Rice University discovered in 1998 that users were starting to filter out “advertising banners”.


pages: 615 words: 187,426

Chinese Spies: From Chairman Mao to Xi Jinping by Roger Faligot

active measures, Albert Einstein, anti-communist, autonomous vehicles, Ayatollah Khomeini, Berlin Wall, British Empire, business intelligence, Deng Xiaoping, disinformation, Donald Trump, Edward Snowden, fake news, Fall of the Berlin Wall, Great Leap Forward, housing crisis, illegal immigration, index card, information security, megacity, Mikhail Gorbachev, military-industrial complex, new economy, offshore financial centre, Pearl River Delta, Port of Oakland, RAND corporation, Ronald Reagan, Shenzhen special economic zone , Silicon Valley, South China Sea, special economic zone, stem cell, union organizing, young professional, éminence grise

Given these circumstances, it comes as no surprise to learn that Huawei has developed a gigantic business intelligence apparatus to unearth everything about its competitors, its potential markets and the research and development of other companies it is interested in acquiring. According to my information, this apparatus also works to the benefit of the state apparatus, including the PLA—in which Ren still serves as an officer in the reserves—and of course, unavoidably in China, the CCP. According to its own documents, this business intelligence system—Huawei TopEng-BI—depends on the internal and external flow of information and information in liaison with all its subsidiaries and the following networks: a real-time data warehouse, an online analysis process, data-mining, an AI system, and a geographical information system.

The notion of the “sea lamprey strategy” (ba mu man ji) comes from the fact that this slippery, greenish fish blends in with the seascape, clinging to the rocks, and then, having waited patiently to select its prey, closes in and latches on, siphoning off its blood through its multiple orifices. It is the perfect metaphor for Chinese espionage techniques. Huawei’s business intelligence The telecommunications empire Huawei Technologies was founded in 1987 by a former PLA officer, Ren Zhengfei, in the Shenzhen Special Economic Zone. It is an excellent example of a company that has mastered the “sea lamprey strategy”, and the perfect symbol of China profiting from and buying up the rest of the world.

It has unprecedented systems in place for analyzing millions of calls, clients, VIP customers, competitors, monitoring systems, automated reports on device use, customer profiles, and data to be exploited. Officially, all of this is used for marketing purposes, including breaking into new markets. But the reality is that Huawei’s business intelligence systems, a programme like no other—except for the American NSA—represent one of the world’s largest organizations dealing in technological intelligence. Britain, thanks to research undertaken at the Government Communications Headquarters (GCHQ), has best understood the threat posed by Huawei due to its technological penetration of Western telephone manufacturers including British Telecom and Orange.


Microchip: An Idea, Its Genesis, and the Revolution It Created by Jeffrey Zygmont

Albert Einstein, Bob Noyce, business intelligence, computer age, El Camino Real, Fairchild Semiconductor, invisible hand, popular electronics, side project, Silicon Valley, Silicon Valley startup, William Shockley: the traitorous eight

Yet even after it obtained ownership, the chip-maker haggled internally about what to do with the microprocessor. The rancor raged three months longer, until competitive cross-fertilization returned to settle the argument, traveling back from Dallas to Santa Clara. This time the stimulus wasn't a technical specification like the one Datapoint had spirited to TI. It was more generalized business intelligence, carried in the person of Ed Gelbach, who was hired away from Texas Instruments to serve as Intel's new marketing director in August 1971. The hire was a coup for Intel because TI was then the prime mover in semiconductors. Before Gelbach had left Dallas, TI had silently developed its own version of a programmable, single-chip computer.

He didn't want the idea to leak out into the wider appliance world. Essex engineers spent a lot of time calling on customers, kibitzing with kindred engineers who made washers, dryers, air conditioners, refrigerators, ranges, and microwaves. The visits kept them current so that Essex could create ever more effective relays and switches. But business intelligence seeped both ways, and Fosnough worried that if the rest of Essex R&D knew what he was up to, some loose-lipped 138 MICROCHIP staffer might say too much as he bent over plans and product drafts at an appliance company. Once an appliance engineer got wind of it, he'd mention it to every other control company he traded with.


pages: 233 words: 73,772

The Secret World of Oil by Ken Silverstein

business intelligence, clean water, corporate governance, corporate raider, Donald Trump, energy security, Exxon Valdez, failed state, financial engineering, Global Witness, Google Earth, John Deuss, offshore financial centre, oil shock, oil-for-food scandal, Oscar Wyatt, paper trading, rolodex, Ronald Reagan, vertical integration, WikiLeaks, Yom Kippur War

Gaddafi also counted on support from the Washington-based US-Libya Business Association, which was founded and funded by American oil companies. The group was headed by David Goldwyn, who worked at the Energy Department under Clinton and who, after retiring from government to run a consulting firm that provided “political and business intelligence” to industry, returned to public service as the State Department’s coordinator for international energy affairs during the Obama administration. A few others featured in this book include: • Former British prime minister Tony Blair, who netted about $150,000 for a twenty-minute speech in Azerbaijan, in which he said that President Ilham Aliyev was a leader with a “very positive and exciting vision for the future of the country.”

According to its federal tax filings, the association sought to “educate the public on the importance of US–Libya trade and investment, and facilitate the commercial and diplomatic dialogue between the two countries.” At least seven of its eight directors were registered lobbyists for oil companies. David Goldwyn—who had served at the Energy Department under Bill Clinton, and who then ran a consulting firm that provided “political and business intelligence” to oil companies—was hired to head the group. The USLBA spent over $1 million between 2006 and 2009, of which more than six hundred thousand dollars was used to pay Goldwyn’s firm. In February 2007, the USLBA brought together government officials from both countries at a gala dinner, held at the Charlie Palmer steakhouse, to honor a senior official of “The Great Socialist People’s Libyan Arab Jamahiriya.”


pages: 258 words: 79,503

The Genius Within: Unlocking Your Brain's Potential by David Adam

Albert Einstein, business intelligence, cognitive bias, CRISPR, Flynn Effect, Gregor Mendel, job automation, John Conway, knowledge economy, lateral thinking, Mark Zuckerberg, meta-analysis, placebo effect, randomized controlled trial, SimCity, Skype, Stephen Hawking, The Bell Curve by Richard Herrnstein and Charles Murray

Despite the theoretical attempt to pull the skills and abilities apart and spread the results across the population, the data puts them back into sticky clumps and hands them, fairly or not, more to some individuals than others. Still, the popularity of the idea of multiple intelligences – all shall have prizes! – has spawned a series of imitators, most of which, in scientific terms, are little more than fashionable labels. Entrepreneurs write and sell books on business intelligence and managerial intelligence. There is spiritual intelligence and existential intelligence and moral intelligence and sexual intelligence and leadership intelligence. There is people intelligence and cultural intelligence and narrative intelligence and creative intelligence. There is even a dark intelligence, made up of an unholy trinity of personality traits: narcissism, Machiavellianism and psychopathy.

This attitude is common and it feeds on all those fears of IQ, typically presented as an elitist establishment idea and a private members’ club that turns away people at the door. Rival intelligences, their inventors claim, are more inclusive, more open and – crucially – more malleable and changeable. For what use are ideas like business intelligence and sexual intelligence if they cannot be increased in exchange for the price of a book, DVD or conference ticket? Rivals to IQ also trade on the idea they are more relevant, they measure separate and different abilities, which, although they are called intelligences, are more useful to have than ‘intelligence’.


pages: 439 words: 79,447

The Finance Book: Understand the Numbers Even if You're Not a Finance Professional by Stuart Warner, Si Hussain

AOL-Time Warner, book value, business intelligence, business process, cloud computing, conceptual framework, corporate governance, Costa Concordia, credit crunch, currency risk, discounted cash flows, double entry bookkeeping, forward guidance, intangible asset, Kickstarter, low interest rates, market bubble, Northern Rock, peer-to-peer lending, price discrimination, Ralph Waldo Emerson, shareholder value, supply-chain management, time value of money

ERP has made the production of MAPs much quicker and more efficient. Alongside the growth in ERP, technological developments such as CRM (customer relationship management), cloud computing and social media have increased the amount of data available for businesses to analyse. This has led to the emergence of new management accounting practices, such as: business intelligence: the interpretation of raw data to explain performance; business analytics: insights into performance from a continuous, iterative and methodical exploration of data; and big data: computational analysis of very large data sets to reveal patterns, trends, and associations, such as customer behaviour and interactions.

abridged accounts, 2nd, 3rd accountants, 2nd, 3rd, 4th accounting computerised systems deadlines for depreciation equity group methods for opex and capex for prepayments and accruals for provisions and contingencies responsibilities standards, 2nd for stock accounts see also company accounts; financial accounts; management accounts abridged, 2nd, 3rd payable, 2nd receivable, 2nd statutory, 2nd value of audited accruals, 2nd accruals accounting accrued income, 2nd acid test ratio actual insolvency see cash flow insolvency administration adverse opinion amortisation, 2nd, 3rd angel investors, 2nd, 3rd annual yield AOL asset turnover (AT) asset-based finance, 2nd asset-based lending asset-based valuation, 2nd assets see also current assets; fixed assets in the balance sheet contingent indefinite (or infinite) life net, 2nd valuation, 2nd associate relationships audit and accruals accounting and the balance sheet committee, 2nd disclaimer and disclosure external financial, 2nd qualified report, 2nd requirements and revenue recognition and valuation value of average cost (AVCO), 2nd B&Q, 2nd BA bad debts, 2nd balance, trial balance sheet balance sheet insolvency banks, and audited accounts Beyond Budgeting Round Table (BBRT) BHS big data bonds borrowing costs, 2nd BP, 2nd budgeting, 2nd, 3rd business analytics business angels see angel investors business finance debt equity business intelligence business tax business valuation buyback agreements capex, 2nd capital see also equity in the balance sheet, 2nd, 3rd cost, 2nd, 3rd share venture working, 2nd, 3rd capital allowances, 2nd capital employed, 2nd capital expenditure see capex capital gain, 2nd, 3rd capital maintenance rules capital redemption reserve capital reserves, 2nd, 3rd Carcraft cash, 2nd cash accounting cash equivalents cash flow discounted, 2nd forecasting from operating activities cash flow insolvency cash flow statement cessation, and equity finance charges choice and accounting standards company accounts see also accounts; financial accounts; management accounts and accounting policies and accruals accounting audit report and the balance sheet and business valuation capital and reserves and the cash flow statement corporate governance debt finance debtors and creditors directors’ remuneration earnings per share and finance systems fixed assets gearing going concern disclosure goodwill and group accounting impairment and information in public domain and insolvency interest cover investment appraisal management accounts pack not published in no budgets in opex and capex prepayments and accruals pricing not in and profit and loss (P&L) profit planning not in profitability performance measures provisions and contingencies revaluation revenue recognition and sources of finance stock tax working capital computerised accounting systems confirmation statement, 2nd, 3rd consolidated accounts see group accounting constraint, budgeting constructive obligations contingencies contingent assets contracts long-term onerous contribution, 2nd contribution percentage of sales (CPS) ratio control and debt finance organisational ownership versus convertible bonds corporate governance, 2nd Corporate Voluntary Arrangement (CVA) corporation tax, 2nd, 3rd costs average, 2nd borrowing, 2nd capital, 2nd, 3rd classification debt finance depreciated replacement fixed, 2nd fixed assets versus net realisable value sales stock variable, 2nd cost-volume-profit analysis (CVP), 2nd covenants, 2nd CPS (contribution percentage of sales) credit control, 2nd, 3rd credit ratings creditors, 2nd crowd lending see peer-to-peer (P2P) lending ‘Crowdcube’ crowdfunding see equity crowdfunding cumulative preference shares current assets, 2nd see also assets CVA (Corporate Voluntary Arrangement) CVP (cost-volume-profit analysis), 2nd DCF (discounted cash flow), 2nd deadlines for business tax financial, 2nd debt bad, 2nd collection factoring, 2nd performance measures sale of and solvency debt finance, 2nd, 3rd, 4th debt-equity mix debtor and creditor days, 2nd, 3rd debtors deferred income, 2nd deferred tax, 2nd, 3rd demand yield see price customization depreciated replacement costs depreciation, 2nd, 3rd directors and corporate governance and going concern disclosure and insolvency personal details remuneration, 2nd responsibilities, 2nd and viability statement disclosure disclaimer, audit disclosure requirements, 2nd discounted cash flow (DCF), 2nd discounting prices and revenue recognition disposal, profit/loss on distributable profits dividend cover dividend policy dividend yield dormant businesses double taxation, 2nd downward revaluation draft financial accounts dynamic pricing see price customisation earned income earnings before interest, tax, depreciation and amortisation (EBITDA), 2nd earnings per share earnings statement see profit and loss (P&L) earnings/profit after tax (EAT/PAT) earnings/profit before interest and tax (EBIT/PBIT) earnings/profit before tax (EBT/PBT) EAT/PAT (earnings/profit after tax) eBay EBITDA (earnings before interest, tax, depreciation and amortisation), 2nd EBIT/PBIT (earnings/profit before interest and tax) EBT/PBT (earnings/profit before tax) effective tax rate employment, 2nd Enron enterprise resource planning (ERP) systems, 2nd, 3rd equity, 2nd, 3rd, 4th see also capital equity accounting equity crowdfunding, 2nd equity finance, 2nd ERP (enterprise resource planning) systems, 2nd, 3rd errors ethical pricing exceptional items executive directors expansion and equity finance expenditure, prepayments and accruals, 2nd expenses, 2nd external audit, 2nd Exxon Mobil fair value FAR (fixed asset register) FASB (Financial Accounting Standards Board) FIFO (first-in first-out) finance department in company accounts finance systems importance personnel, 2nd finance director finance personnel, 2nd finance sources in the balance sheet debt, 2nd equity finance systems Financial Accounting Standards Board (FASB) financial accounting team financial accounts see also accounts; company accounts budgeting and forecasting contrasted with management accounts draft investment appraisal profit planning profitable pricing reconciliation with management accounts financial deadlines, 2nd financial health business valuation debt and solvency insolvency and going concern risk investor ratios profitability performance measures working capital and liquidity management financial reporting standards (FRS) financial risk, 2nd, 3rd financial statements and insolvency key elements business tax capital and reserves debtors and creditors goodwill group accounting impairment prepayments and accruals provisions and contingencies revaluation revenue recognition stock tangible fixed assets and depreciation primary balance sheet cash flow statement profit and loss (P&L) requirement for financing activities, 2nd first-in-first-out (FIFO), 2nd fixed asset register (FAR) fixed assets see also assets amortisation, 2nd in the balance sheet cost of versus current assets depreciation impairment, 2nd revaluation fixed charge, 2nd fixed costs, 2nd fixed term loans floating charge forecasting, 2nd foreign currency translation reserve formal insolvency procedures forward ratios fraud, 2nd FRS (financial reporting standards) full statutory accounts Funding Circle GAAP (Generally Accepted Accounting Principles), 2nd gain see capital gain gearing, 2nd, 3rd, 4th Generally Accepted Accounting Principles (GAAP), 2nd going concern, 2nd, 3rd, 4th goods goodwill, 2nd, 3rd, 4th, 5th GPM (gross profit margin), 2nd Greggs auditor’s report balance sheet business valuation capital and reserves cash flow statement corporate governance debtors and creditors directors’ remuneration dividend policy dividends earnings per share finance systems Financial Accounts 2015 and supporting notes going concern disclosure group accounting impairment lack of debt, 2nd opex and capex profit and loss (P&L) profitability performance measures provisions revaluation revenue recognition, 2nd stock stock days tangible fixed assets tax gross profit, 2nd, 3rd gross profit margin (GPM), 2nd group accounting, 2nd growth Her Majesty’s Revenue and Customs (HMRC) IAS (International Accounting Standards) IASB (International Accounting Standards Board) IFRS (international accounting/financial reporting standards), 2nd impairment, 2nd, 3rd, 4th income, 2nd, 3rd, 4th income statement see profit and loss (P&L) income-based valuation method incorporation incremental budgeting, 2nd incurred expenses indefinite (or infinite) life assets, 2nd, 3rd individual shareholders information in the public domain initial public offering (IPO), 2nd insolvency, 2nd, 3rd, 4th institutional investors insurance intangible assets, 2nd interest, tax relief interest cover, 2nd interest rates Internal Rate of Return (IRR), 2nd, 3rd International Accounting Standards Board (IASB) International Accounting Standards (IAS) international accounting/financial reporting standards (IFRS), 2nd international taxation inventory see stock investing activities, 2nd investment appraisal definition, 2nd minimising versus maximising return on investor ratios investors, 2nd invoice factoring invoice finance IPO (initial public offering), 2nd IRR (Internal Rate of Return), 2nd, 3rd JIT (just in time), 2nd journals, 2nd just in time (JIT), 2nd Kingfisher, 2nd land large businesses audit disclosure requirements financial statements ledgers, debtor and creditor legal obligations of directors, 2nd, 3rd in financial statements of group companies for tax leverage, 2nd, 3rd liabilities in the balance sheet provisions and contingencies valuation limited liability liquidation liquidity liquidity ratio loans, fixed term long-term assets see fixed assets long-term contracts long-term liabilities losses, tax relief Majestic Wine management accounting team management accounts, 2nd, 3rd see also accounts; company accounts management accounts pack (MAP) margin of safety, 2nd margins, 2nd market to book ratio, 2nd mark-ups, 2nd maturity, and equity finance mergers and acquisitions micro businesses mixed costs, 2nd modifications, audit multiple obligations MySpace Naked Wines National Insurance Contributions (NIC), 2nd NBV (net book value), 2nd negative goodwill net assets, 2nd net book value (NBV), 2nd Net Present Value (NPV), 2nd, 3rd net realisable value (NRV) net working capital, 2nd Next NIC (National Insurance Contributions), 2nd nominal ledger, 2nd non-controlling interest (NCI), 2nd non-current assets see fixed assets non-current liabilities non-executive directors (NEDs) non-revenue producing activities, in profit and loss (P&L) NPV (Net Present Value), 2nd, 3rd NRV (net realisable value) obligations, provisions for onerous contracts operating activities, 2nd, 3rd operating expenditure see opex operating expenses operating profit, 2nd operating profit margin (OPM), 2nd, 3rd operating risk, 2nd opex, 2nd OPM (operating profit margin), 2nd, 3rd opportunity cost of money ordinary shareholders/shares, 2nd overdraft facilities overtrading owner’s equity see reserves ownership versus control and debt finance and equity finance P&L (profit and loss) P2P (peer-to-peer) lending, 2nd Panko, R.


pages: 458 words: 135,206

CTOs at Work by Scott Donaldson, Stanley Siegel, Gary Donaldson

Amazon Web Services, Andy Carvin, bioinformatics, business intelligence, business process, call centre, centre right, cloud computing, computer vision, connected car, crowdsourcing, data acquisition, distributed generation, do what you love, domain-specific language, functional programming, glass ceiling, Hacker News, hype cycle, Neil Armstrong, orbital mechanics / astrodynamics, pattern recognition, Pluto: dwarf planet, QR code, Richard Feynman, Ruby on Rails, Salesforce, shareholder value, Silicon Valley, Skype, smart grid, smart meter, software patent, systems thinking, thinkpad, web application, zero day, zero-sum game

It's now part of the enterprise, and if you don't have it, you'd better do it now because it cuts costs and makes IT very flexible. Another one is the ability to handle large data. A lot of companies are going to be facing issues regarding enormous amounts of data, how to present it, and how to create business intelligence solutions out of it. There are not a lot of standards yet how to handle these petabytes of data, how to access it in an optimal, cost effective way. There are some open source applications to manage big data, and some very well-known large companies are starting to support those standards.

Siegel: So, just to follow up on that. Are you guys in the role of establishing a data dictionary for all that or is that something you hand off to someone else? Mosca: We are involved with the data dictionary, master data management, all of the attributes as well. Yeah, it's ultimately going to be a large business intelligence initiative. Siegel: Okay. So you're hired in this case to get down to the blade-of-grass level. Mosca: Absolutely. G. Donaldson: Dmitry, what do you see as some of the important issues confronting your industry today from a technology perspective? Cherches: Security and hacking are going to become more of a daily normal event.

Cherches: Security and hacking are going to become more of a daily normal event. Unfortunately, many still believe that it's a lot easier to steal versus innovate. So, the CTO is responsible for making sure data is protected and mobile devices are not creating a threat. Being able to manage data and doing a lot of business intelligence from the data. That area is starting to become fairly standard. The technology is fairly affordable and if you don't have good KPI's (Key Performance Indicators), if you don't have good alerts that you can deliver to business stakeholders, you need to look into it ASAP. There's a big chance that a business executive wants to know when on this particular day some indicator is not right.


pages: 292 words: 85,151

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It) by Salim Ismail, Yuri van Geest

23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, anti-fragile, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, behavioural economics, Ben Horowitz, bike sharing, bioinformatics, bitcoin, Black Swan, blockchain, Blue Ocean Strategy, book value, Burning Man, business intelligence, business process, call centre, chief data officer, Chris Wanstrath, circular economy, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, commoditize, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, data science, Dean Kamen, deep learning, DeepMind, dematerialisation, discounted cash flows, disruptive innovation, distributed ledger, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fail fast, game design, gamification, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, holacracy, Hyperloop, industrial robot, Innovator's Dilemma, intangible asset, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Joi Ito, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, lifelogging, loose coupling, loss aversion, low earth orbit, Lyft, Marc Andreessen, Mark Zuckerberg, market design, Max Levchin, means of production, Michael Milken, minimum viable product, natural language processing, Netflix Prize, NetJets, Network effects, new economy, Oculus Rift, offshore financial centre, PageRank, pattern recognition, Paul Graham, paypal mafia, peer-to-peer, peer-to-peer model, Peter H. Diamandis: Planetary Resources, Peter Thiel, Planet Labs, prediction markets, profit motive, publish or perish, radical decentralization, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Ronald Coase, Rutger Bregman, Salesforce, Second Machine Age, self-driving car, sharing economy, Silicon Valley, skunkworks, Skype, smart contracts, Snapchat, social software, software is eating the world, SpaceShipOne, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Jurvetson, subscription business, supply-chain management, synthetic biology, TaskRabbit, TED Talk, telepresence, telepresence robot, the long tail, Tony Hsieh, transaction costs, Travis Kalanick, Tyler Cowen, Tyler Cowen: Great Stagnation, uber lyft, urban planning, Virgin Galactic, WikiLeaks, winner-take-all economy, X Prize, Y Combinator, zero-sum game

Dashboards Extending the notion that decision-making in companies should be driven by data rather than by intuition, Dashboards offer an intuitive way to present complex information in a simple and cogent way. John Seely Brown and John Hagel have observed that although all of our large organizations are set up to scale efficiencies, in this new economy what we actually need to scale is learning. And while some very good business intelligence (BI) systems exist out there, they are set up largely to measure scaling of efficiency. What is needed now are new dashboards that measure the learning capability of organizations. And if those learning dashboards don’t emerge soon, big companies should consider requiring that their newly minted chief data officers (the hottest new C-Level position) build them.

Key Opportunity Implications and Actions Externally driven IT Leverage external community (developers) and partnerships (startups, SaaS, companies) for new services/products and open platforms with open APIs (remix datasets, open source standards) and provide own metadata (access, remixing). Business intelligence (BI) Data management systems that use methodologies, processes, architectures and technologies to transform raw data into meaningful and useful business information (more effective strategic, tactical and operational insights and decision-making). A key heuristic: if you operate in a highly uncertain environment, make it simple (not too many variables); if you operate in a predictable environment, make it complex (use more variables to manage BI).


pages: 251 words: 80,831

Super Founders: What Data Reveals About Billion-Dollar Startups by Ali Tamaseb

"World Economic Forum" Davos, 23andMe, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Anne Wojcicki, asset light, barriers to entry, Ben Horowitz, Benchmark Capital, bitcoin, business intelligence, buy and hold, Chris Wanstrath, clean water, cloud computing, coronavirus, corporate governance, correlation does not imply causation, COVID-19, cryptocurrency, data science, discounted cash flows, diversified portfolio, Elon Musk, Fairchild Semiconductor, game design, General Magic , gig economy, high net worth, hiring and firing, index fund, Internet Archive, Jeff Bezos, John Zimmer (Lyft cofounder), Kickstarter, late fees, lockdown, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Max Levchin, Mitch Kapor, natural language processing, Network effects, nuclear winter, PageRank, PalmPilot, Parker Conrad, Paul Buchheit, Paul Graham, peer-to-peer lending, Peter Thiel, Planet Labs, power law, QR code, Recombinant DNA, remote working, ride hailing / ride sharing, robotic process automation, rolodex, Ruby on Rails, Salesforce, Sam Altman, Sand Hill Road, self-driving car, shareholder value, sharing economy, side hustle, side project, Silicon Valley, Silicon Valley startup, Skype, Snapchat, SoftBank, software as a service, software is eating the world, sovereign wealth fund, Startup school, Steve Jobs, Steve Wozniak, survivorship bias, TaskRabbit, telepresence, the payments system, TikTok, Tony Fadell, Tony Hsieh, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, web application, WeWork, work culture , Y Combinator

After my cousin was diagnosed, we started researching cancer. We didn’t really know anything about healthcare, let alone cancer, but we went all in. We started off looking at health insurance ideas. We started looking at second-opinion services for cancer patients; my cousin had been misdiagnosed twice. We considered a business-intelligence tool for cancer centers. We did over a year of ideation while we were employees at Google before we formally started Flatiron Health, in June 2012. I believe that if Flatiron had been our first company, it would have been a total failure. We needed the credibility of the Invite Media acquisition and of carrying the Google business card.

Within a few years of our selling Invite Media, it was a giant company revenue-wise, processing billions of dollars of ads. The company was a rocket ship, and it turned out we undersold. So from day one at Flatiron Health, we had big expectations and big hopes. But our expectations were dashed pretty quickly. We were a business-intelligence tool for cancer centers, doing analytics for private practices and hospitals, but it turns out hospitals and cancer centers don’t really have big budgets. You’re never going to build a business selling that. So we pivoted into giving away the software and tools for free to those centers, and in return using their data for commercialization and research purposes.


Service Design Patterns: Fundamental Design Solutions for SOAP/WSDL and RESTful Web Services by Robert Daigneau

Amazon Web Services, business intelligence, business logic, business process, continuous integration, create, read, update, delete, en.wikipedia.org, fault tolerance, loose coupling, machine readable, MITM: man-in-the-middle, MVC pattern, OSI model, pull request, RFC: Request For Comment, Ruby on Rails, software as a service, web application

This logic can be encapsulated within a Service Connector (168) that translates the client’s message to the canonical form, then sends the transformed message to the bus. Once the bus has received a message, it may Virtual Service A Client Virtual Service B Virtual Service C Service Registry Message Store Enterprise Service Bus Wire Tap Target Service A Target Service B Business Intelligence Applications Target Service C Figure 6.15 ESBs provide a layer of indirection that enables services to be added, upgraded, replaced, or deprecated while minimizing the impact on client applications. A Q UICK R EVIEW OF SOA I NFRASTRUCTURE P ATTERNS 223 use a Message Translator to convert the canonical message to the format defined in the service’s contract.

This removes the responsibility from the target service, and ensures that policies are consistently enforced throughout the company. Since the bus “sees” all of the messages carried between clients and services in real time, a Wire Tap [EIP] can be established to forward information from the bus to business intelligence applications. The Orchestration Engine SOA Infrastructure Patterns ESBs often route messages to services that connect to Orchestration Engines. These are centralized infrastructures that direct the activities of long-running or complex workflows (see Figure 6.17). The activities, or tasks, found in these workflows are often performed by services, though they need not be web services that use HTTP.


pages: 329 words: 95,309

Digital Bank: Strategies for Launching or Becoming a Digital Bank by Chris Skinner

algorithmic trading, AltaVista, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, bank run, Basel III, bitcoin, Bitcoin Ponzi scheme, business cycle, business intelligence, business process, business process outsourcing, buy and hold, call centre, cashless society, clean water, cloud computing, corporate social responsibility, credit crunch, cross-border payments, crowdsourcing, cryptocurrency, demand response, disintermediation, don't be evil, en.wikipedia.org, fault tolerance, fiat currency, financial innovation, gamification, Google Glasses, high net worth, informal economy, information security, Infrastructure as a Service, Internet of things, Jeff Bezos, Kevin Kelly, Kickstarter, M-Pesa, margin call, mass affluent, MITM: man-in-the-middle, mobile money, Mohammed Bouazizi, new economy, Northern Rock, Occupy movement, Pingit, platform as a service, Ponzi scheme, prediction markets, pre–internet, QR code, quantitative easing, ransomware, reserve currency, RFID, Salesforce, Satoshi Nakamoto, Silicon Valley, smart cities, social intelligence, software as a service, Steve Jobs, strong AI, Stuxnet, the long tail, trade route, unbanked and underbanked, underbanked, upwardly mobile, vertical integration, We are the 99%, web application, WikiLeaks, Y2K

This means that the way in which you guard against data failings from external attack is by having the obvious data protections: firewalls, secure sign-on, dual authentication with triangulation of access, real-time business events monitoring and so on. What I mean by this is that banks should be moving towards much improved real-time tracking and business intelligence about their information flows, and this will alert them to any security breach. After all, most banks know that they will be breached. In fact, they know they cannot stop a breach. It will happen. The real question then is how you deal with it and how fast. That’s the key. This is why complex event monitoring of business intelligence flows with real-time alerts is a key focal point. The ability for a bank to keep its finger on the pulse of every transaction across its global operations will be the key to protecting against internal and external threats.


pages: 320 words: 90,526

Squeezed: Why Our Families Can't Afford America by Alissa Quart

Affordable Care Act / Obamacare, Airbnb, Alvin Toffler, antiwork, Automated Insights, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, business intelligence, do what you love, Donald Trump, Downton Abbey, East Village, Elon Musk, emotional labour, full employment, future of work, gentrification, gig economy, glass ceiling, haute couture, income inequality, independent contractor, information security, Jaron Lanier, Jeremy Corbyn, job automation, late capitalism, Lyft, minimum wage unemployment, moral panic, new economy, nuclear winter, obamacare, peak TV, Ponzi scheme, post-work, precariat, price mechanism, rent control, rent stabilization, ride hailing / ride sharing, school choice, sharing economy, Sheryl Sandberg, Silicon Valley, Skype, Snapchat, stop buying avocado toast, surplus humans, TaskRabbit, tech worker, TED Talk, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, union organizing, universal basic income, upwardly mobile, wages for housework, WeWork, women in the workforce, work culture , working poor

She believed that “robotic” software services had lowered the document review wages for humans so that all that’s left is work in these kinds of precarious and underpaid sites. “Doc monkeys” are typically now earning just $17 to $20 an hour, while shouldering upward of $200,000 in student debt. They usually have law degrees. In a sample ad for such a job, a company called Business Intelligence Associates (BIA) offered $20 an hour to both recent law school grads and licensed attorneys for temporary work doing document review, trial prep, and support work. The ad claimed that the perks included a “great work-life balance.” However undesirable, jobs like these may now be the only ones available to recent law school graduates.

., 28–29 Branded: The Buying and Selling of Teenagers (Quart), 216 Brandeis University, 217 Bravo TV, 211–12 Breaking Bad (TV show), 208, 219 Breastfeeding, 19–20, 23, 27 Brightly Cleaning, 259–60 Britain hospital birth costs, 24 parental leave, 26 social mobility in, 112–13 Brodkin, Karen, 126 Brown, Tamara Mose, 119–20 Brown University, 184 Budig, Michelle, 17 Buery, Richard, 83, 254–56 Bureau of Labor Statistics (BLS), 68–69, 116, 169, 209–10, 235 Business Intelligence Associates (BIA), 233 California. See also San Francisco; Silicon Valley family leave, 278n California Nurses Association, 233–34 Campos, Paul, 105 Canada basic income project, 242, 256 day care, 80 parental leave, 26 Cardiff University, 90 Care.com, 159, 254 Career navigators, 165–68, 186–87 Careers, second acts.


pages: 398 words: 86,855

Bad Data Handbook by Q. Ethan McCallum

Amazon Mechanical Turk, asset allocation, barriers to entry, Benoit Mandelbrot, business intelligence, cellular automata, chief data officer, Chuck Templeton: OpenTable:, cloud computing, cognitive dissonance, combinatorial explosion, commoditize, conceptual framework, data science, database schema, DevOps, en.wikipedia.org, Firefox, Flash crash, functional programming, Gini coefficient, hype cycle, illegal immigration, iterative process, labor-force participation, loose coupling, machine readable, natural language processing, Netflix Prize, One Laptop per Child (OLPC), power law, quantitative trading / quantitative finance, recommendation engine, selection bias, sentiment analysis, SQL injection, statistical model, supply-chain management, survivorship bias, text mining, too big to fail, web application

Should I have been surprised by this? Absolutely not. What the government had done was something I had seen during the early days at OpenTable. We emphasized creating systems and focusing on the market rather than the technology; we raced ahead without giving the underlying architecture enough attention. In addition, business intelligence was still in its infancy. A good report would often just consist of output in a spreadsheet. At that point, data mining and predictive modeling were completely absent from the strategy. Then there was the perception component. Data that was of slightly questionable quality, or data that could possibly have contained any errors, could not be used.

We know what we have, and we’ve tried answering specific questions, but we’re stuck. Case study: A Fortune 500 technology company had a process that generated data based on day-to-day operations. Historically, their data warehousing team performed most analytics tasks, such as traditional Business Intelligence (BI). This team’s primary responsibility was to develop relational database-driven tools, though they had also been dabbling in less-conventional analytics projects. They decided to bring in a data scientist to help with the latter endeavor. The belief was that the data scientist would magically find a golden nugget hidden in their data, which they could easily translate into some results.


pages: 360 words: 96,275

PostgreSQL 9 Admin Cookbook: Over 80 Recipes to Help You Run an Efficient PostgreSQL 9. 0 Database by Simon Riggs, Hannu Krosing

business intelligence, business process, database schema, Debian, en.wikipedia.org, full text search, GnuPG, MITM: man-in-the-middle, Skype

Some are not because they aren't so much technical tasks but more just planning and understanding of your environment. You might also find time to consider the following things: ff ff 240 Data quality: Are the contents of the database accurate and meaningful? Could the data be enhanced? Business intelligence: Is the data being used for everything that can bring value to the organization? 10 Performance & Concurrency In this chapter, we will cover the following: ff Finding slow SQL statements ff Collecting regular statistics from pg_stat* views ff Finding what makes SQL slow ff Reducing the number of rows returned ff Simplifying complex SQL ff Speeding up queries without rewriting them ff Why queries do not use an index ff How do force a query to use an index ff Using optimistic locking ff Reporting performance problems Introduction Performance and concurrency are two problems that are often tightly coupled—when concurrency grows, performance usually degrades, in some cases a lot.

There are usually two main reasons for wanting to do this, and often those reasons are combined: ff 300 High availability: Reducing the chances of data unavailability by having multiple systems each holding a full copy of the data. Chapter 12 ff Data movement: Allowing data to be used by additional applications or workloads on additional hardware. Examples are Reference Data Management, where a single central server might provide information to many other applications, and also Business Intelligence/Reporting Systems. Of course, both of those topics are complex areas, and there are many architectures and possibilities for doing each of those. What we will talk about here is data movement, where there is no transformation of the data—we simply copy the data from one PostgreSQL database server to another.


pages: 419 words: 102,488

Chaos Engineering: System Resiliency in Practice by Casey Rosenthal, Nora Jones

Amazon Web Services, Asilomar, autonomous vehicles, barriers to entry, blockchain, business continuity plan, business intelligence, business logic, business process, cloud computing, cognitive load, complexity theory, continuous integration, cyber-physical system, database schema, DevOps, fail fast, fault tolerance, hindsight bias, human-factors engineering, information security, Kanban, Kubernetes, leftpad, linear programming, loose coupling, microservices, MITM: man-in-the-middle, no silver bullet, node package manager, operational security, OSI model, pull request, ransomware, risk tolerance, scientific management, Silicon Valley, six sigma, Skype, software as a service, statistical model, systems thinking, the scientific method, value engineering, WebSocket

Coinciding disaster tests with major holidays and significant cultural events (major sporting events, shopping holidays, etc.) should be avoided. Be wary of interfering with your business’ supporting monthly or quarterly processes, unless these are specifically what you are intending to test. Finance, accounting, and business intelligence reports may not be the first things that come to mind when you are considering impact beforehand, but you don’t want to be the person that took out the payroll system the night before payday. The common advice at Google is to “look both ways” before kicking off a DiRT test. Immediately before the test starts do your best to watch out for unplanned coincidental events that might impact your test, being especially wary of ongoing production issues that your test may exacerbate.

That process has legal implications and must not be affected by efforts or tooling like Chaos Engineering. On the other hand, with the rise of digital banks and neobanks,1 the way customers interact with their money is changing. Financial capabilities powered by blockchain, AI, machine learning, and business intelligence has exposed the need for highly robust and scalable systems that the cloud infrastructure provides. This drives an evolution in software development methodologies and the need to bake in the right engineering practices into their way of working. Just like automated deployments have improved feature velocity, and immutable infrastructure makes sure the deployed servers are never altered, the systems need to be continuously validated for reliability.


The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do by Erik J. Larson

AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, autonomous vehicles, Big Tech, Black Swan, Bletchley Park, Boeing 737 MAX, business intelligence, Charles Babbage, Claude Shannon: information theory, Computing Machinery and Intelligence, conceptual framework, correlation does not imply causation, data science, deep learning, DeepMind, driverless car, Elon Musk, Ernest Rutherford, Filter Bubble, Geoffrey Hinton, Georg Cantor, Higgs boson, hive mind, ImageNet competition, information retrieval, invention of the printing press, invention of the wheel, Isaac Newton, Jaron Lanier, Jeff Hawkins, John von Neumann, Kevin Kelly, Large Hadron Collider, Law of Accelerating Returns, Lewis Mumford, Loebner Prize, machine readable, machine translation, Nate Silver, natural language processing, Nick Bostrom, Norbert Wiener, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Peter Thiel, public intellectual, Ray Kurzweil, retrograde motion, self-driving car, semantic web, Silicon Valley, social intelligence, speech recognition, statistical model, Stephen Hawking, superintelligent machines, tacit knowledge, technological singularity, TED Talk, The Coming Technological Singularity, the long tail, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, theory of mind, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, Yochai Benkler

The a­ ctual term first appeared in print in 1997 in a scientific context—in a NASA paper describing challenges to visualizing data using existing computer graphics technology. It ­d idn’t catch on, however, ­u ntil it became popu­lar in the next de­cade as a catchall term for business and computing. The modern concept of big data appears to have surfaced first in business intelligence discussions, notably in a 2001 Gartner Group report on business intelligence challenges. The report highlighted “three Vs”—­volume, velocity, and variety—to describe features of large datasets that would become increasingly impor­tant as computational resources continued to get more power­f ul and cheaper. Yet the report did not actually use the term big data.5 Nevertheless, the term started appearing everywhere by the end of the 2000s, and by 2014 Forbes captured the hype and confusion with an article titled “12 Big Data Definitions: What’s Yours?”


Thinking with Data by Max Shron

business intelligence, Carmen Reinhart, confounding variable, correlation does not imply causation, data science, Growth in a Time of Debt, iterative process, Kenneth Rogoff, randomized controlled trial, Richard Feynman, statistical model, The Design of Experiments, the scientific method

People may behave rationally according to microeconomic models, but they might also have grudges that the models didn’t account for. Still, when the analogy holds, mathematical modeling is a very powerful way to reason. Special Arguments Every discipline has certain argument strategies that it shares with others. The special arguments of data science overlap with those of engineering, machine learning, business intelligence, and the rest of the mathematically inclined disciplines. There are patterns of argument that occur frequently in each of these disciplines that also pop up in settings where we are using data professionally. They can be mixed and matched in a variety of ways. Optimization, bounding cases, and cost/benefit analysis are three special arguments that deserve particular focus, but careful attention to any case study from data science or related disciplines will reveal many more.


PostgreSQL Administration Essentials by Hans-Jurgen Schonig

business intelligence, Debian, full text search

Strauch is a 20-year veteran of software consulting at companies such as IBM, Sears, Ernst & Young, and Kraft Foods. He has a Bachelor's degree in Business Administration and leverages his technical skills to improve the business' self-awareness. His interests include data gathering, management, and mining; maps and mapping; business intelligence; and application of data analysis for continuous improvement. He is currently focused on development of an end-to-end data management and mining at Enova International, a financial services company located in Chicago. In his spare time, he enjoys the performing arts, particularly music, and traveling with his wife, Marilyn.


pages: 364 words: 99,897

The Industries of the Future by Alec Ross

"World Economic Forum" Davos, 23andMe, 3D printing, Airbnb, Alan Greenspan, algorithmic bias, algorithmic trading, AltaVista, Anne Wojcicki, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, bioinformatics, bitcoin, Black Lives Matter, blockchain, Boston Dynamics, Brian Krebs, British Empire, business intelligence, call centre, carbon footprint, clean tech, cloud computing, collaborative consumption, connected car, corporate governance, Credit Default Swap, cryptocurrency, data science, David Brooks, DeepMind, Demis Hassabis, disintermediation, Dissolution of the Soviet Union, distributed ledger, driverless car, Edward Glaeser, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, fiat currency, future of work, General Motors Futurama, global supply chain, Google X / Alphabet X, Gregor Mendel, industrial robot, information security, Internet of things, invention of the printing press, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Joi Ito, Kevin Roose, Kickstarter, knowledge economy, knowledge worker, lifelogging, litecoin, low interest rates, M-Pesa, machine translation, Marc Andreessen, Mark Zuckerberg, Max Levchin, Mikhail Gorbachev, military-industrial complex, mobile money, money: store of value / unit of account / medium of exchange, Nelson Mandela, new economy, off-the-grid, offshore financial centre, open economy, Parag Khanna, paypal mafia, peer-to-peer, peer-to-peer lending, personalized medicine, Peter Thiel, precision agriculture, pre–internet, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rubik’s Cube, Satoshi Nakamoto, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, social graph, software as a service, special economic zone, supply-chain management, supply-chain management software, technoutopianism, TED Talk, The Future of Employment, Travis Kalanick, underbanked, unit 8200, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, work culture , Y Combinator, young professional

Whereas land was the raw material of the agricultural age and iron was the raw material of the industrial age, data is the raw material of the information age. The Internet has become an ocean of jumbled, chaotic information, but now there is a way to connect this information and draw actionable business intelligence from it. Big data is transitioning from a tool primarily for targeted advertising to an instrument with profound applications for diverse corporate sectors and for addressing chronic social problems. At the same time, the industries of the future will both be created within the current geopolitical structure and transform it.

We can now chew through enough data fast enough in a way that people can afford . . . and storage got cheap, so we can store lots of data . . . and then we can actually process it fast enough to make use of it.” Increases in data gathering and growth in computing power complement each other. The more data there is, the more investment there is in powerful computers and abundant storage to chew through the data and draw business intelligence from it. The more powerful computers are, the easier it is to gather large amounts of data and produce larger and more in-depth data sets. Big data is inherently contradictory. It is both intimate and expansive. It examines small facts and aggregates these finite facts into information that can be both comprehensive and personalized.


pages: 398 words: 111,333

The Einstein of Money: The Life and Timeless Financial Wisdom of Benjamin Graham by Joe Carlen

Abraham Maslow, Albert Einstein, asset allocation, Bernie Madoff, book value, Bretton Woods, business cycle, business intelligence, discounted cash flows, Eugene Fama: efficient market hypothesis, full employment, index card, index fund, intangible asset, invisible hand, Isaac Newton, John Bogle, laissez-faire capitalism, margin call, means of production, Norman Mailer, oil shock, post-industrial society, price anchoring, price stability, reserve currency, Robert Shiller, the scientific method, Vanguard fund, young professional

Graham's 1937 publication, The Interpretation of Financial Statements (cowritten with Spencer B. Meredith, then a security-analysis instructor at the New York Stock Exchange), addresses this issue (among others) item by item. According to its preface, the purpose of the book is to enable one to read the financial statements of a business “intelligently”52 so that one becomes “better equipped to gauge its future possibilities.”53 For example, when discussing the potentially misleading reporting of a company's intangible assets (i.e., nonphysical resources such as goodwill, intellectual property, etc.), Graham writes that “little if any weight should be given to the figures at which intangible assets appear on the balance sheet.”54 Instead, Graham counseled that “it is the earning power of these intangibles, rather than their balance sheet valuation, that really counts.”55 Similarly, Graham assails corporate reporting of property values (“the same misleading results which were obtained before the war by overstating property values are now sought by the opposite stratagem of understating these assets”56), the “book value” item on the balance sheet, meant to represent the value of all of the assets available for the security in question (“if the company were actually liquidated the value of the assets would most probably be much less than the book value [of the stock]”57), reported earnings figures (“look out for booby traps in the per-share [earnings] figures”58), and more.

Of course, understanding financial statements is integral to a successful application of Graham's basic investment principles, so such a book made eminent sense. While portions of Security Analysis pertain to this topic, Graham and Meredith's 1937 publication, The Interpretation of Financial Statements, addresses this topic exclusively. According to its preface, the purpose of the book is to enable one to read the financial statements of a business “intelligently” so that one is “better equipped to gauge its future possibilities.”30 The Interpretation of Financial Statements is packed with the discerning analytical wisdom associated with Graham. For example, when discussing the potentially misleading reporting of a company's intangible assets, Graham writes: “In general, it may be said that little if any weight should be given to the figures at which intangible assets appear on the balance sheet….


pages: 197 words: 35,256

NumPy Cookbook by Ivan Idris

business intelligence, cloud computing, computer vision, data science, Debian, en.wikipedia.org, Eratosthenes, mandelbrot fractal, p-value, power law, sorting algorithm, statistical model, transaction costs, web application

Rosario Acquisition Editor Usha Iyer Lead Technical Editor Ankita Shashi Technical Editors Merin Jose Rohit Rajgor Farhaan Shaikh Nitee Shetty Copy Editor Insiya Morbiwala Project Coordinator Vishal Bodwani Proofreader Clyde Jenkins Indexer Monica Ajmera Mehta Production Coordinators Arvindkumar Gupta Manu Joseph Cover Work Arvindkumar Gupta Manu Joseph About the Author Ivan Idris has an MSc in Experimental Physics. His graduation thesis had a strong emphasis on Applied Computer Science. After graduating, he worked for several companies as a Java Developer, Data Warehouse Developer, and QA Analyst. His main professional interests are business intelligence, big data, and cloud computing. He enjoys writing clean, testable code, and interesting technical articles. He is the author of NumPy 1.5 Beginner's Guide. You can find more information and a blog with a few NumPy examples at ivanidris.net. I would like to dedicate this book to my family and friends.


pages: 924 words: 198,159

Blackwater: The Rise of the World's Most Powerful Mercenary Army by Jeremy Scahill

"World Economic Forum" Davos, air freight, anti-communist, Berlin Wall, Bernie Sanders, business climate, business intelligence, centralized clearinghouse, collective bargaining, Columbine, facts on the ground, Fall of the Berlin Wall, independent contractor, Kickstarter, military-industrial complex, multilevel marketing, Naomi Klein, no-fly zone, operational security, private military company, Project for a New American Century, Robert Bork, Ronald Reagan, school choice, school vouchers, Seymour Hersh, stem cell, Timothy McVeigh, urban planning, vertical integration, zero-sum game

Pizarro also sold his services to defense and weapons companies—in both the United States and in Europe—seeking to break into Latin American markets. He would tell these companies, “Well, let’s say you pay me $10,000 a month times three months, I will provide you with enough information and enough business intelligence so your sales-people will know exactly which doors to knock, to which officers they’re supposed to address, and how and when and for how much and for how long.” Pizarro said he made enough money selling “business intelligence” that he decided in early 2003 to “step away from the company and enjoy the money, enjoy my free time.” Leaving the day-to-day operations of Red Tactica to his business partners, Pizarro began writing for a German magazine focused on military technology.

Eventually, Pizarro struck up a relationship with virtually every defense and military attaché from “friendly” Latin American nations and earned a reputation as a go-to guy for Latin American countries seeking to purchase specialized weapons systems from major defense companies. Pizarro hotly denied that he was an arms dealer and scoffed at the label. Instead, he said, he was selling “business intelligence” to Latin American officials he characterized as essentially paying him to do their jobs. “A military attaché by definition is a gift, is a reward, is a promotion, is a vacation in Washington. You’re not supposed to actually work,” Pizarro said. “That is in the Latino world. For us, if you’re a general and you get promoted to a senior general, you get a year of vacation, a paid vacation with your entire family in Washington, D.C.


Text Analytics With Python: A Practical Real-World Approach to Gaining Actionable Insights From Your Data by Dipanjan Sarkar

bioinformatics, business intelligence, business logic, computer vision, continuous integration, data science, deep learning, Dr. Strangelove, en.wikipedia.org, functional programming, general-purpose programming language, Guido van Rossum, information retrieval, Internet of things, invention of the printing press, iterative process, language acquisition, machine readable, machine translation, natural language processing, out of africa, performance metric, premature optimization, recommendation engine, self-driving car, semantic web, sentiment analysis, speech recognition, statistical model, text mining, Turing test, web application

Automated Text Classification Text Classification Blueprint Text Normalization Feature Extraction Bag of Words Model TF-IDF Model Advanced Word Vectorization Models Classification Algorithms Multinomial Naïve Bayes Support Vector Machines Evaluating Classification Models Building a Multi-Class Classification System Applications and Uses Summary Chapter 5:​ Text Summarization Text Summarization and Information Extraction Important Concepts Documents Text Normalization Feature Extraction Feature Matrix Singular Value Decomposition Text Normalization Feature Extraction Keyphrase Extraction Collocations Weighted Tag–Based Phrase Extraction Topic Modeling Latent Semantic Indexing Latent Dirichlet Allocation Non-negative Matrix Factorization Extracting Topics from Product Reviews Automated Document Summarization Latent Semantic Analysis TextRank Summarizing a Product Description Summary Chapter 6:​ Text Similarity and Clustering Important Concepts Information Retrieval (IR) Feature Engineering Similarity Measures Unsupervised Machine Learning Algorithms Text Normalization Feature Extraction Text Similarity Analyzing Term Similarity Hamming Distance Manhattan Distance Euclidean Distance Levenshtein Edit Distance Cosine Distance and Similarity Analyzing Document Similarity Cosine Similarity Hellinger-Bhattacharya Distance Okapi BM25 Ranking Document Clustering Clustering Greatest Movies of All Time K-means Clustering Affinity Propagation Ward’s Agglomerative Hierarchical Clustering Summary Chapter 7:​ Semantic and Sentiment Analysis Semantic Analysis Exploring WordNet Understanding Synsets Analyzing Lexical Semantic Relations Word Sense Disambiguation Named Entity Recognition Analyzing Semantic Representations Propositional Logic First Order Logic Sentiment Analysis Sentiment Analysis of IMDb Movie Reviews Setting Up Dependencies Preparing Datasets Supervised Machine Learning Technique Unsupervised Lexicon-based Techniques Comparing Model Performances Summary Index Contents at a Glance About the Author About the Technical Reviewer Acknowledgments Introduction Chapter 1:​ Natural Language Basics Chapter 2:​ Python Refresher Chapter 3:​ Processing and Understanding Text Chapter 4:​ Text Classification Chapter 5:​ Text Summarization Chapter 6:​ Text Similarity and Clustering Chapter 7:​ Semantic and Sentiment Analysis Index About the Author and About the Technical Reviewer About the Author Dipanjan Sarkar is a data scientist at Intel, the world’s largest silicon company, which is on a mission to make the world more connected and productive. He primarily works on analytics, business intelligence, application development, and building large-scale intelligent systems. He received his master’s degree in information technology from the International Institute of Information Technology, Bangalore, with a focus on data science and software engineering. He is also an avid supporter of self-learning, especially through massive open online courses, and holds a data science specialization from Johns Hopkins University on Coursera.

This involves using NLP, information retrieval, and machine learning techniques to parse unstructured text data into more structured forms and deriving patterns and insights from this data that would be helpful for the end user. Text analytics comprises a collection of machine learning, linguistic, and statistical techniques that are used to model and extract information from text primarily for analysis needs, including business intelligence, exploratory, descriptive, and predictive analysis. Here are some of the main techniques and operations in text analytics:. Text classification Text clustering Text summarization Sentiment analysis Entity extraction and recognition Similarity analysis and relation modeling Doing text analytics is sometimes a more involved process than normal statistical analysis or machine learning.


pages: 160 words: 45,516

Tomorrow's Lawyers: An Introduction to Your Future by Richard Susskind

business intelligence, business process, business process outsourcing, call centre, Clayton Christensen, cloud computing, commoditize, crowdsourcing, data science, disruptive innovation, global supply chain, information retrieval, invention of the wheel, power law, pre–internet, Ray Kurzweil, Silicon Valley, Skype, speech recognition, supply-chain management, telepresence, Watson beat the top human players on Jeopardy!

Online Dispute Resolution (ODR) When the process of actually resolving a legal dispute, especially the formulation of the solution, is entirely or largely conducted across the Internet, then we have some form of online dispute resolution (known in the trade as ODR—see Chapter 10 for more detail and some examples). For litigators whose work is premised on the conventional, court-based trial process, ODR, such as e-negotiation and e-mediation, is a challenge to the heart of their business. Intelligent Legal Search Emerging systems, if properly primed, are now able, in terms of precision and recall, to outperform paralegals and junior lawyers when reviewing and categorizing large bodies of documents. This is disruptive, not simply for law firms which have profited from employing human beings to wade through roomfuls of paperwork (whether on transactions or dispute-related projects), but also for legal process outsourcers who currently offer similar services.


Presentation Zen Design: Simple Design Principles and Techniques to Enhance Your Presentations by Garr Reynolds

Albert Einstein, barriers to entry, business intelligence, business process, cloud computing, cognitive load, Everything should be made as simple as possible, Hans Rosling, Kaizen: continuous improvement, Kickstarter, lateral thinking, off-the-grid, Paradox of Choice, Richard Feynman, Silicon Valley, TED Talk, women in the workforce, Yogi Berra

The numbers in this simple bar chart are easy to read quickly, but the numbers become harder to see when a picture graph is used to show the same information. (Images in slides from iStockphoto.com.) You can always recognize truth by its beauty and simplicity. —Richard Feynman, physicist Stephen Few’s Graph Design IQ Test Stephen Few is one of the leading authorities in the field of data visualization and business intelligence. Through his company, Perceptual Edge, he focuses on the effective analysis and presentation of quantitative business information. Stephen is a remarkable presenter and a highly sought after speaker, trainer, and consultant. He is the author of several books on data information visualization, including his latest best-seller Now You See It: Simple Visualization Techniques for Quantitative Analysis (Analytics Press, 2009).


pages: 172 words: 49,890

The Dhandho Investor: The Low-Risk Value Method to High Returns by Mohnish Pabrai

asset allocation, backtesting, beat the dealer, Black-Scholes formula, book value, business intelligence, call centre, cuban missile crisis, discounted cash flows, Edward Thorp, Exxon Valdez, fixed income, hiring and firing, index fund, inventory management, John Bogle, Mahatma Gandhi, merger arbitrage, passive investing, price mechanism, Silicon Valley, time value of money, transaction costs, two and twenty, zero-sum game

Fulbright, William Funeral service companies G Gates, Bill GEICO George, Abraham Geus, Arie de Gibran, Kahlil Goodwin, Leo Google Graham, Benjamin Greenblatt, Joel. See also Magic Formula; Value Investment Club Gujarat, India Guru Focus H Harazim, Tom I Indexes Information sources/business publications Innovation, see Copycat businesses Intelligent Investor, The (Graham) Intrinsic value: calculating company’s life expectancy and discount to distressed businesses and margin of safety and odds and investing and selling of stock and K Karmet Steel Works Kelly, John Larry Jr. Kelly Formula: Berkshire Hathaway’s Washington Post investment and few bets investing and investment selling and Knightsbridge Tankers Limited Kroc, Ray L Lampert, Eddie Level 3 Communications Little Book That Beats the Market, The (Greenblatt).


pages: 234 words: 57,267

Python Network Programming Cookbook by M. Omar Faruque Sarker

business intelligence, cloud computing, Debian, DevOps, Firefox, inflight wifi, machine readable, RFID, web application

Tom Stephens has worked in software development for nearly 10 years and is currently working in embedded development dealing with smartcards, cryptography, and RFID in the Denver metro area. His diverse background includes experience ranging from embedded virtual machines to web UX/UI design to enterprise Business Intelligence. He is most passionate about good software design, including intelligent testing and constantly evolving practices to produce a better product with minimal effort. Deepak Thukral is a polyglot who is also a contributor to various open source Python projects. He moved from India to Europe where he worked for various companies helping them scale their platforms with Python.


pages: 180 words: 55,805

The Price of Tomorrow: Why Deflation Is the Key to an Abundant Future by Jeff Booth

3D printing, Abraham Maslow, activist fund / activist shareholder / activist investor, additive manufacturing, AI winter, Airbnb, Albert Einstein, AlphaGo, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, Bretton Woods, business intelligence, butterfly effect, Charles Babbage, Claude Shannon: information theory, clean water, cloud computing, cognitive bias, collapse of Lehman Brothers, Computing Machinery and Intelligence, corporate raider, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, dark matter, deep learning, DeepMind, deliberate practice, digital twin, distributed ledger, Donald Trump, Elon Musk, fiat currency, Filter Bubble, financial engineering, full employment, future of work, game design, gamification, general purpose technology, Geoffrey Hinton, Gordon Gekko, Great Leap Forward, Hyman Minsky, hype cycle, income inequality, inflation targeting, information asymmetry, invention of movable type, Isaac Newton, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, John von Neumann, Joseph Schumpeter, late fees, low interest rates, Lyft, Maslow's hierarchy, Milgram experiment, Minsky moment, Modern Monetary Theory, moral hazard, Nelson Mandela, Network effects, Nick Bostrom, oil shock, OpenAI, pattern recognition, Ponzi scheme, quantitative easing, race to the bottom, ride hailing / ride sharing, self-driving car, software as a service, technoutopianism, TED Talk, the long tail, the scientific method, Thomas Bayes, Turing test, Uber and Lyft, uber lyft, universal basic income, winner-take-all economy, X Prize, zero-sum game

Virtual and augmented reality (mixed reality) will offer a different, more immersive connection with our technology, and it will change the way many things are done. Take, for example, a startup in Vancouver called LlamaZOO, which is in a new category of data collection called spatial data that is at the intersection of digital twinning (an exact twin of the physical world that is digital), mixed reality, and business intelligence. By twinning the real world via satellite imagery, drones, and lidar, and adding global positioning, mapping, and other data streams, the company uses mixed reality to reduce the cost of planning and work in the physical world. It allows for remote analyzing of massive amounts of data without traversing faraway sites with people.


pages: 525 words: 142,027

CIOs at Work by Ed Yourdon

8-hour work day, Apple's 1984 Super Bowl advert, business intelligence, business process, call centre, cloud computing, crowdsourcing, distributed generation, Donald Knuth, fail fast, Flash crash, Free Software Foundation, Googley, Grace Hopper, information security, Infrastructure as a Service, Innovator's Dilemma, inventory management, Julian Assange, knowledge worker, Mark Zuckerberg, Multics, Nicholas Carr, One Laptop per Child (OLPC), rolodex, Salesforce, shareholder value, Silicon Valley, six sigma, Skype, smart grid, smart meter, software as a service, Steve Ballmer, Steve Jobs, Steven Levy, the new new thing, the scientific method, WikiLeaks, Y2K, Zipcar

And a certain portion of our customer base really likes getting information that way, so I think there’s a great opportunity in that mobility space, especially to engage our customers in more two-way communication. The second area where there’s a huge opportunity for us is on data analytics. Business intelligence is going to be huge because we have so many sensors out on our network now that we can better predict when an outage will occur. We’ll never be able to predict them all because you don’t know where lightning’s going to hit. You don’t know when somebody’s going to run into a pole in their car.

., 87 Arizona Public Service (APS) Company, 66, 211, 223 Arizona State University, 227 ARPANET, 19, 117, 135 Art of Computer Programming, 2 Atlanta-based Southern Company, 191 AT&T, 191, 249 B Ballmer, Steve, 39 Bank of Boston, 47 Baylor-Grapevine Board of Trustees, 47 Bedrock foundation, 249 Bell Atlantic Mobile, 231 Bell Labs, 2, 249 BlackBerry, 60, 96, 116, 121, 171, 184, 246, 261, 296, 317 Blalock, Becky, 182, 191, 215 adaptability, 192 Air Force brat, 191, 192 Atlanta-based Southern Company, 191 banking industry, 203 Boucher, Marie, 196 brainstorm, 202 24/7 business, 199 business intelligence, 204 cloud computing, 205 cognitive surplus, 206 cognitive time, 206 Coker, Dave, 196 communication and education, 200 Community and Economic Development, 194 consumer market, 202 cybersecurity, 207, 209 data analytics, 204, 205 disaster recovery, 209 distributed generation, 204 distribution organization, 201 Egypt revolution, 198 farming technology, 206 finance backgrounds/marketing, 200, 209 Franklin, Alan, 193 Georgia Power, 191 Georgia Power Management Council, 193 global society, 206 Google, 198 incredible technology, 195 Industrial Age, 206 Information Age, 206 InformationWeek's, 196 infrastructure, 202 intellectual property, 196 intelligence and redundancy, 207 Internet, 198, 206 leapfrog innovations, 205 mainframe system, 207 marketing and customer service, 193, 200 MBA, finance, 192 microfiche, 207 microwave tower, 207 mobile devices, 203 mobility and business analytics, 205 Moore's Law, 205 new generation digital natives, 197 flexible and adaptable, 199 innovation and creativity, 199 superficial fashion, 198 Olympic sponsor, 193 out pushing technology, 202 reinforcement, 201 sense of integrity, 200 Southern Company, 194, 198, 201, 207 teamwork survey, 201 technology lab, 202 undergraduate degree, marketing, 192 virtualization, 205 VRU, 203 Ward, Eileen, 196 wire business, 201 world-class customer service, 203 Bohlen, Ken, 211 American Production Inventory Control Society, 211 Apple, 217 APS, 211, 223 ASU, 227 benchmarking company, 216 chief innovation officer, 229 Citrix, 217 cloud computing, 218, 219 cognitive surplus, 220 DECnet, 212 Department of Defense, 222 distributed computing, 217 energy industry, 214 gizmo/whiz-bang show, 216 GoodLink, 217 hard-line manufacturing, 218 home computing, 219 home entertainment, 219 Honeywell, 219 HR generalists, 215 information technology department, 211 Intel machines, 217 John Deere, 213 just say yes program, 223 Lean Six Sigma improvement process, 211 Linux, 220 MBA program, 214 mentors, 213 national alerts, 224 North American universities, 228 paradigm shifts, 218, 220 PDP minicomputers, 212 Peopleware, 226 prefigurative culture, 221 R&D companies, 218 Rhode Island, 226 role models, 213 San Diego Fire Department, 224 security/privacy issues, 217 skip levels, 223 smart home concepts, 219 smartphone, 217 social media, 225 Stead, Jerry, 214 Stevie Award, 211 Storefront engineering, 212 traditional management, 219, 226 Twitter, 224 vocabulary, 221 Waterloo operations, 213 Web 2.0 companies, 227 Web infrastructure, 215 wikipedia, 220 Y2K, 222 Botnets, 23 Brian's and Rob Pike's, 2 Bristol-Myers Squibb, 33 Broadband networks, 241 Brown, 227 Bryant, 227 BT Global Services, 253 BT Innovate & Design (BTI&D), 253 Bumblebee tuna, 130 C Career writing technology, 67 CASE tools, 232 Cash, Jim, 50 Christensen, Clyde, 212 Chrome, 14, 18 Chrysler Corporation, 175 Citibank, 337 Citicorp, 313 Citrix, 217 Client-server-type applications, 59 Cloud computing, 218, 219, 239, 240, 261, 262, 310, 311, 313 Cloud technology, 62 CNN, 54 COBOL, 250 Cognitive surplus, 20, 79, 206, 291 College of Engineering, University of Miami, 113 Columbia University, 1 Community and Economic Development, 194 Computer Sciences Corporation, 35 Computerworld magazine, 196 Consumer-oriented technology, 22 Content management system, 133 Corporate information management (CIM) program, 309 Corporate Management Information Systems, 87 Corvus disk drive, 36 Customer Advisory Boards of Oracle, 191 Customer-relationship management (CRM), 56 Cutter Business Technology Council, 173 D Dallas Children's Medical Center Development Board, 48 DARPA, 19 DDoS attacks and security, 81 DECnet, 212 Dell Platinum Council, 113 DeMarco, Tom, 16, 226 Department of Defense, 222, 329, 332 Detroit Energy, 252 Digital books, 30 Digital Equipment, 48 Distributed computing, 217 Dodge, 189 Dogfooding, 11, 37, 38, 236 DTE Energy, 173 DuPont Dow Elastomers, 151 E Educational Testing Service (ETS), 151 E-government, 282, 285 Electrical distribution grid, 182 Elementary and Secondary Education Strategic Business Unit, 151 Elements of Programming Style, 2 Ellyn, Lynne, 173 advanced technology software planning, 175 Amazon, 184 artificial intelligence group, 175 Association for Women in Computing, 173 benchmark, 180, 181 BlackBerries, 184 Burns, Ursula, 175 Chrysler, 176 Cisco, 186 cloud computing, 183, 184 component-based architecture, 186 corporate communications customer service, 185 Crain's Detroit Business, 173 cyber security threats, 177 degree of competence, 187 diversity and sophistication, 182 DTE Energy, 173 energy trading, 176 engineering and science programs, 188 enterprise business systems policy, 186 executive MBA program, 176 Facebook, 185 fresh-out-of-the-university, 187 General Electric, 174 Google, 184 Grace Hopper, 174 grid re-automation, 182 Henry Ford Hospital, 174 internal social media, 185 International Coaching Federation, 178 iPads, 184 IP electrical grids, 182 iPod applications, 182 IT budgets, 186 IT responsibilities, 176 Java, 186 level of sophistication, 179 lobbying efforts, 181 medical computing, 175 Miller, Joan, 174 Mulcahey, Anne, 175 Netscape, 175 neuroscience leadership, 189 object-oriented programming, 186 Oracle, 186 peer-level people, 179 people system, 177 policies and strategies, 180 Radio Shack, 180 remote access capacity, 189 security tool and patch, 183 sense of community, 180 Shipley, Jim, 174 smart grid, 177, 182 smart meters, 182 smart phone applications, 183 swarming, 179 technical competence, 178, 179 Thomas, Marlo, 174 Twitter, 185 UNITE, 181 vendor community, 186 virtualization, 183, 184 Xerox, 175 E-mail, 9 Employee-relationship management (ERM), 56 Encyclopedia, 115 Encyclopedia Britannica, 292 ERP, 123 F Facebook, 244 Ellyn, Lynne, 185 Sridhara, Mittu, 73, 84 Temares, Lewis, 116, 121, 131 Wakeman, Dan, 169 Federal information technology investments, 299 Flex, 236 Ford, 102 Ford, Monte, 47 agile computing, 59 agile development, 62, 66 airplanes, 51 American Airlines, 47 Arizona Public Services, 66 Bank of Boston, 47 Baylor-Grapevine Board of Trustees, 47 BlackBerry, 60 board of Chubb, 51 board of Tandy, 51 business organizations, 63 business school, dean, 50 career writing technology, 67 client-server-type applications, 59 cloud technology, 62 CNN, 54 common-sense functionality, 49 consumer-based technology, 60 CRM, 56 Dallas Children's Medical Center Development Board, 48 Digital Equipment, 48 ERM, 56 financial expert, 69 frequent-flier program, 57 frontal lobotomy, 57 Harvard Business Review, 50 HR policies, 65 IBM, 48 information technology, 47, 52 Internet, 54 Internet-based protocol, 59 iPhone, 52 IT stuff, 58 Knight Ridder, 51 legacy apps, 59 mainframe-like applications, 59 management training program, 64 marketing and technical jobs, 48 Maynard, Massachusetts mill, 48 MBA program, 50 mentors, 49 Microsoft, 50 mobile computing, 62 New York Times, 53 operations center, 54 PDP-5, 49 PDP-6, 49 Radio Shack, 51 revenue management, 57 role models, 49 security paradigms, 62 self-service machine, 57 Silicon Valley companies, 68 smartphones, 54 social networking, 51, 53, 56, 58 stateful applications, 59 techie department, 48 The Associates First Capital Corporation, 47 transmission and distribution companies, 47 wireless network, 59 YouTube, 65 Fort Worth, 226 Free software foundation, 19 Fried, Benjamin, 1, 241 agile development, 25 agile methodologies, 26 Apple Genius Bar, 8 ARPANET, 19 Art of Computer Programming, 2 Bell Labs, 2 books and records, accuracy, 25 botnets, 23 Brian's and Rob Pike's, 2 cash-like principles, 29 CFO, 4 check writers, 18 chrome, 14, 18 classic computer science text, 1 cognitive surplus, 20 Columbia University, 1 compensation management, 7 competitive advantage, 9, 18 computer science degree, 1 computer scientists, 6 consumer-driven computing, 12 consumer-driven software-as-a-service offerings, 12 consumer-driven technology, 12 consumer-oriented technology, 14, 22 corporate leadership, 25 cost centers, 4 DARPA, 19 decision makers, 17 decision making, 13 360-degree performance management, 7 detroit energy, 30 digital books, 30 document workbench, 2 dogfooding, 11 e-books, 29 Elements of Programming Style, 2 e-mail, 9 end-user support, 7 engineering executive group, 4 European vendors, 6 file servers and print servers, 17 Folger Library editions, 30 free software foundation, 19 German company, 13 German engineering, 13 Gmail, 15 Godot, 26 Google, 1 books, 29 products, 5, 10 software engineers, 6 hiring managers, 6 HR processes and technologies, 6 IBM model, 13 instant messaging, 9 Internet age, 6 interviewers, training, 6 iPad, 29 iPhone, 29 IPO, 3 IT, engineering and computer science parts, 4 Knuth's books, 2 Linux machine, 8 Linux software, 19 machine running Windows, 8 Macintosh, 8 Mac OS, 9 macro factors, 11 Managing Director, 1 mentors, 1 microcomputers, 18 Microsoft, 5 Minds for Sale, 20 Morgan Stanley, 1–3, 5, 16 nonacademic UNIX license, 2 nontechnical skills, 5 oil exploration office, 17 open-source phone operating system, 20 outlook, 15 PARC, 19 performance review cycles, 7 personal computer equipment, 15 post-Sarbanes-Oxley world, 25 project manager, 13 quants, 24 rapid-release cycle, 26 R&D cycle, 24 regression testing, 27 role models, 1 shrink-wrapped software, 14 signature-based anti-virus, 22 smartphone, 20, 27 social contract, 8 society trails technology, 21 software engineering tool, 13 software installation, 14 supply chain and inventory and asset management, 10 SVP, 4 telephony, 17 ten things, 13 TMRC, 19 TROFF, 2 typesetter workbench, 2 UI designer, 14 university computing center, 28 videoconferencing, 12 Visicalc, 24 Wall Street, 23 Walmart, 6 waterfall approach, 25 XYZ widget company, 5 YouTube video, 20 G Gates, Bill, 39, 50 General Electric, 134 General Foods, 309, 326–328 General Motors, 33, 321, 329, 332 George Mason School of Information Technology, 309 Georgia Power Company, 191–193, 196 Georgia Power Management Council, 193 German company, 13 German engineering, 13 German manufacturing company, 232 Gizmo/whiz-bang show, 216 Gmail, 15 GoodLink, 217 Google, 1, 84, 85, 117, 217, 219, 220, 222, 235, 241, 263, 302, 319 apps, 314 books, 29 commercial products, 10 model, 293 Government Accountability Office (GAO), 305 4G program, 250 4G smartphone, 235 GTE, 231 Gupta, Ashish aspiration, organization, 256 bandwidth and network infrastructure, 267 BlackBerry, 261 business and customer outcomes, 274 capital investment forums, 269 career progression, 255 cloud-based shared infrastructure model, 263 cloud computing, 261, 262 collaboration, 272 communications infrastructure, 258 compute-utility-based model, 262 control and integrity, 268 core competency, 255 core network infrastructure, 267 core strengths, 256 cost per unit of bandwidth, 267 customer demands, 268 data protection, 261, 262 decision-making bodies, 269 demographics, 272, 273 device convergence, 263 dogfooding, 259 employee flexibility, 260, 264 engagement and governance, 269 enterprise market segment, 261 equipment management, 260 executive MBA, 256 fourth-generation LTE networks, 267 functional service departments, 270 Global Services, distributed organization, 257 Google, 263, 275 Google Apps, 266 handheld devices, 265 hastily formed networks, 258 IMF, 266 innovation and application development, 265 iPad, 257, 260, 261, 266,267 iPhone, 266 Japan, 257, 258 London Business School, 253 management functions, 257 management sales functions, 257 market segments, 259 MBA, General Management, 253 measurements, 271 messaging with voice capability, 264 mini-microcomputer model, 261 mobile communications network, 258 mobile-enabling voice, 259 mobile phone network, 260 mobile traffic explosion, 265 network infrastructures, 265 network IT services, 254 network quality, 257 new generation digital natives, 271 disadvantages, 273 Google, 273 opportunities, 273 Olympics, 263 opportunities, 275 organizational construct, 272 outsourced network IT services, 259 outsourcing, 271 per-use-based model, 262 portfolio and business alignment, 274 Portfolio & Service Design (P&SD), 253 primary marketing thrust, 264 product development thrust, 264 product management team, 259 project and program management, 255 resource balance, 270 scalability, 262 security, 262 Selley, Clive, 254, 255 service delivery organization, 254 single-device model, 264 smart devices, 267 smart phones, 266 telecommunications capability, 259 upward-based apps, 264 virtualization, 261 voice-over-IP connections, 258 Windows platform, 261 Gurnani, Roger, 231 accounting/finance department, 233 analog cellular networks, 250 AT&T, 249 bedrock foundation, 249 Bell Atlantic Mobile, 231 Bell Labs, 249 blogs, 244 broadband networks, 241 business benefits, 237 business device, 240 business executives, 238 business leaders, 248, 249 business relationship management, 248 buzzword, 239 CASE tools, 232 cloud computing, 239, 240 COBOL, 250 consumer and business products, 231 consumer electronics devices, 241 consumer telecom business, 233 customer-engagement channel, 244 customer forums, 244 customer support operations, 251 customer-touching channels, 236 degree of control, 246 distribution channel, 250 dogfooding, 236 ecosystem, 243, 249 enterprise business, 233 ERP systems, 236 face-to-face communications, 244 FiOS product, 235 flex, 236 "follow the sun" model, 239 German manufacturing company, 232 4G program, 250 4G smartphone, 235 hardware/software vendors, 247 information assets, 245 information technology strategy, 231 intellectual property rights, 244 Internet, 235, 239 iPhone, 243 Ivan, 232 Lowell, 232 LTE technology-based smartphone, 235 marketing, 251 MIT, 246 mobile technology, 234 Moore's law, 242 MP3 file, 235 network-based services, 240 Nynex Mobile, 233 P&L responsibility, 251 PDA, 238 personal computing, 235 product development, 234, 251 role models, 232 sales channels, 251 smartphones, 238 state-level regulatory issues, 251 state-of-the-art networks, 243 telecom career, 232 telephone company, Phoenix, 234 Verizon Communication, 231, 232 virtual corporations, 241 Web 2.0, 244 Williams Companies, 232, 233 WillTell, 233 wireless business, 233 H Hackers, 19 Harmon, Jay, 213 Harvard Business Review, 50 Harvard Business School, 331 Heller, Martha, 171 Henry Ford Hospital, 174 Hewlett-Packard piece, 129 Home computing, 219 Honda, 102 Honeywell, 219 Houghton Mifflin, 134, 136 I IBM, 48, 250 manpower, 311 model, 13 Indian IT outsourcing company, 255 Information technology, 52 Intel machines, 217 International Coaching Federation, 178 Internet, 9, 44, 54, 117, 235, 239, 316, 322 Internet-based protocol, 59 Interoperability, 341 iPads, 2, 94, 97, 184, 257, 260, 264, 267, 288, 289, 295, 296 IP electrical grids, 182 iPhones, 43, 52, 96, 101, 170, 181, 260, 264,296 iPod, 101 IT lifecycle management process, 37 Ivan, 232 J John Deere, 213 K Kansas, 226 Kernigan, Brian, 2 Knight Ridder, 51 Knuth, Donald, 2, 29 Kraft Foods Inc, 309 Krist, Nicholas, 28 Kundra, Vivek Clever Commute, 305 cognitive surplus, 303 command and control systems, 301 consumerization, 302 consumption-based model, 300 cyber-warfare, 301 Darwinian pressure, 302 desktop core configuration, 306 digital-borne content, 301 digital oil, 300, 307 digital public square, 304 enterprise software, 303 entrepreneurial startup model, 306 frugal engineering, 306 Google, 302 government business, 302 innovator's dilemma, 307 iPad, 302 IT dashboard, 302 leapfrog technology, 306 massive consumerization, 301 megatrends, 301 parameter security, 302 Patent Office, 305 pharmaceutical industry, 304 phishing attacks, 301 policy and strategic planning, 299 security and privacy, 301 server utilization, 300 social media and technology, 300, 306 storage utilization, 300 Trademark Office, 305 Wikipedia, 303 L LAN, 259 Lean Six Sigma improvement process, 211 Levy, Steven (Hackers), 19 Linux, 220 machine, 8 open-source software, 19 Lister, Tim, 226 London Business School, 73, 253, 256 Long-term evolution (LTE), 235 Lowell, 232 M MacArthur's intelligence officer, 327 Macintosh, 8 Mainframe computers, 118 Mainframe-like applications, 59 Marriott's Great America, 35 McDade, 327 McGraw-Hill Education, 133, 147, 150 Mead, Margaret, 221 Mendel, 311 Microcomputers, 18 Microsoft Corporation, 5, 11, 33, 36, 38, 41, 44, 46, 50, 156, 217, 223, 236, 250, 293 Microsoft Higher Education Advisory Group, 113 Microsoft's operational enterprise risk management, 33 Middlesex University, 189 Miller, Joan Apple products, 295 authority and accuracy, 292 award-winning ICT programs and services, 277 back locked-down information, 289 big-scale text issues, 294 big-time computing, 279 BlackBerry, 296 business management training, 281 business skills, 281 central government, 283 cognitive surplus, 291 community care project, 278 community development programs, 277, 278 computers, constituency office, 294 confidential information, 284 data management, 281 decision making, 286 democratic process, 288 economics degree, 278 e-government, 282, 285 electronic communication, 289 electronic-enabled public voice, 286 electronic information, 288 electronic media, 286 electronic records, 280, 284 electronic services, 294 e-mail, 289, 290, 295 forgiving technology, 296 front-office service, 282, 283 Google, 292 Google's cloud service, 290 Government 2, 287 Health and Social Care, 284 House business, 294 House of Lords, 288 ICT strategy, 289, 290 information management, 278 insurance company, 278 Internet information, 285 iPad, 288, 289, 296 IT data management, 279 management principle, 280 local government, 283 mainframe environment, 289 member-led activity, 287 messages, 289 Microsoft, 293 Microsoft's cloud service, 290 mobile electronic information, 284 mobile technology, 289 national organization, 284 network perimeters, 290 official government information, 285 on-the-job training, 281 organizational planning, 278 Parliamentary ICT, 277 project management, 279 public sector, 282 public transportation, 285 quango-type organizations, 283 representational democracy, 286 security, 290, 291 social care organization, 279 social care services, Essex, 278 social care systems, 284 social networking, 285 sovereignty, 291 sustainability and growth, 293 technical language, 294 technology skills, 281 transactional services, 285 transferability, 291 Web-based services, 285 Wikipedia, 291, 292 X-factor, 286 Minds for Sale, 20 Mitchell & Co, 333 MIT Media Labs, 149 Mobile computing, 62 Mobile technology, 234 Mooney, Mark, 133 artificial intelligence, 134 back-office legacy, 136 balancing standpoint, 145 BBC, 140 Bermuda Triangle, 135 BlackBerry shop, 142 Bureau of National Standards, 136 business model, 140 career spectrum, 144 cloud computing, 148 competitive intelligence and knowledge, 143 Connect, 141 customer-facing and product development, 135 customer-facing product space, 137 customer space and product development, 136 digital products development, 144 digital space and product, 146 educational and reference content, 139 educational products, 141 entrepreneur, 150 General Electric, 134 GradeGuru, 140 handheld devices, 142 hard-core technical standpoint, 146 hardware servers, 142 Houghton Mifflin, 134, 136 HTML, 138 industrial-strength product, 141 intellectual content, 148 Internet, 148 iPad, 138, 139, 142 iPhone, 142, 143 iTunes, 138 Klein, Joel, 147 learning management systems, 137 long-term production system, 141 Marine Corps, 134 McGraw-Hill Education, 133, 147 media development, 144 media space, 138, 142 mobile computing, 139 MOUSE, 150 online technology, 138 open-source capabilities, 142 Oracle quota-management system, 143 people's roles and responsibilities, 137 Phoenix, 149 product development, 149 publishing companies, 142 publishing systems, 137 Reed Elsevier, 133, 136 Salesforce.com, 144, 149 scalability testing, 145 senior business leaders, 146 social network, 148 soft discipline guidelines, 141 solar energy, 149 Strassmann, Paul, 135 technical skill set, 143, 144 testing systems integration, 145 The Shallows, 139 transactional systems, 142 trust and integrity, 145 TTS, QuickPro, and ACL, 144 Vivendi Universal, 134 War and Peace today, 139 Moore's law, 242 Morgan Stanley, 2, 3, 16 N NASA, 309, 333, 334 National Institute of Standards and Technology (NIST), 173 Naval Postgraduate School, 134 Netscape, 175 New Brunswick model, 282 News Corp., 147 New York Stock Exchange (NYSE), 87, 116, 223, 278 New York Times, 53 North American universities, 228 NSA/CIA software, 134 Nynex Mobile, 233 O Oil exploration office, 17 Open-source phone operating system, 20 Outlook, 15 P Pacer Software, 135 Paradigm shifts, 218, 220 Parks and Recreation Department, 126 PDP minicomputers, 212 Peopleware, 226 Personal computing, 235 Personal digital assistant (PDA), 238 Petri dish, 44 Phoenix, 211 Plauger, Bill, 2 Q Quants, 24 R Radio Shack, 51 Reed Elsevier, 133, 136 Reed, John, 335 Rubinow, Steve, 87 AdKnowledge, Inc., 87 agile development, 110 Agile Manifesto, 110 Archipelago Holdings Inc., 87 attributes, 108 capital market community, 91 cash/actual trading business, 88 channel marketing departments, 92 cloud computing, 97 CNBC, 89 collaborative technology, 95 collective intelligence, 95 communication skills, 102, 106 conference organizations, 99 consumer marketplace, 94 data center, 90 decision making, 105, 108 economy standpoint, 100 e-mail, 100 Fidelity Investments, 105 financial services, 92 IEEE, 101 innovative impression, 94 Internet, 98 iPad, 97 iPod device, 91 labor laws, 110 listening skills, 106 logical progression, 104 Mac, 96 mainframe, 104 management and leadership, 104, 105 market data system, 89 micro-second response time, 89 mobile applications, 94 multidisciplinary approach, 103 multimedia, 97 multi-national projects, 110 multiprocessing options, 99 network operating system, 103 NYSE Euronext, 87 open outside system, 88 parallel programming models, 99 personal satisfaction, 109 PR function, 106 proclaimed workaholic, 109 real estate business, 88 regulatory and security standpoint, 96 Rolodex, 94 Rubin, Howard, 99 server department, 97 software development, 89 sophisticated technology, 101 technology business, 88 technology integration, 91 trading engines, 90 typewriter ribbon, 94 virtualization, 98 Windows 7, 96 younger generation video games, 93 visual interfaces, 93 Rumsfeld, Donald, 222 S San Diego Fire Department, 224 Santa Clara University, 36 SAS programs, 131 Scott, Tony, 10, 33, 236 Android, 43 Apple Computer, 35 architectural flaw, 44 BASIC and Pascal, 35 Bristol-Myers Squibb, 33 Bunch, Rick (role model), 34 business groups, 42 COO, 39 Corporate Vice President, 33 Corvus disk drive, 36 CSC, 35 Defense department, 45 dogfooding, 37, 38 games and arcades, 35 General Motors, 33 IBM's role, 37 information systems management, 36 integrity factor, 40 Internet, 44 iPhone, 43 IT lifecycle management process, 37 leadership capability, 40 leisure studies, 34 macro-architectural threats, 44 Marriott's Great America, 35 math models, 36 Microsoft Corporation, 33, 36, 38, 41, 44, 46 Microsoft's operational enterprise risk management, 33 parks and recreation, 34 Petri dish, 44 playground leader, 42 product groups, 42 quality and business excellence team, 33 Santa Clara University, 36 Senior Vice President, 33 smartphone, 43 social computing, 38 Sun Microsystems, 36 theme park industry, 35 University of Illinois, 34 University of San Francisco, 36 value-added business, 33 Walt Disney Company, 33 Senior Leadership Technology and Product Marketing, 71 Shakespeare, 30 Shirky, Clay, 220 Sierra Ventures, 191 Silicon Valley companies, 68 Silicon Valley software factories, 323 Skype, 118 Smart Grid Advisory Committee, 177 Smartphones, 20, 27, 43, 54, 217, 238 Social care computer electronic record system, 279 Social computing, 38, 320 Social networking, 51, 53, 56, 58 Society trails technology, 21 SPSS programs, 131 Sridhara, Mittu, 71 Amazon, 76 American Airlines, 72 back-end computation and presentation, 80 banking, 77 B2B and B2C, 85 business/product departments, 82 business work context, 74 buzzword, 77 career aspiration, 73 career spans, 73 coders, 72 cognitive surplus, 79 competitive differentiation, 74 computing power, 78 contribution and energy, 85 convergence, 75 CPU cycles, 78 cross-channel digital business, 71 cultural and geographic implementation, 72 customer experience, 84, 85 customer profile, 76 data visualization, 79, 80 DDoS protection, 81 economies of scale, 77 elements of technology, 72 encryption, 82 end customer, 83 entertainment, 75 ERP system, 72 Facebook, 84 finance and accounting, 73 foster innovation and open culture, 81 friends/mentors/role models, 74 FSA, 76 gambling acts, 81 games, 79 gaming machines, 80 GDS, 72 global organization, 71 Google, 75, 84, 85 Group CIO, Ladbrokes PLC, 71 industry-standard technologies, 77 integrity and competence, 83 IT, 74, 82 KickOff app, 71 land-based casinos, 79 live streaming, 78 London Business School, 73 mobile computing, 78 multimedia, 84 new generation, 84 on-the-job training, 73 open-source computing, 79 opportunity, 80, 83 PCA-compliant, 81 personalization, 76 real-time systems, 74 re-evaluation, 81 reliability and availability, 77 security threats, 80 smart mobile device, 75 technology-intense customer, 85 top-line revenue, 74 trader apps, 82 true context, 73 underpinning business process, 76 virtualization, 78 Visa/MasterCard transactions, 78 Web 3.0 business, 76 web-emerging web channel, 76 Wikipedia, 79, 85 Word documents and e-mail, 82 work-life balance, 84 young body with high miles, 72 Zuckerberg, Mark, 73 Stead, Jerry, 214 Storefront engineering, 212 Strassmann, Paul, 228, 309 agile development, 340 Amazon EC2, 314 America information processors, 322 Annapolis, 340 AT&T, 332 backstabbing culture, 339 BlackBerry, 317 block houses, 319 CFO/CEO position, 337 CIM program, 309 Citibank, 337 Citicorp, 313, 339 cloud computing, 310, 311, 313 coding infrastructure, 341 communication infrastructure, 341 corporate information management, 329 Corporate Information Officer, 309 counterintelligence, 320 cyber-operations, 338 Dell server, 314 Department of Defense, 329, 332 Director of Defense Information, 309 employee-owned technology, 316 enterprise architecture, 316 exfiltration, 313 financial organizations, 320 firewalls and antiviruses, 312 General Foods, 309, 326–328 General Motors, 321, 329, 332 George Mason School of Information Technology, 309 Google apps, 314 government-supported activities, 326 Harvard Business School, 331 HR-related issues, 331 IBM manpower, 311 infiltration, 313 Internet, 316, 322 interoperability, 315, 317, 341 Kraft Foods Inc, 309 MacArthur's intelligence officer, 327 Machiavellian view, 327 mash-up, 316 military service, 331 NASA, 309, 333, 334 police department, economics, 312 powerpoint slides, 324 Radio Shack, 319 senior executive position, 334 service-oriented architecture, 316 Silicon Valley software factories, 323 social computing, 320 Strassmann's concentration camp, 318 structured methodologies, 342 U.S.


pages: 496 words: 154,363

I'm Feeling Lucky: The Confessions of Google Employee Number 59 by Douglas Edwards

"World Economic Forum" Davos, Albert Einstein, AltaVista, Any sufficiently advanced technology is indistinguishable from magic, AOL-Time Warner, barriers to entry, book scanning, Build a better mousetrap, Burning Man, business intelligence, call centre, commoditize, crowdsourcing, don't be evil, Dutch auction, Elon Musk, fault tolerance, Googley, gravity well, invisible hand, Jeff Bezos, job-hopping, John Markoff, Kickstarter, machine translation, Marc Andreessen, Menlo Park, microcredit, music of the spheres, Network effects, PageRank, PalmPilot, performance metric, pets.com, Ralph Nader, risk tolerance, second-price auction, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley startup, slashdot, stem cell, Superbowl ad, Susan Wojcicki, tech worker, The Turner Diaries, Y2K

There were other glimpses of Larry's thinking. He and Eric shared a list of possible strategies that included Google as the publisher of all content, where users would pay us and we would reimburse the creators of everything from books to movies to music. Google as a provider of market research and business intelligence based on what we knew about the world. Google as an infrastructure platform and communications provider tying email and web data together. Google as the leader in machine intelligence backed by all the world's data and massive computing power that learned as it went along. He had no small plans.

Alan understood exactly what was at stake, and he knew the opposition. Overture had been born as GoTo in Pasadena, right down the street from the Jet Propulsion Laboratory. Even though they drew talent from Cal Tech and were plenty smart, they weren't Silicon Valley smart. They weren't really a tech company, and they didn't have the business intelligence Alan saw at Google. On the other hand, they had come up with the idea of marrying ads with search and implemented it before Google had. When Omid called the AOL business-development unit run by David Colburn, AOL agreed to start talks with Google. Not because they intended to give us their business, but because they could use Google as a bludgeon to beat a better deal out of Overture when their contract came up for renewal.


pages: 292 words: 62,575

97 Things Every Programmer Should Know by Kevlin Henney

A Pattern Language, active measures, Apollo 11, business intelligence, business logic, commoditize, continuous integration, crowdsourcing, database schema, deliberate practice, domain-specific language, don't repeat yourself, Donald Knuth, fixed income, functional programming, general-purpose programming language, Grace Hopper, index card, inventory management, job satisfaction, level 1 cache, loose coupling, machine readable, Silicon Valley, sorting algorithm, The Wisdom of Crowds

Upon graduating from Edinburgh University in 1987, he worked on the REKURSIV project before becoming a freelance contractor. Today, his primary software interests are agile practices and the resuscitation of legacy code. Chapter 1 Steve Berczuk Steve Berczuk is a software engineer at Humedica, where he develops business intelligence solutions for the healthcare industry. He has been developing software applications for over 20 years, and is the author of Software Configuration Management Patterns: Effective Teamwork, Practical Integration (Addison-Wesley Professional). In addition to developing software, he enjoys helping teams deliver more effectively through the use of agile methods and software configuration management.


pages: 693 words: 169,849

The Aristocracy of Talent: How Meritocracy Made the Modern World by Adrian Wooldridge

"World Economic Forum" Davos, Ada Lovelace, affirmative action, Alan Greenspan, Albert Einstein, assortative mating, barriers to entry, Bernie Sanders, Black Lives Matter, Bletchley Park, borderless world, Boris Johnson, Brexit referendum, business intelligence, central bank independence, circulation of elites, Clayton Christensen, cognitive bias, Corn Laws, coronavirus, corporate governance, correlation coefficient, COVID-19, creative destruction, critical race theory, David Brooks, Dominic Cummings, Donald Trump, Double Irish / Dutch Sandwich, Etonian, European colonialism, fake news, feminist movement, George Floyd, George Gilder, Gini coefficient, glass ceiling, helicopter parent, Home mortgage interest deduction, income inequality, intangible asset, invention of gunpowder, invention of the printing press, Isaac Newton, Jeff Bezos, Jeremy Corbyn, Jim Simons, joint-stock company, Joseph Schumpeter, knowledge economy, knowledge worker, land tenure, London Interbank Offered Rate, Long Term Capital Management, Louis Pasteur, Mahatma Gandhi, Mark Zuckerberg, means of production, meritocracy, meta-analysis, microaggression, mortgage tax deduction, Myron Scholes, offshore financial centre, opioid epidemic / opioid crisis, Panopticon Jeremy Bentham, Peter Thiel, plutocrats, post-industrial society, post-oil, pre–internet, public intellectual, publish or perish, Ralph Waldo Emerson, RAND corporation, rent-seeking, Richard Florida, Ronald Reagan, scientific management, sexual politics, shareholder value, Sheryl Sandberg, Silicon Valley, spinning jenny, Steve Bannon, Steven Pinker, supply-chain management, surveillance capitalism, tech bro, The Bell Curve by Richard Herrnstein and Charles Murray, The Wealth of Nations by Adam Smith, Thorstein Veblen, three-martini lunch, Tim Cook: Apple, transfer pricing, Tyler Cowen, unit 8200, upwardly mobile, Vilfredo Pareto, W. E. B. Du Bois, wealth creators, women in the workforce

The large group of researchers who flooded into academia after the Second World War were professionals who measured their lives in terms of Ph.D.s earned and articles accepted. Yet even as academics became more obsessed with producing professional articles, universities became more important in controlling entry into the professional life, including business life. BUSINESS INTELLIGENCE In Chapter Ten we noted that American business played a significant part in nurturing the meritocratic spirit. Giant businesses such as the railroads required specialized middle-managers to keep the trains running on time, and great universities such as the University of Pennsylvania and Harvard established business schools to produce them.

W. 319 Bush, George P. 319 Bush, George W. 316, 318, 319, 331–2 business and academic qualifications 340 American corporations 197, 252, 253 American robber barons 17, 193, 196–7 consultancies 253, 322, 352 corporate disasters and scandals 337–8 family 370 and global elite 321–3 meritocratic companies 2, 3 public 370 regulators 343 screening for class privilege 318 women in senior positions 273, 275 business intelligence 252–5 business schools 308 America 253–4 business theory 254–5 Butler, Nicholas Murray 197–8 Butler, R. A. B. 235 Butler, Samuel 152 Byron, Lord 142 C. H. Stoelting Company 214 California, University of 239, 251, 303 Callixtus III, Pope (Alfonso de Borgia) 47 Calvin, John 111 Calvinism, and ambition 34 Cambridge, Prince George, Duke of 160 Cambridge University 32 and elite schools 8, 307 examinations 149–51 ‘gentleman commoners’ 231 Girton College 268, 270–71 grammar-school students 246 King’s College 103 Newnham College 268, 269–70 Royal Commission (1850) 159 and school examinations 161 women admitted to men’s colleges 275 Cameralists, Austria 122–3 Cameron, David 2, 3, 48, 392 Campbell, James 69 Cao Xueqin, The Dream of the Red Chamber 83 Caplan, Gordon 315 ‘captains of learning’ (US) 223–7 Cardwell, Edward 160 Carlson, Tucker 6, 334 Carlyle, Thomas 127 on literary genius 141–2 Sartor Resartus 173 Carmichael, Stokely, Black Power 298 Carnegie, Andrew 193, 196, 230 Carnegie Foundation 254 Catherine of Braganza, wife of Charles II 41 Catherine the Great, of Russia 43, 123–4 and aristocracy 129 Catholic Church, Voltaire on 119 central banks 385 chain of being, notion of 28, 123 Challand, Albert 206 Chamberlain, Houston Stewart 87 Chamberlain, Neville 394 Charlemagne 26 and Jews 93 and succession 42 Charles the Bald, king of France 42 Charles Frederick, Grand Duke of Baden 124 Charles II, king of England 41, 52, 144 and patronage 55 Charles II, king of Spain 46 Charles V, emperor 45, 110 Charles VI, king of France 40–41 Charterhouse School 104 Chatham House 343 Chauncey, Henry 224, 226–7 Chauvin, Derek 346 chess grandmasters 86 Chesterton, G.


pages: 250 words: 64,011

Everydata: The Misinformation Hidden in the Little Data You Consume Every Day by John H. Johnson

Affordable Care Act / Obamacare, autism spectrum disorder, Black Swan, business intelligence, Carmen Reinhart, cognitive bias, correlation does not imply causation, Daniel Kahneman / Amos Tversky, data science, Donald Trump, en.wikipedia.org, Kenneth Rogoff, labor-force participation, lake wobegon effect, Long Term Capital Management, Mercator projection, Mercator projection distort size, especially Greenland and Africa, meta-analysis, Nate Silver, obamacare, p-value, PageRank, pattern recognition, publication bias, QR code, randomized controlled trial, risk-adjusted returns, Ronald Reagan, selection bias, statistical model, The Signal and the Noise by Nate Silver, Thomas Bayes, Tim Cook: Apple, wikimedia commons, Yogi Berra

In his litigation work, he guides companies and outside counsel on the appropriate use and interpretation of complex data sets, and has served as an expert witness in some of today’s most high-stakes corporate lawsuits. On the business analytics side, Dr. Johnson helps companies translate their complex internal data sets into strategic, actionable information across a variety of business settings including human resources, finance, marketing, manufacturing, and business intelligence. Both aspects share the need to understand—and properly apply—large, complex sets of data. He applies this same skill to his writing and speaking, where he helps audiences avoid common pitfalls people make when confronted with data, so they can become more confident and discerning consumers of data and make better decisions in their professional and personal lives.


pages: 681 words: 64,159

Numpy Beginner's Guide - Third Edition by Ivan Idris

algorithmic trading, business intelligence, Conway's Game of Life, correlation coefficient, data science, Debian, discrete time, en.wikipedia.org, functional programming, general-purpose programming language, Khan Academy, p-value, random walk, reversible computing, time value of money

Shrawane Copy Editors Charlote Carneiro Vikrant Phadke Sameen Siddiqui About the Author Ivan Idris has an MSc in experimental physics. His graduaton thesis had a strong emphasis on applied computer science. Afer graduatng, he worked for several companies as a Java developer, data warehouse developer, and QA Analyst. His main professional interests are business intelligence, big data, and cloud computng. Ivan enjoys writng clean, testable code and interestng technical artcles. He is the author of NumPy Beginner's Guide , NumPy Cookbook , Learning NumPy Array , and Python Data Analysis . You can fnd more informaton about him and a blog with a few examples of NumPy at http://ivanidris.net/ wordpress/ .


pages: 244 words: 66,977

Subscribed: Why the Subscription Model Will Be Your Company's Future - and What to Do About It by Tien Tzuo, Gabe Weisert

3D printing, Airbnb, airport security, Amazon Web Services, augmented reality, autonomous vehicles, Big Tech, bike sharing, blockchain, Brexit referendum, Build a better mousetrap, business cycle, business intelligence, business process, call centre, cloud computing, cognitive dissonance, connected car, data science, death of newspapers, digital nomad, digital rights, digital twin, double entry bookkeeping, Elon Musk, factory automation, fake news, fiat currency, Ford Model T, fulfillment center, growth hacking, hockey-stick growth, Internet of things, inventory management, iterative process, Jeff Bezos, John Zimmer (Lyft cofounder), Kevin Kelly, Lean Startup, Lyft, manufacturing employment, Marc Benioff, Mary Meeker, megaproject, minimum viable product, natural language processing, Network effects, Nicholas Carr, nuclear winter, pets.com, planned obsolescence, pneumatic tube, profit maximization, race to the bottom, ride hailing / ride sharing, Salesforce, Sand Hill Road, shareholder value, Silicon Valley, skunkworks, smart meter, social graph, software as a service, spice trade, Steve Ballmer, Steve Jobs, subscription business, systems thinking, tech worker, TED Talk, Tim Cook: Apple, transport as a service, Uber and Lyft, uber lyft, WeWork, Y2K, Zipcar

It has clearly mastered the ability to migrate customers from acquired companies onto a single platform for improved accuracy and efficiency of its back-office systems. OPTIMIZE YOUR PRICING AND PACKAGING Over the course of the entire lifetime of a subscription business, do you know how much time the average management team devotes to planning their pricing? According to business intelligence platform ProfitWell, the average amount of time a company spends per year on pricing is less than ten hours. That’s nuts, especially considering the huge impact that pricing has on your bottom line—it can be much more impactful than similar amounts of effort spent on acquisition or retention.


pages: 296 words: 66,815

The AI-First Company by Ash Fontana

23andMe, Amazon Mechanical Turk, Amazon Web Services, autonomous vehicles, barriers to entry, blockchain, business intelligence, business process, business process outsourcing, call centre, Charles Babbage, chief data officer, Clayton Christensen, cloud computing, combinatorial explosion, computer vision, crowdsourcing, data acquisition, data science, deep learning, DevOps, en.wikipedia.org, Geoffrey Hinton, independent contractor, industrial robot, inventory management, John Conway, knowledge economy, Kubernetes, Lean Startup, machine readable, minimum viable product, natural language processing, Network effects, optical character recognition, Pareto efficiency, performance metric, price discrimination, recommendation engine, Ronald Coase, Salesforce, single source of truth, software as a service, source of truth, speech recognition, the scientific method, transaction costs, vertical integration, yield management

Managed by those that otherwise manage engineers. Data engineer: some difference. Managed by those that otherwise manage engineers but may require coordination by those managing a company’s data assets, such as a chief data officer. Data analyst: little difference. Management by analytics or business intelligence leaders, or by a general manager within a business unit. Data scientist: different. Management by nonanalytics leaders is difficult because the work is more experimental and involves advanced analytical methods. Management by engineering is difficult because most of the work is mathematics rather than engineering.


pages: 661 words: 185,701

The Future of Money: How the Digital Revolution Is Transforming Currencies and Finance by Eswar S. Prasad

access to a mobile phone, Adam Neumann (WeWork), Airbnb, algorithmic trading, altcoin, bank run, barriers to entry, Bear Stearns, Ben Bernanke: helicopter money, Bernie Madoff, Big Tech, bitcoin, Bitcoin Ponzi scheme, Bletchley Park, blockchain, Bretton Woods, business intelligence, buy and hold, capital controls, carbon footprint, cashless society, central bank independence, cloud computing, coronavirus, COVID-19, Credit Default Swap, cross-border payments, cryptocurrency, deglobalization, democratizing finance, disintermediation, distributed ledger, diversified portfolio, Dogecoin, Donald Trump, Elon Musk, Ethereum, ethereum blockchain, eurozone crisis, fault tolerance, fiat currency, financial engineering, financial independence, financial innovation, financial intermediation, Flash crash, floating exchange rates, full employment, gamification, gig economy, Glass-Steagall Act, global reserve currency, index fund, inflation targeting, informal economy, information asymmetry, initial coin offering, Internet Archive, Jeff Bezos, Kenneth Rogoff, Kickstarter, light touch regulation, liquidity trap, litecoin, lockdown, loose coupling, low interest rates, Lyft, M-Pesa, machine readable, Mark Zuckerberg, Masayoshi Son, mobile money, Money creation, money market fund, money: store of value / unit of account / medium of exchange, Network effects, new economy, offshore financial centre, open economy, opioid epidemic / opioid crisis, PalmPilot, passive investing, payday loans, peer-to-peer, peer-to-peer lending, Peter Thiel, Ponzi scheme, price anchoring, profit motive, QR code, quantitative easing, quantum cryptography, RAND corporation, random walk, Real Time Gross Settlement, regulatory arbitrage, rent-seeking, reserve currency, ride hailing / ride sharing, risk tolerance, risk/return, Robinhood: mobile stock trading app, robo advisor, Ross Ulbricht, Salesforce, Satoshi Nakamoto, seigniorage, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, smart contracts, SoftBank, special drawing rights, the payments system, too big to fail, transaction costs, uber lyft, unbanked and underbanked, underbanked, Vision Fund, Vitalik Buterin, Wayback Machine, WeWork, wikimedia commons, Y Combinator, zero-sum game

The Dominant Dollar On the dollar’s role as an invoicing currency in international trade, see Gopinath (2016) and chart 26 in “The International Role of the Euro,” European Central Bank, June 2019. Data on the relative importance of various currencies in international payments come from the SWIFT RMB Tracker. The numbers are based on the May 2021 report (which has data through April 2021): https://www.swift.com/our-solutions/compliance-and-shared-services/business-intelligence/renminbi/rmb-tracker/rmb-tracker-document-centre. According to SWIFT calculations (also reported in the SWIFT RMB Tracker), in some months the share of the euro in international payments is slightly larger (although still below that of the US dollar) when payments within the eurozone (which are presumably denominated entirely in euros) are excluded.

The Renminbi Gains Ground, Then Stalls For a discussion of the renminbi’s rise and stall, see Prasad (2019a, 2020). Data on the renminbi’s share of global payments are from the SWIFT RMB Tracker, May 2021 (which has data through April 2021): https://www.swift.com/our-solutions/compliance-and-shared-services/business-intelligence/renminbi/rmb-tracker/document-centre. For market perceptions of the August 2015 policy shift on the exchange rate, see, for instance, Gabriel Wildau, “Renminbi Devaluation Tests China’s Commitment to Free Markets,” Financial Times, August 12, 2015, https://www.ft.com/content/65d07e26-40d0-11e5-9abe-5b335da3a90e.


pages: 269 words: 77,042

Sex, Lies, and Pharmaceuticals: How Drug Companies Plan to Profit From Female Sexual Dysfunction by Ray Moynihan, Barbara Mintzes

business intelligence, clean water, meta-analysis, moral panic, Naomi Klein, New Journalism, placebo effect, profit motive, Ralph Nader, systematic bias

For many researchers, all this activity was bringing what they regarded as long-overdue recognition to women’s sexual suffering, and legitimacy to its study. For the drug companies, it was a strategic part of the planning for what was being billed as the next billion-dollar market. A forward-looking business intelligence report in 2003 named FSD drugs as an area of great future growth for the pharmaceutical industry, part of the burgeoning ‘lifestyle’ market including medicines for baldness, smoking cessation and obesity.38 The report was prepared for industry insiders and, with a hefty price tag, was never intended for public consumption.


pages: 270 words: 79,068

The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers by Ben Horowitz

Airbnb, Ben Horowitz, Benchmark Capital, business intelligence, cloud computing, financial independence, Google Glasses, hiring and firing, Isaac Newton, Jeff Bezos, Kiva Systems, Larry Ellison, Marc Andreessen, Mark Zuckerberg, move fast and break things, new economy, nuclear winter, Peter Thiel, Productivity paradox, random walk, Ronald Reagan, Silicon Valley, six sigma, SoftBank, Steve Ballmer, Steve Jobs, stock buybacks, Strategic Defense Initiative

Without a well thought out, disciplined process for titles and promotions, your employees will become obsessed with the resulting inequities. If you structure things properly, nobody other than you will spend much time thinking about titles other than Employee of the Month. WHEN SMART PEOPLE ARE BAD EMPLOYEES In business, intelligence is always a critical element in any employee, because what we do is difficult and complex and the competitors are filled with extremely smart people. However, intelligence is not the only important quality. Being effective in a company also means working hard, being reliable, and being an excellent member of the team.


Designing Search: UX Strategies for Ecommerce Success by Greg Nudelman, Pabini Gabriel-Petit

access to a mobile phone, Albert Einstein, AltaVista, augmented reality, barriers to entry, Benchmark Capital, business intelligence, call centre, cognitive load, crowdsourcing, folksonomy, information retrieval, Internet of things, Neal Stephenson, Palm Treo, performance metric, QR code, recommendation engine, RFID, search costs, search engine result page, semantic web, Silicon Valley, social graph, social web, speech recognition, text mining, the long tail, the map is not the territory, The Wisdom of Crowds, web application, zero-sum game, Zipcar

Formerly, as UX Manager at WebEx, Pabini led UX strategy, design, and user research for online meeting and collaboration applications. She designed the award-winning Meeting Center and Training Center, setting the industry standard for online meeting software. About the Contributors Pete Bell is the co-founder of Endeca, a company with more than ten years of experience designing search & business intelligence solutions for more than 600 companies. Pete writes and speaks frequently about information science and user experience for audiences like User Interface Engineering, the Simmons Graduate School of Library and Information Science, the Dublin Core Metadata Initiative conference, and the Boston Museum of Science.


pages: 318 words: 78,451

Kanban: Successful Evolutionary Change for Your Technology Business by David J. Anderson

airport security, anti-pattern, business intelligence, call centre, collapse of Lehman Brothers, continuous integration, corporate governance, database schema, domain-specific language, index card, Kaizen: continuous improvement, Kanban, knowledge worker, lateral thinking, loose coupling, performance metric, six sigma, systems thinking, tacit knowledge, Toyota Production System, transaction costs

The business case had been stellar but its candidacy was suspect from the beginning, and some had questioned the quality of the data in the business case. After several attempts, this feature had been selected and was duly implemented. It was one of the larger features processed through the RRT system, and many people got involved and noticed it. Two months after launch, our Director of Business Intelligence did some data mining on the revenue generated. It was a fraction of what had been promised in the original business case, and the estimated payback period against the effort expended was calculated at 19 years. Due to the transparency that Kanban offered us, many stakeholders became aware of this, and there was discussion about how precious capacity had been wasted on this choice when a better choice might have been made instead.


pages: 305 words: 79,303

The Four: How Amazon, Apple, Facebook, and Google Divided and Conquered the World by Scott Galloway

"Susan Fowler" uber, activist fund / activist shareholder / activist investor, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Amazon Robotics, Amazon Web Services, Apple II, autonomous vehicles, barriers to entry, Ben Horowitz, Bernie Sanders, Big Tech, big-box store, Bob Noyce, Brewster Kahle, business intelligence, California gold rush, Cambridge Analytica, cloud computing, Comet Ping Pong, commoditize, cuban missile crisis, David Brooks, Didi Chuxing, digital divide, disintermediation, don't be evil, Donald Trump, Elon Musk, fake news, follow your passion, fulfillment center, future of journalism, future of work, global supply chain, Google Earth, Google Glasses, Google X / Alphabet X, Hacker Conference 1984, Internet Archive, invisible hand, Jeff Bezos, Jony Ive, Khan Academy, Kiva Systems, longitudinal study, Lyft, Mark Zuckerberg, meta-analysis, Network effects, new economy, obamacare, Oculus Rift, offshore financial centre, passive income, Peter Thiel, profit motive, race to the bottom, RAND corporation, ride hailing / ride sharing, risk tolerance, Robert Mercer, Robert Shiller, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, Silicon Valley, Snapchat, software is eating the world, speech recognition, Stephen Hawking, Steve Ballmer, Steve Bannon, Steve Jobs, Steve Wozniak, Stewart Brand, supercomputer in your pocket, Tesla Model S, the long tail, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, undersea cable, vertical integration, warehouse automation, warehouse robotics, Wayback Machine, Whole Earth Catalog, winner-take-all economy, working poor, you are the product, young professional

Excellence, grit, and empathy are timeless attributes of successful people in every field. But as the pace and variability of work increase, success will be at the margins, separating successful people from the herd. As I described at the beginning of this book, my sixth company is L2, a business intelligence (fancy term for research) firm that has grown to 140 people in seven years. Seventy percent of our employees are under thirty; the average age is twenty-eight. L2 employees are often recruited by aspirational firms. They are kids: raw, having had little time to shape their working personalities beyond the nature and the nurture of their youth.


pages: 317 words: 87,566

The Happiness Industry: How the Government and Big Business Sold Us Well-Being by William Davies

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 1960s counterculture, Abraham Maslow, Airbnb, behavioural economics, business intelligence, business logic, corporate governance, data science, dematerialisation, experimental subject, Exxon Valdez, Frederick Winslow Taylor, Gini coefficient, income inequality, intangible asset, invisible hand, joint-stock company, Leo Hollis, lifelogging, market bubble, mental accounting, military-industrial complex, nudge unit, Panopticon Jeremy Bentham, Philip Mirowski, power law, profit maximization, randomized controlled trial, Richard Thaler, road to serfdom, Ronald Coase, Ronald Reagan, science of happiness, scientific management, selective serotonin reuptake inhibitor (SSRI), sentiment analysis, sharing economy, Slavoj Žižek, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, social contagion, social intelligence, Social Responsibility of Business Is to Increase Its Profits, Steve Jobs, TED Talk, The Chicago School, The Spirit Level, theory of mind, urban planning, Vilfredo Pareto, W. E. B. Du Bois, you are the product

Thanks to the security offered by its corporate behemoth of a patron, JWT became an international player, and its particular style of marketing expertise went global. The capacity of US businesses to export to global markets, which surged after World War Two, was greatly aided by the fact that such networks of business intelligence had already permeated much of the capitalist world. Knowledge of foreign consumers was already on hand. Following the acquisition of the GM contract, JWT set about accumulating consumer insight on an unprecedented scale. In less than eighteen months, over 44,000 interviews were done around the world, many in relation to cars, but also on topics such as food and toiletry consumption.2 This was the most ambitious project of mass psychological profiling ever attempted.


pages: 286 words: 87,401

Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies by Reid Hoffman, Chris Yeh

"Susan Fowler" uber, activist fund / activist shareholder / activist investor, adjacent possible, Airbnb, Amazon Web Services, Andy Rubin, autonomous vehicles, Benchmark Capital, bitcoin, Blitzscaling, blockchain, Bob Noyce, business intelligence, Cambridge Analytica, Chuck Templeton: OpenTable:, cloud computing, CRISPR, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, database schema, DeepMind, Didi Chuxing, discounted cash flows, Elon Musk, fake news, Firefox, Ford Model T, forensic accounting, fulfillment center, Future Shock, George Gilder, global pandemic, Google Hangouts, Google X / Alphabet X, Greyball, growth hacking, high-speed rail, hockey-stick growth, hydraulic fracturing, Hyperloop, initial coin offering, inventory management, Isaac Newton, Jeff Bezos, Joi Ito, Khan Academy, late fees, Lean Startup, Lyft, M-Pesa, Marc Andreessen, Marc Benioff, margin call, Mark Zuckerberg, Max Levchin, minimum viable product, move fast and break things, Network effects, Oculus Rift, oil shale / tar sands, PalmPilot, Paul Buchheit, Paul Graham, Peter Thiel, pre–internet, Quicken Loans, recommendation engine, ride hailing / ride sharing, Salesforce, Sam Altman, Sand Hill Road, Saturday Night Live, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Skype, smart grid, social graph, SoftBank, software as a service, software is eating the world, speech recognition, stem cell, Steve Jobs, subscription business, synthetic biology, Tesla Model S, thinkpad, three-martini lunch, transaction costs, transport as a service, Travis Kalanick, Uber for X, uber lyft, web application, winner-take-all economy, work culture , Y Combinator, yellow journalism

Twitter had to tighten its API access rules to reduce the call volume. Whatever metrics you choose, when the organization is still small, the data can generally spread via osmosis between individual employees, supplemented by a regular review during weekly company meetings. You don’t need fancy business intelligence (BI) tools or a dedicated team. Once your organization reaches the Village stage, however, osmosis won’t work. Your people are working on multiple threads, and the organization (which has exceeded Dunbar’s number) is now too big for everyone to know one another. Using a common dashboard will allow you not only to see how the threads interlock but also to coordinate the work of different groups.


pages: 304 words: 82,395

Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schonberger, Kenneth Cukier

23andMe, Affordable Care Act / Obamacare, airport security, Apollo 11, barriers to entry, Berlin Wall, big data - Walmart - Pop Tarts, Black Swan, book scanning, book value, business intelligence, business process, call centre, cloud computing, computer age, correlation does not imply causation, dark matter, data science, double entry bookkeeping, Eratosthenes, Erik Brynjolfsson, game design, hype cycle, IBM and the Holocaust, index card, informal economy, intangible asset, Internet of things, invention of the printing press, Jeff Bezos, Joi Ito, lifelogging, Louis Pasteur, machine readable, machine translation, Marc Benioff, Mark Zuckerberg, Max Levchin, Menlo Park, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, obamacare, optical character recognition, PageRank, paypal mafia, performance metric, Peter Thiel, Plato's cave, post-materialism, random walk, recommendation engine, Salesforce, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, smart grid, smart meter, social graph, sparse data, speech recognition, Steve Jobs, Steven Levy, systematic bias, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Thomas Davenport, Turing test, vertical integration, Watson beat the top human players on Jeopardy!

Besides, the amounts may be changing so quickly that a specific figure would be out of date the moment it appeared. Similarly, Google’s Gmail presents the time of recent messages with exactness, such as “11 minutes ago,” but treats longer durations with a nonchalant “2 hours ago,” as do Facebook and some others. The industry of business intelligence and analytics software was long built on promising clients “a single version of the truth”—the popular buzz words from the 2000s from the technology vendors in these fields. Executives used the phrase without irony. Some still do. By this, they mean that everyone accessing a company’s information-technology systems can tap into the same data; that the marketing team and the sales team don’t have to fight over who has the correct customer or sales numbers before the meeting even begins.


pages: 323 words: 92,135

Running Money by Andy Kessler

Alan Greenspan, Andy Kessler, Apple II, bioinformatics, Bob Noyce, British Empire, business intelligence, buy and hold, buy low sell high, call centre, Charles Babbage, Corn Laws, cotton gin, Douglas Engelbart, Fairchild Semiconductor, family office, flying shuttle, full employment, General Magic , George Gilder, happiness index / gross national happiness, interest rate swap, invisible hand, James Hargreaves, James Watt: steam engine, joint-stock company, joint-stock limited liability company, junk bonds, knowledge worker, Leonard Kleinrock, Long Term Capital Management, mail merge, Marc Andreessen, margin call, market bubble, Mary Meeker, Maui Hawaii, Menlo Park, Metcalfe’s law, Michael Milken, Mitch Kapor, Network effects, packet switching, pattern recognition, pets.com, railway mania, risk tolerance, Robert Metcalfe, Sand Hill Road, Silicon Valley, South China Sea, spinning jenny, Steve Jobs, Steve Wozniak, Suez canal 1869, Toyota Production System, TSMC, UUNET, zero-sum game

“I’ll bet Uncle Witty owned Quarter-deck, Level 3, 4-Systems, and the worst of them all, 3-Con.” “Take it easy,” I told Nick. “Some people never learn.” “Did you hear about Micro-tragedy? Could see that blowup from miles away?” It was Nick Moore again for my afternoon dose of cynicism. Microstrategy was one of those scary software blowups. They sold “business intelligence” software, one of those vague names only a McKinsey consultant could love, so companies could track business trends. The stock was hot, and growth was through the roof, until someone figured out that they were signing three-year contracts and reporting the revenues right away, a Bozo no-no of accounting.


pages: 389 words: 87,758

No Ordinary Disruption: The Four Global Forces Breaking All the Trends by Richard Dobbs, James Manyika

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, access to a mobile phone, additive manufacturing, Airbnb, Amazon Mechanical Turk, American Society of Civil Engineers: Report Card, asset light, autonomous vehicles, Bakken shale, barriers to entry, business cycle, business intelligence, carbon tax, Carmen Reinhart, central bank independence, circular economy, cloud computing, corporate governance, creative destruction, crowdsourcing, data science, demographic dividend, deskilling, digital capitalism, disintermediation, disruptive innovation, distributed generation, driverless car, Erik Brynjolfsson, financial innovation, first square of the chessboard, first square of the chessboard / second half of the chessboard, Gini coefficient, global supply chain, global village, high-speed rail, hydraulic fracturing, illegal immigration, income inequality, index fund, industrial robot, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, inventory management, job automation, Just-in-time delivery, Kenneth Rogoff, Kickstarter, knowledge worker, labor-force participation, low interest rates, low skilled workers, Lyft, M-Pesa, machine readable, mass immigration, megacity, megaproject, mobile money, Mohammed Bouazizi, Network effects, new economy, New Urbanism, ocean acidification, oil shale / tar sands, oil shock, old age dependency ratio, openstreetmap, peer-to-peer lending, pension reform, pension time bomb, private sector deleveraging, purchasing power parity, quantitative easing, recommendation engine, Report Card for America’s Infrastructure, RFID, ride hailing / ride sharing, Salesforce, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Snapchat, sovereign wealth fund, spinning jenny, stem cell, Steve Jobs, subscription business, supply-chain management, synthetic biology, TaskRabbit, The Great Moderation, trade route, transaction costs, Travis Kalanick, uber lyft, urban sprawl, Watson beat the top human players on Jeopardy!, working-age population, Zipcar

Upgrading to premium membership—monthly prices start at $59.99 per month for the Business Plus account—affords the user greater insight into who has been looking at his or her profile, the ability to send more messages to potential leads, and the use of more advanced search filters.60 A third model is monetization of big data, either through innovative business-to-business offerings (for example, crowd-sourcing business intelligence or outsourced data science services) or through developing more relevant products, services, or content for which consumers are willing to pay. LinkedIn, for example, makes 20 percent of its revenue from subscriptions, 30 percent from marketing, and 50 percent from talent solutions, a core part of which is selling targeted talent intelligence and tools to recruiters.61 You will have to keep experimenting in order to capture more consumer surplus for your business.


pages: 336 words: 90,749

How to Fix Copyright by William Patry

A Declaration of the Independence of Cyberspace, barriers to entry, big-box store, borderless world, bread and circuses, business cycle, business intelligence, citizen journalism, cloud computing, commoditize, content marketing, creative destruction, crowdsourcing, death of newspapers, digital divide, en.wikipedia.org, facts on the ground, Frederick Winslow Taylor, George Akerlof, Glass-Steagall Act, Gordon Gekko, haute cuisine, informal economy, invisible hand, John Perry Barlow, Joseph Schumpeter, Kickstarter, knowledge economy, lone genius, means of production, moral panic, new economy, road to serfdom, Ronald Coase, Ronald Reagan, search costs, semantic web, shareholder value, Silicon Valley, The Chicago School, The Wealth of Nations by Adam Smith, trade route, transaction costs, trickle-down economics, Twitter Arab Spring, Tyler Cowen, vertical integration, winner-take-all economy, zero-sum game

In a 2009 interview, MPAA Director of Special Projects Robert Bauer was quoted as saying, “Our job is to isolate the forms of piracy that compete with legitimate sales, treat those as a proxy for unmet consumer demand, and find a way to meet that demand.”35 One way was recently suggested by Ben Karakunnel, director of business intelligence at Warner Brothers’ antipiracy unit, as reported in the paidContent. org website: In the international markets, illegal WB content in which pirates dub or subtitle themselves is increasingly popular. For one unspecified program Karakunnel used as an example, it wasn’t until the third day after its initial airdate that one such pirate-created translated version accounted for 23 percent of pirated files of that particular program.


pages: 339 words: 88,732

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson, Andrew McAfee

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, Alan Greenspan, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, American Society of Civil Engineers: Report Card, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Boston Dynamics, British Empire, business cycle, business intelligence, business process, call centre, carbon tax, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, congestion pricing, corporate governance, cotton gin, creative destruction, crowdsourcing, data science, David Ricardo: comparative advantage, digital map, driverless car, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, Fairchild Semiconductor, falling living standards, Filter Bubble, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, full employment, G4S, game design, general purpose technology, global village, GPS: selective availability, Hans Moravec, happiness index / gross national happiness, illegal immigration, immigration reform, income inequality, income per capita, indoor plumbing, industrial robot, informal economy, intangible asset, inventory management, James Watt: steam engine, Jeff Bezos, Jevons paradox, jimmy wales, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Khan Academy, Kiva Systems, knowledge worker, Kodak vs Instagram, law of one price, low skilled workers, Lyft, Mahatma Gandhi, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Mars Rover, mass immigration, means of production, Narrative Science, Nate Silver, natural language processing, Network effects, new economy, New Urbanism, Nicholas Carr, Occupy movement, oil shale / tar sands, oil shock, One Laptop per Child (OLPC), pattern recognition, Paul Samuelson, payday loans, post-work, power law, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Robert Solow, Rodney Brooks, Ronald Reagan, search costs, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Simon Kuznets, six sigma, Skype, software patent, sovereign wealth fund, speech recognition, statistical model, Steve Jobs, Steven Pinker, Stuxnet, supply-chain management, TaskRabbit, technological singularity, telepresence, The Bell Curve by Richard Herrnstein and Charles Murray, the Cathedral and the Bazaar, the long tail, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K

“Computer and Dynamo: The Modern Productivity Paradox in a Not-Too-Distant Mirror,” Center for Economic Policy Research, no. 172, Stanford University, July 1989, http://www.dklevine.com/archive/refs4115.pdf. 10. For instance, Materials Resource Planning (MRP) systems, which begat Enterprise Resource Planning (ERP), and then Supply Chain Management (SCM), Customer Relationship Management (CRM), and, more recently, Business Intelligence (BI), Analytics and many other large-scale systems. 11. Todd Traub, “Wal-Mart Used Technology to Become Supply Chain Leader,” Arkansas Business, http://www.arkansasbusiness.com/article/85508/wal-mart-used-technology-to-become-supply-chain-leader (accessed July 20, 2013). 12. This is consistent with a similar analysis by Oliner and Sichel (2002), who wrote, “both the use of information technology and the efficiency gains associated with the production of information technology were central factors in [the productivity] resurgence.”


pages: 319 words: 90,965

The End of College: Creating the Future of Learning and the University of Everywhere by Kevin Carey

Albert Einstein, barriers to entry, Bayesian statistics, behavioural economics, Berlin Wall, Blue Ocean Strategy, business cycle, business intelligence, carbon-based life, classic study, Claude Shannon: information theory, complexity theory, data science, David Heinemeier Hansson, declining real wages, deliberate practice, discrete time, disruptive innovation, double helix, Douglas Engelbart, Douglas Engelbart, Downton Abbey, Drosophila, Fairchild Semiconductor, Firefox, Frank Gehry, Google X / Alphabet X, Gregor Mendel, informal economy, invention of the printing press, inventory management, John Markoff, Khan Academy, Kickstarter, low skilled workers, Lyft, Marc Andreessen, Mark Zuckerberg, meta-analysis, natural language processing, Network effects, open borders, pattern recognition, Peter Thiel, pez dispenser, Recombinant DNA, ride hailing / ride sharing, Ronald Reagan, Ruby on Rails, Sand Hill Road, self-driving car, Silicon Valley, Silicon Valley startup, social web, South of Market, San Francisco, speech recognition, Steve Jobs, technoutopianism, transcontinental railway, uber lyft, Vannevar Bush

MicroStrategy analyzed the inventory data and told Victoria’s Secret it could make a lot more money by varying inventory accordingly. That kind of work was enough to make MicroStrategy a thriving technology company. Saylor played the venture capital game shrewdly, keeping the lion’s share of the company’s stock for himself. He also had grander visions than mere business intelligence. At MIT he had learned about the structure of scientific revolutions, how inventions like the printing press had profoundly changed not just the act of learning but whole systems of politics and philosophy. The world was on the verge of another such change, Saylor believed, and MicroStrategy software would be the key tool for making sense of the new information—not just for businesses but for every single person on earth.


The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences by Rob Kitchin

Bayesian statistics, business intelligence, business process, cellular automata, Celtic Tiger, cloud computing, collateralized debt obligation, conceptual framework, congestion charging, corporate governance, correlation does not imply causation, crowdsourcing, data science, discrete time, disruptive innovation, George Gilder, Google Earth, hype cycle, Infrastructure as a Service, Internet Archive, Internet of things, invisible hand, knowledge economy, Large Hadron Collider, late capitalism, lifelogging, linked data, longitudinal study, machine readable, Masdar, means of production, Nate Silver, natural language processing, openstreetmap, pattern recognition, platform as a service, recommendation engine, RFID, semantic web, sentiment analysis, SimCity, slashdot, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart grid, smart meter, software as a service, statistical model, supply-chain management, technological solutionism, the scientific method, The Signal and the Noise by Nate Silver, transaction costs

Similarly, with data analytics, clients usually receive the analysis, but not the underlying data. Data consolidation and re-sale, and associated data analysis and value-added services, are a multibillion-dollar industry, with vast quantities of data and derived information being rented, bought and sold daily across a variety of markets – retail, financial, health, tourism, logistics, business intelligence, real estate, private security, political polling, and so on. These data concern all facets of everyday life and include public administration, communications, consumption of goods and media, travel, leisure, crime, social media interactions, and so on. Specialist data brokers have been around for a long time, collating data from media subscriptions (e.g., newspapers, magazines), mail-order retailers, polls, surveys, travel agencies, conferences, contests, product registration and warranties, payment processing companies, government records, and so on (CIPPIC 2006).


pages: 408 words: 85,118

Python for Finance by Yuxing Yan

asset-backed security, book value, business cycle, business intelligence, capital asset pricing model, constrained optimization, correlation coefficient, data science, distributed generation, diversified portfolio, financial engineering, functional programming, implied volatility, market microstructure, P = NP, p-value, quantitative trading / quantitative finance, risk free rate, Sharpe ratio, tail risk, time value of money, value at risk, volatility smile, zero-sum game

Rozario, Swati Kumari, Arwa Manasawala, Ruchita Bhansali, Apeksha Chitnis, and Pramila Balan as well as the external reviewers, Martin Olveyra, Mourad MOURAFIQ, and Loucas Parayiannis, for their valuable advice, suggestions, and criticism. Finally, and most importantly, I thank my wife, Xiaoning Jin, for her strong support, my daughter, Jing Yan, and son, James Yan, for their understanding and love they have showered on me over the years. About the Reviewers Jiri Pik is a finance and business intelligence consultant working with major investment banks, hedge funds, and other financial players. He has architected and delivered breakthrough trading, portfolio and risk management systems, and decision-support systems across industries. His consulting firm, WIXESYS, provides their clients with certified expertise, judgment, and execution at the speed of light.


pages: 347 words: 91,318

Netflixed: The Epic Battle for America's Eyeballs by Gina Keating

activist fund / activist shareholder / activist investor, AOL-Time Warner, Apollo 13, barriers to entry, Bear Stearns, business intelligence, Carl Icahn, collaborative consumption, company town, corporate raider, digital rights, inventory management, Jeff Bezos, late fees, Mark Zuckerberg, McMansion, Menlo Park, Michael Milken, Netflix Prize, new economy, out of africa, performance metric, Ponzi scheme, pre–internet, price stability, recommendation engine, Saturday Night Live, shareholder value, Silicon Valley, Silicon Valley startup, Steve Jobs, subscription business, Superbowl ad, tech worker, telemarketer, warehouse automation, X Prize

Hastings exacerbated the tension by baldly stating at a board meeting, in response to a director’s question about succession, and in front of a surprised Randolph and McCarthy, that he considered Kilgore his successor. Hastings invariably seemed to come down on Kilgore’s side when she demanded more money from the company’s tight budget for marketing and “business intelligence” programs. He also allowed her to consolidate marketing and public relations functions under her control, creating a fiefdom inside of Netflix that went unchallenged for more than a decade. Her loyal number two was Jessie Becker, a fellow alumnus of the University of Pennsylvania’s Wharton School and Stanford University’s business school who unquestioningly carried out Kilgore’s dictates, just as McCord carried out Hastings’s wishes.


Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann

active measures, Amazon Web Services, billion-dollar mistake, bitcoin, blockchain, business intelligence, business logic, business process, c2.com, cloud computing, collaborative editing, commoditize, conceptual framework, cryptocurrency, data science, database schema, deep learning, DevOps, distributed ledger, Donald Knuth, Edward Snowden, end-to-end encryption, Ethereum, ethereum blockchain, exponential backoff, fake news, fault tolerance, finite state, Flash crash, Free Software Foundation, full text search, functional programming, general-purpose programming language, Hacker News, informal economy, information retrieval, Internet of things, iterative process, John von Neumann, Ken Thompson, Kubernetes, Large Hadron Collider, level 1 cache, loose coupling, machine readable, machine translation, Marc Andreessen, microservices, natural language processing, Network effects, no silver bullet, operational security, packet switching, peer-to-peer, performance metric, place-making, premature optimization, recommendation engine, Richard Feynman, self-driving car, semantic web, Shoshana Zuboff, social graph, social web, software as a service, software is eating the world, sorting algorithm, source of truth, SPARQL, speech recognition, SQL injection, statistical model, surveillance capitalism, systematic bias, systems thinking, Tragedy of the Commons, undersea cable, web application, WebSocket, wikimedia commons

• How many more bananas than usual did we sell during our latest promotion? • Which brand of baby food is most often purchased together with brand X diapers? 90 | Chapter 3: Storage and Retrieval These queries are often written by business analysts, and feed into reports that help the management of a company make better decisions (business intelligence). In order to differentiate this pattern of using databases from transaction processing, it has been called online analytic processing (OLAP) [47].iv The difference between OLTP and OLAP is not always clear-cut, but some typical characteristics are listed in Table 3-1. Table 3-1. Comparing characteristics of transaction processing versus analytic systems Property Main read pattern Transaction processing systems (OLTP) Small number of records per query, fetched by key Analytic systems (OLAP) Aggregate over large number of records Main write pattern Random-access, low-latency writes from user input Bulk import (ETL) or event stream Primarily used by End user/customer, via web application Internal analyst, for decision support What data represents Latest state of data (current point in time) History of events that happened over time Dataset size Gigabytes to terabytes Terabytes to petabytes At first, the same databases were used for both transaction processing and analytic queries.

Specialization for different domains While the extensibility of being able to run arbitrary code is useful, there are also many common cases where standard processing patterns keep reoccurring, and so it is worth having reusable implementations of the common building blocks. Tradition‐ ally, MPP databases have served the needs of business intelligence analysts and busi‐ ness reporting, but that is just one among many domains in which batch processing is used. Another domain of increasing importance is statistical and numerical algorithms, which are needed for machine learning applications such as classification and recom‐ mendation systems.


pages: 1,237 words: 227,370

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann

active measures, Amazon Web Services, billion-dollar mistake, bitcoin, blockchain, business intelligence, business logic, business process, c2.com, cloud computing, collaborative editing, commoditize, conceptual framework, cryptocurrency, data science, database schema, deep learning, DevOps, distributed ledger, Donald Knuth, Edward Snowden, end-to-end encryption, Ethereum, ethereum blockchain, exponential backoff, fake news, fault tolerance, finite state, Flash crash, Free Software Foundation, full text search, functional programming, general-purpose programming language, Hacker News, informal economy, information retrieval, Infrastructure as a Service, Internet of things, iterative process, John von Neumann, Ken Thompson, Kubernetes, Large Hadron Collider, level 1 cache, loose coupling, machine readable, machine translation, Marc Andreessen, microservices, natural language processing, Network effects, no silver bullet, operational security, packet switching, peer-to-peer, performance metric, place-making, premature optimization, recommendation engine, Richard Feynman, self-driving car, semantic web, Shoshana Zuboff, social graph, social web, software as a service, software is eating the world, sorting algorithm, source of truth, SPARQL, speech recognition, SQL injection, statistical model, surveillance capitalism, systematic bias, systems thinking, Tragedy of the Commons, undersea cable, web application, WebSocket, wikimedia commons

How many more bananas than usual did we sell during our latest promotion? Which brand of baby food is most often purchased together with brand X diapers? These queries are often written by business analysts, and feed into reports that help the management of a company make better decisions (business intelligence). In order to differentiate this pattern of using databases from transaction processing, it has been called online analytic processing (OLAP) [47].iv The difference between OLTP and OLAP is not always clear-cut, but some typical characteristics are listed in Table 3-1. Table 3-1. Comparing characteristics of transaction processing versus analytic systems Property Transaction processing systems (OLTP) Analytic systems (OLAP) Main read pattern Small number of records per query, fetched by key Aggregate over large number of records Main write pattern Random-access, low-latency writes from user input Bulk import (ETL) or event stream Primarily used by End user/customer, via web application Internal analyst, for decision support What data represents Latest state of data (current point in time) History of events that happened over time Dataset size Gigabytes to terabytes Terabytes to petabytes At first, the same databases were used for both transaction processing and analytic queries.

Specialization for different domains While the extensibility of being able to run arbitrary code is useful, there are also many common cases where standard processing patterns keep reoccurring, and so it is worth having reusable implementations of the common building blocks. Traditionally, MPP databases have served the needs of business intelligence analysts and business reporting, but that is just one among many domains in which batch processing is used. Another domain of increasing importance is statistical and numerical algorithms, which are needed for machine learning applications such as classification and recommendation systems.


pages: 328 words: 100,381

Top Secret America: The Rise of the New American Security State by Dana Priest, William M. Arkin

airport security, business intelligence, company town, dark matter, disinformation, drone strike, friendly fire, Google Earth, hiring and firing, illegal immigration, immigration reform, index card, information security, Julian Assange, operational security, profit motive, RAND corporation, Ronald Reagan, Timothy McVeigh, WikiLeaks

Commercial real-estate agent Lane, the building’s owner, had his engineers reinforce the steel beams to meet government specifications for security. The senior vice president of a local real estate firm has become something of a snoop himself when it comes to his NSA neighborhood. At fifty-five, he has lived and worked in its shadow all his life and has schooled himself on its growing presence in his community. He collects business intelligence. He has his own network of informants, executives like himself hoping to make a killing off an organization many of his neighbors don’t know a thing about. Lane takes note when the NSA or another secretive government organization leases another building, hires more contractors, and expands its outreach to the local business community.


Cataloging the World: Paul Otlet and the Birth of the Information Age by Alex Wright

1960s counterculture, Ada Lovelace, barriers to entry, British Empire, business climate, business intelligence, Cape to Cairo, card file, centralized clearinghouse, Charles Babbage, Computer Lib, corporate governance, crowdsourcing, Danny Hillis, Deng Xiaoping, don't be evil, Douglas Engelbart, Douglas Engelbart, Electric Kool-Aid Acid Test, European colonialism, folksonomy, Frederick Winslow Taylor, Great Leap Forward, hive mind, Howard Rheingold, index card, information retrieval, invention of movable type, invention of the printing press, Jane Jacobs, John Markoff, Kevin Kelly, knowledge worker, Law of Accelerating Returns, Lewis Mumford, linked data, Livingstone, I presume, lone genius, machine readable, Menlo Park, military-industrial complex, Mother of all demos, Norman Mailer, out of africa, packet switching, pneumatic tube, profit motive, RAND corporation, Ray Kurzweil, scientific management, Scramble for Africa, self-driving car, semantic web, Silicon Valley, speech recognition, Steve Jobs, Stewart Brand, systems thinking, Ted Nelson, The Death and Life of Great American Cities, the scientific method, Thomas L Friedman, urban planning, Vannevar Bush, W. E. B. Du Bois, Whole Earth Catalog

As Krajewski writes, “As soon as a box of index cards reaches a critical mass of entries and cross-references, it offers the basis for a special form of communication, a proper poetological procedure of knowledge production that leads users to unexpected results.”34 Those results—what today we might call business intelligence—offered the prospect of a powerful competitive advantage for corporations, which in turn began to see the long-term potential in applying the lessons of the library catalog to their business operations. They found a willing partner in Melvil Dewey. In 1876, Dewey established a company called the Library Bureau, whose mission was to sell to libraries and other organizations supplies such as catalog cards, drawers, “bureau boxes,” and other material to help companies implement his scheme.


pages: 404 words: 95,163

Amazon: How the World’s Most Relentless Retailer Will Continue to Revolutionize Commerce by Natalie Berg, Miya Knights

3D printing, Adam Neumann (WeWork), Airbnb, Amazon Robotics, Amazon Web Services, asset light, augmented reality, Bernie Sanders, big-box store, business intelligence, cloud computing, Colonization of Mars, commoditize, computer vision, connected car, deep learning, DeepMind, digital divide, Donald Trump, Doomsday Clock, driverless car, electronic shelf labels (ESLs), Elon Musk, fulfillment center, gig economy, independent contractor, Internet of things, inventory management, invisible hand, Jeff Bezos, Kiva Systems, market fragmentation, new economy, Ocado, pattern recognition, Ponzi scheme, pre–internet, QR code, race to the bottom, random stow, recommendation engine, remote working, Salesforce, sensor fusion, sharing economy, Skype, SoftBank, Steve Bannon, sunk-cost fallacy, supply-chain management, TaskRabbit, TechCrunch disrupt, TED Talk, trade route, underbanked, urban planning, vertical integration, warehouse automation, warehouse robotics, WeWork, white picket fence, work culture

It is also a lesson in putting the needs of the customer at the heart of innovation any business could learn from. In the 2010 Amazon Annual Report, Bezos wrote: Look inside a current textbook on software architecture, and you’ll find few patterns that we don’t apply at Amazon. We use high-performance transactions systems, complex rendering and object caching, workflow and queuing systems, business intelligence and data analytics, machine learning and pattern recognition, neural networks and probabilistic decision making, and a wide variety of other techniques. And while many of our systems are based on the latest in computer science research, this often hasn’t been sufficient; our architects and engineers have had to advance research in directions that no academic had yet taken.


pages: 340 words: 97,723

The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity by Amy Webb

"Friedman doctrine" OR "shareholder theory", Ada Lovelace, AI winter, air gap, Airbnb, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic bias, AlphaGo, Andy Rubin, artificial general intelligence, Asilomar, autonomous vehicles, backpropagation, Bayesian statistics, behavioural economics, Bernie Sanders, Big Tech, bioinformatics, Black Lives Matter, blockchain, Bretton Woods, business intelligence, Cambridge Analytica, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, Computing Machinery and Intelligence, CRISPR, cross-border payments, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Demis Hassabis, Deng Xiaoping, disinformation, distributed ledger, don't be evil, Donald Trump, Elon Musk, fail fast, fake news, Filter Bubble, Flynn Effect, Geoffrey Hinton, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Herman Kahn, high-speed rail, Inbox Zero, Internet of things, Jacques de Vaucanson, Jeff Bezos, Joan Didion, job automation, John von Neumann, knowledge worker, Lyft, machine translation, Mark Zuckerberg, Menlo Park, move fast and break things, Mustafa Suleyman, natural language processing, New Urbanism, Nick Bostrom, one-China policy, optical character recognition, packet switching, paperclip maximiser, pattern recognition, personalized medicine, RAND corporation, Ray Kurzweil, Recombinant DNA, ride hailing / ride sharing, Rodney Brooks, Rubik’s Cube, Salesforce, Sand Hill Road, Second Machine Age, self-driving car, seminal paper, SETI@home, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart cities, South China Sea, sovereign wealth fund, speech recognition, Stephen Hawking, strong AI, superintelligent machines, surveillance capitalism, technological singularity, The Coming Technological Singularity, the long tail, theory of mind, Tim Cook: Apple, trade route, Turing machine, Turing test, uber lyft, Von Neumann architecture, Watson beat the top human players on Jeopardy!, zero day

The result is a first-of-its-kind film shown inside a special theatrical set, one that (along with the retinal projection system) produces a completely original—and entirely immersive— storytelling experience. AI is helping organizations of all stripes be more creative in their approach to management. The G-MAFIA powers predictive models for business intelligence, helping to find efficiencies, cost savings, and areas for improvement. Human resources departments use pattern recognition to evaluate productivity and morale—and to effectively solve for bias in hiring and promotions. We no longer use resumes; our PDRs show our strengths and weaknesses, and AI programs scan our records before recommending us to human hiring managers.


pages: 362 words: 104,308

Forty Signs of Rain by Kim Stanley Robinson

bioinformatics, business intelligence, double helix, Dr. Strangelove, experimental subject, Intergovernmental Panel on Climate Change (IPCC), Kim Stanley Robinson, phenotype, precautionary principle, prisoner's dilemma, Ronald Reagan, social intelligence, stem cell, the scientific method, zero-sum game

., and the colophon is a trademark of Random House, Inc. Visit our website at www.bantamdell.com Library of Congress Cataloging in Publication Data Robinson, Kim Stanley. Forty signs of rain / Kim Stanley Robinson. p. cm. eISBN 0-553-89817-5 1. Scientists—Fiction. 2. Legislators—Fiction. 3. Washington (D.C.)—Fiction. 4. Business intelligence—Fiction. I. Title. PS3568.O2893F67 2004 2003063683 v1.0 eBook Info Title: Forty Signs of Rain Creator: Kim Stanley Robinson Publisher: Random House Contributor: None Date: 2004-06-01 Format: OEB Identifier: 0-553-89817-5 Type: Subject: Description: Language: en Rights: Copyright © 2004 by Kim Stanley Robinson


pages: 348 words: 98,757

The Trade of Queens by Charles Stross

business intelligence, call centre, Dr. Strangelove, false flag, illegal immigration, index card, inflation targeting, land reform, multilevel marketing, profit motive, Project for a New American Century, seigniorage

"Paulet, Paulette, Powell-et? How do you spell it, it's a first name. . . ." He read for a long time, swearing occasionally at Miriam's spidery handwriting and her copious list of contacts—She's a journalist, it's what she does—until he hit paydirt a third of the way through: Milan, Paulette. Business intelligence division, the Weatherman. That was where Miriam had worked, last time he looked. "Bingo," Mike muttered. There was a cell number and a street address out in Somerville. He made a note of it; then, systematic to the end, he went back to the cassette tape. The next message was a call from Steve Schroeder—his voice familiar—asking Miriam to get in touch.


pages: 390 words: 96,624

Consent of the Networked: The Worldwide Struggle for Internet Freedom by Rebecca MacKinnon

A Declaration of the Independence of Cyberspace, Bay Area Rapid Transit, Berlin Wall, blood diamond, business cycle, business intelligence, Cass Sunstein, Chelsea Manning, citizen journalism, Citizen Lab, cloud computing, cognitive dissonance, collective bargaining, conceptual framework, corporate social responsibility, Deng Xiaoping, digital divide, digital Maoism, don't be evil, Eben Moglen, Evgeny Morozov, Filter Bubble, Firefox, future of journalism, Global Witness, high-speed rail, illegal immigration, Jaron Lanier, Jeff Bezos, John Markoff, John Perry Barlow, Joi Ito, Julian Assange, Mark Zuckerberg, Mikhail Gorbachev, MITM: man-in-the-middle, national security letter, online collectivism, Panopticon Jeremy Bentham, Parag Khanna, pre–internet, race to the bottom, real-name policy, Richard Stallman, Ronald Reagan, sharing economy, Silicon Valley, Silicon Valley startup, Skype, Steve Crocker, Steven Levy, Tactical Technology Collective, technological determinism, WikiLeaks, Yochai Benkler

For more on DPI and its origins, see Hal Abelson, Ken Ledeen, and Chris Lewis, “Just Deliver the Packets,” in Essays on Deep Packet Inspection, Office of the Privacy Commissioner of Canada, 2009, http://dpi.priv.gc.ca/index.php/essays/just-deliver-the-packets; and Ralf Bendrath, “Global Technology Trends and National Regulation: Explaining Variation in the Governance of Deep Packet Inspection,” paper presented at the International Studies Annual Convention, New York City, February 2009, International Studies Association, http://userpage.fu-berlin.de/~bendrath/Paper_Ralf-Bendrath_DPI_v1-5.pdf. 59 the Egyptian government had purchased DPI technology from a company called Narus: Timothy Karr, “One US Corporation’s Role in Egypt’s Brutal Crackdown,” Huffington Post, January 28, 2011, www.huffingtonpost.com/timothy-karr/one-us-corporations-role-_b_815281.html (accessed June 27, 2011). 59 Narus signed a multimillion-dollar deal in 2005 with Giza Systems of Egypt: Trevor Lloyd-Jones, “Narus Signs Regional Licence with Giza Systems,” Business Intelligence Middle East, September 14, 2005, www.bi-me.com/main.php?id=2047&t=1 (accessed June 27, 2011). See also p. 10 of a Giza Systems newsletter, mentioning the licensing of Narus technology to Saudi Arabia and Libya: www.gizasystems.com/admin%5CNewsLetter%5CPDF%5C8.pdf. 59 Karr’s Free Press and the Paris-based Reporters Without Borders also lodged inquiries with Cisco Systems: Timothy Karr and Clothilde Le Coz, “Corporations and the Arab Net Crackdown,” March 25, 2011, www.fpif.org/articles/corporations_and_the_arab_net_crackdown (accessed June 27, 2011). 60 a business offer for an intrusion and surveillance software product called FinFisher: Eli Lake, “British Firm Offered Spy Software to Egypt,” Washington Times, April 25, 2011, www.washingtontimes.com/news/2011/apr/25/british-firm-offered-spy-software-to-egypt; Hussein uploaded the original document online: http://moftasa.posterous.com/finfisher-intrusion-software-spy-on-email-sof.


pages: 335 words: 96,002

WEconomy: You Can Find Meaning, Make a Living, and Change the World by Craig Kielburger, Holly Branson, Marc Kielburger, Sir Richard Branson, Sheryl Sandberg

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, Airbnb, Albert Einstein, An Inconvenient Truth, barriers to entry, benefit corporation, blood diamond, Boeing 747, business intelligence, business process, carbon footprint, clean tech, clean water, Colonization of Mars, content marketing, corporate social responsibility, Downton Abbey, Elon Musk, energy transition, family office, food desert, future of work, global village, impact investing, inventory management, James Dyson, job satisfaction, Kickstarter, market design, meta-analysis, microcredit, Nelson Mandela, Occupy movement, pre–internet, retail therapy, Salesforce, shareholder value, sharing economy, Sheryl Sandberg, Silicon Valley, Snapchat, Steve Jobs, TED Talk, telemarketer, The Fortune at the Bottom of the Pyramid, Virgin Galactic, working poor, Y Combinator

Nonprofits and corporate purpose projects can use the same monitoring and evaluation (M&E) tools available to track business targets. Because each action plan is unique—with highly focused goals—I'll briefly review some general best practices. 67 percent of companies are involved in a partnership with a nonprofit or charity.4 Monitoring tools, or what you might call “business intelligence,” track your program's vitals. How were resources spent? How many participants signed up? How many team members were involved? Did participants experience the intended outcome? If these questions seem like no-brainers, you'd be surprised to learn I've encountered many companies that forget how to be well-run companies when they pick a cause.


pages: 365 words: 102,306

Legacy: Gangsters, Corruption and the London Olympics by Michael Gillard

Boris Johnson, business intelligence, centre right, Crossrail, forensic accounting, Jeremy Corbyn, offshore financial centre, One Laptop per Child (OLPC), upwardly mobile, working-age population, young professional

The jowly investigator in his mid-fifties is part of the newer breed of corporate gumshoe, the accountants whose playgrounds are the boardroom battles when clients find themselves on the wrong end of a criminal or civil prosecution. Hill was a partner at PKF, a forensic accountancy firm, where he ran the business intelligence team carrying out due diligence inquiries for clients, a fancy name for all manner of investigative techniques, some that straddle the line of legality and plausible deniability. The unwritten agreement between private investigators and their corporate clients in this new world is the same as it ever was: don’t get caught, and if you do, you’re on your own.


pages: 479 words: 102,876

The King of Oil: The Secret Lives of Marc Rich by Daniel Ammann

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", accounting loophole / creative accounting, anti-communist, Ayatollah Khomeini, banking crisis, Berlin Wall, Boeing 747, book value, Boycotts of Israel, business intelligence, buy low sell high, energy security, family office, Johann Wolfgang von Goethe, Michael Milken, Mikhail Gorbachev, Nelson Mandela, oil shock, peak oil, purchasing power parity, Ronald Reagan, subprime mortgage crisis, Suez crisis 1956, trade liberalization, transaction costs, transfer pricing, Upton Sinclair, Yom Kippur War

It was during this time that Azulay met Ehud Barak, who was the head of Israeli military intelligence and would later become the prime minister of Israel.7 In 1983–84, after having resigned from the Mossad, Azulay was advising a Spanish bank on how to deal with Basque terrorists when he was introduced to Marc Rich. Rich engaged his services during the time of Giuliani’s indictment against him. “He had security problems. He had business intelligence problems,” Azulay remembers and explains how he went about determining Rich’s security vulnerabilities. “It’s not magic. It’s about accurately evaluating each situation. For example, when Marc was invited to somewhere, I used to ask, ‘How did the invitation come about? Who made the invitation?


The Unusual Billionaires by Saurabh Mukherjea

Albert Einstein, asset light, Atul Gawande, backtesting, barriers to entry, Black-Scholes formula, book value, British Empire, business cycle, business intelligence, business process, buy and hold, call centre, Checklist Manifesto, commoditize, compound rate of return, corporate governance, dematerialisation, disintermediation, diversification, equity risk premium, financial innovation, forensic accounting, full employment, inventory management, low cost airline, low interest rates, Mahatma Gandhi, Peter Thiel, QR code, risk free rate, risk-adjusted returns, shareholder value, Silicon Valley, Steve Jobs, supply-chain management, The Wisdom of Crowds, transaction costs, upwardly mobile, Vilfredo Pareto, wealth creators, work culture

Sharma told me, ‘As I had a background in creating various start-ups for ICICI Bank, I knew it was best to go to an organization which had well-developed skills for each of the specialist areas.’ Sridharan built a team for retail credit policy and retail credit underwriting. He strengthened this by adding teams for analytics and business intelligence with large-scale capability for analytics and modelling. What started off in 2010 with a couple of members is now a 100-member team, doing analytics not only for retail lending but also for retail liability, corporate banking and wholesale banking. In 2012, Sharma focused on the rebranding of Axis Bank with the tag line, Badhti ka naam zindagi (life is about progress).


pages: 385 words: 111,113

Augmented: Life in the Smart Lane by Brett King

23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apollo 11, Apollo Guidance Computer, Apple II, artificial general intelligence, asset allocation, augmented reality, autonomous vehicles, barriers to entry, bitcoin, Bletchley Park, blockchain, Boston Dynamics, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, Computing Machinery and Intelligence, congestion charging, CRISPR, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, deskilling, different worldview, disruptive innovation, distributed generation, distributed ledger, double helix, drone strike, electricity market, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, fiat currency, financial exclusion, Flash crash, Flynn Effect, Ford Model T, future of work, gamification, Geoffrey Hinton, gig economy, gigafactory, Google Glasses, Google X / Alphabet X, Hans Lippershey, high-speed rail, Hyperloop, income inequality, industrial robot, information asymmetry, Internet of things, invention of movable type, invention of the printing press, invention of the telephone, invention of the wheel, James Dyson, Jeff Bezos, job automation, job-hopping, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, Kim Stanley Robinson, Kiva Systems, Kodak vs Instagram, Leonard Kleinrock, lifelogging, low earth orbit, low skilled workers, Lyft, M-Pesa, Mark Zuckerberg, Marshall McLuhan, megacity, Metcalfe’s law, Minecraft, mobile money, money market fund, more computing power than Apollo, Neal Stephenson, Neil Armstrong, Network effects, new economy, Nick Bostrom, obamacare, Occupy movement, Oculus Rift, off grid, off-the-grid, packet switching, pattern recognition, peer-to-peer, Ray Kurzweil, retail therapy, RFID, ride hailing / ride sharing, Robert Metcalfe, Salesforce, Satoshi Nakamoto, Second Machine Age, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart transportation, Snapchat, Snow Crash, social graph, software as a service, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, synthetic biology, systems thinking, TaskRabbit, technological singularity, TED Talk, telemarketer, telepresence, telepresence robot, Tesla Model S, The future is already here, The Future of Employment, Tim Cook: Apple, trade route, Travis Kalanick, TSMC, Turing complete, Turing test, Twitter Arab Spring, uber lyft, undersea cable, urban sprawl, V2 rocket, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, white picket fence, WikiLeaks, yottabyte

In about the same time that it has taken for the iPhone and smartphones to dominate every corner of society, we will see smart, autonomous cars exploding onto the scene. Business Insider has estimated that we’ll have 10 million cars with self-driving features on the road by 2020. The exponential curve of this technology means that there will be close to 100 million self-driving cars on the road just ten years after that. Figure 8.2: Business Intelligence projections on self-driving vehicles (Credit: BI) Within 15 years, we can expect that major cities and local authorities will be giving strong preferences to self-driving cars. Within 20 years, cities like London and New York won’t just have congestion charges, there will also be charges for traditional, human piloted vehicles to enter the city centres, or more probably even banning them from city streets.


pages: 343 words: 102,846

Trees on Mars: Our Obsession With the Future by Hal Niedzviecki

"World Economic Forum" Davos, Ada Lovelace, agricultural Revolution, Airbnb, Albert Einstein, Alvin Toffler, Amazon Robotics, anti-communist, big data - Walmart - Pop Tarts, big-box store, business intelligence, Charles Babbage, Colonization of Mars, computer age, crowdsourcing, data science, David Brooks, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, Flynn Effect, Ford Model T, Future Shock, Google Glasses, hive mind, Howard Zinn, if you build it, they will come, income inequality, independent contractor, Internet of things, invention of movable type, Jaron Lanier, Jeff Bezos, job automation, John von Neumann, knowledge economy, Kodak vs Instagram, life extension, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Marshall McLuhan, Neil Armstrong, One Laptop per Child (OLPC), Peter H. Diamandis: Planetary Resources, Peter Thiel, Pierre-Simon Laplace, Ponzi scheme, precariat, prediction markets, Ralph Nader, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, rising living standards, Robert Solow, Ronald Reagan, Salesforce, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, TaskRabbit, tech worker, technological singularity, technological solutionism, technoutopianism, Ted Kaczynski, TED Talk, Thomas L Friedman, Tyler Cowen, Uber and Lyft, uber lyft, Virgin Galactic, warehouse robotics, working poor

Now you can see what crimes have taken place, and what crimes might take place, so you are able to visualize key information spatially, allow the user to interact with information spatially, draw a circle around an area of interest, what crimes actually take place in this specific region, detailed reports on the crimes and their status.”5 The police department (PD) in Richmond, Virginia, is a pioneer in this kind of predictive policing approach. They’ve been applying the BI or business intelligence approach to policing since the early 2000s. By 2007, they were able to report, “Using predictive analysis and BI technology, we are applying information-based policy to predict the likelihood of crime and prevent future crimes from occurring. . . . Already, our officers have arrested 16 fugitives and confiscated 18 guns based on this system’s guidance.


pages: 427 words: 112,549

Freedom by Daniel Suarez

augmented reality, big-box store, British Empire, Burning Man, business intelligence, call centre, cloud computing, corporate personhood, digital map, game design, global supply chain, illegal immigration, Naomi Klein, new economy, Pearl River Delta, plutocrats, private military company, RFID, Shenzhen special economic zone , special economic zone, speech recognition, Stewart Brand, telemarketer, the scientific method, young professional

However, these young MBAs had no idea that they were really taking this meeting with him. They were bringing a problem that needed solving, even if they didn't realize it. They were the messengers. His firm would get the contract. It would be for an infrastructure security assessment or a market risk analysis, or something similar. Korr Business Intelligence Services did not advertise, and they did not submit proposals. They were the junior partners of a security consultant to the engineering department of a construction division of a real estate subsidiary of a financial group. They had no signage out front and no listing for their firm in the lobby directory.


pages: 696 words: 111,976

SQL Hacks by Andrew Cumming, Gordon Russell

Apollo 13, bioinformatics, book value, business intelligence, business logic, business process, database schema, en.wikipedia.org, Erdős number, Firefox, full text search, Hacker Conference 1984, Hacker Ethic, leftpad, Paul Erdős, SQL injection, Stewart Brand, web application

The wizard, shown in Figure 10-2, includes options to pull the source data from an ODBC source, and that includes pretty much every SQL product available. If you want to use this with MySQL, be sure to get the MySQL ODBC driver from http://dev.mysql.com/downloads/connector/odbc. Figure 10-2. PivotTable Wizard The wizard is intimidating at first, but with a little experimentation you can use it to produce useful business intelligence. The real value to this kind of report comes when the data is updated regularly and you can compare the most recent data with corresponding historical reports. This means that you have to produce the reports regularly; that is much easier if you automate the process. 10.1.1. Include Missing Values In Figure 10-1 you might notice that the location East is missing.


pages: 352 words: 104,411

Rush Hour: How 500 Million Commuters Survive the Daily Journey to Work by Iain Gately

Albert Einstein, Alvin Toffler, autonomous vehicles, Beeching cuts, blue-collar work, Boris Johnson, British Empire, business intelligence, business process, business process outsourcing, California high-speed rail, call centre, car-free, Cesare Marchetti: Marchetti’s constant, Clapham omnibus, cognitive dissonance, congestion charging, connected car, corporate raider, DARPA: Urban Challenge, Dean Kamen, decarbonisation, Deng Xiaoping, Detroit bankruptcy, don't be evil, driverless car, Elon Musk, extreme commuting, Ford Model T, General Motors Futurama, global pandemic, Google bus, Great Leap Forward, Henri Poincaré, high-speed rail, Hyperloop, Jeff Bezos, lateral thinking, Lewis Mumford, low skilled workers, Marchetti’s constant, planned obsolescence, postnationalism / post nation state, Ralph Waldo Emerson, remote working, safety bicycle, self-driving car, Silicon Valley, social distancing, SpaceShipOne, stakhanovite, Steve Jobs, Suez crisis 1956, telepresence, Tesla Model S, Traffic in Towns by Colin Buchanan, urban planning, éminence grise

Although the potential for lawsuits is dizzying, car-makers nonetheless bow to what they perceive to be their customers’ wants and compete to offer them better facilities for eating and drinking during their commutes. The automobile is now an official ‘Food and beverage venue’ in the opinion of Food and Drug Packaging, and home to a majority of the 4.4 billion ‘on-the-go’ ‘eating occasions’ that took place in the USA in 2008.*4 According to Datamonitor, the business intelligence provider, eating ‘is no longer considered a primary activity’ in the sense of being an end in itself, but rather is something we do while driving or working or watching television. Food on the go has become ‘ingrained in our lives’, and fast-food manufacturers have altered their products to suit the new Zeitgeist.


Reset by Ronald J. Deibert

23andMe, active measures, air gap, Airbnb, Amazon Web Services, Anthropocene, augmented reality, availability heuristic, behavioural economics, Bellingcat, Big Tech, bitcoin, blockchain, blood diamond, Brexit referendum, Buckminster Fuller, business intelligence, Cal Newport, call centre, Cambridge Analytica, carbon footprint, cashless society, Citizen Lab, clean water, cloud computing, computer vision, confounding variable, contact tracing, contact tracing app, content marketing, coronavirus, corporate social responsibility, COVID-19, crowdsourcing, data acquisition, data is the new oil, decarbonisation, deep learning, deepfake, Deng Xiaoping, disinformation, Donald Trump, Doomsday Clock, dual-use technology, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Evgeny Morozov, failed state, fake news, Future Shock, game design, gig economy, global pandemic, global supply chain, global village, Google Hangouts, Great Leap Forward, high-speed rail, income inequality, information retrieval, information security, Internet of things, Jaron Lanier, Jeff Bezos, John Markoff, Lewis Mumford, liberal capitalism, license plate recognition, lockdown, longitudinal study, Mark Zuckerberg, Marshall McLuhan, mass immigration, megastructure, meta-analysis, military-industrial complex, move fast and break things, Naomi Klein, natural language processing, New Journalism, NSO Group, off-the-grid, Peter Thiel, planetary scale, planned obsolescence, post-truth, proprietary trading, QAnon, ransomware, Robert Mercer, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, single source of truth, Skype, Snapchat, social distancing, sorting algorithm, source of truth, sovereign wealth fund, sparse data, speech recognition, Steve Bannon, Steve Jobs, Stuxnet, surveillance capitalism, techlash, technological solutionism, the long tail, the medium is the message, The Structural Transformation of the Public Sphere, TikTok, TSMC, undersea cable, unit 8200, Vannevar Bush, WikiLeaks, zero day, zero-sum game

Social media companies derive revenues by allowing third-party developers, applications, data brokers, and other services in their orbit to access their customer data, through either application programming interfaces (APIs) or other privately contracted data feeds. Analytic companies take the data harvested by the sensors of frontline companies, subject them to analysis, and then sell or trade business intelligence to advertisers and other companies. Orbiting further out are still other companies that provide the algorithms, software, techniques, and tradecraft used by the social media analytics firms. (Further out yet are those that supply the basic hardware, software, and energy required to keep it all operating.)


Reactive Messaging Patterns With the Actor Model: Applications and Integration in Scala and Akka by Vaughn Vernon

A Pattern Language, business intelligence, business logic, business process, cloud computing, cognitive dissonance, domain-specific language, en.wikipedia.org, fault tolerance, finite state, functional programming, Internet of things, Kickstarter, loose coupling, remote working, type inference, web application

Enterprise Applications The software applications needed by organizations to run their day-to-day operations are broad and varied. Depending on the kind of business, you can anticipate some of the required application software. Do any of the following application categories overlap with your enterprise? Accounting, Accounts (Financial and others), Aerospace Systems Design, Automated Trading, Banking, Budgeting, Business Intelligence, Business Process, Claims, Clinical, Collaboration, Communications, Computer-Aided Design (CAD), Content/Document Management, Customer Relationship Management, Electronic Health Record, Electronic Trading, Engineering, Enterprise Resource Planning, Finance, Healthcare Treatment, Human Resource Management, Identity and Access Management, Invoicing, Inventory, IT and Datacenter Management, Laboratory, Life Sciences, Maintenance, Manufacturing, Medical Diagnosis, Networking, Order Placement, Payroll, Pharmaceuticals, Publishing, Shipping, Project Management, Purchasing Support, Policy Management, Risk Assessment, Risk Management, Sales Forecasting, Scheduling and Appointment Management, Text Processing, Time Management, Transportation, Underwriting However incomplete the list, much of this software can be acquired as licensed commodities.


pages: 302 words: 82,233

Beautiful security by Andy Oram, John Viega

Albert Einstein, Amazon Web Services, An Inconvenient Truth, Bletchley Park, business intelligence, business process, call centre, cloud computing, corporate governance, credit crunch, crowdsourcing, defense in depth, do well by doing good, Donald Davies, en.wikipedia.org, fault tolerance, Firefox, information security, loose coupling, Marc Andreessen, market design, MITM: man-in-the-middle, Monroe Doctrine, new economy, Nicholas Carr, Nick Leeson, Norbert Wiener, operational security, optical character recognition, packet switching, peer-to-peer, performance metric, pirate software, Robert Bork, Search for Extraterrestrial Intelligence, security theater, SETI@home, Silicon Valley, Skype, software as a service, SQL injection, statistical model, Steven Levy, the long tail, The Wisdom of Crowds, Upton Sinclair, web application, web of trust, zero day, Zimmermann PGP

Participants can run benchmarks of service quality in relation to software cost, corporate vulnerabilities, and threat profiles. The potential scope is enormous, as shown through the MeMe map in Figure 9-1, taken from a security industry source. Extensible (”hackable”) and tailorable Customer service; long tails and idea viruses Decentralized connectivity Trading platform Business intelligence Business peer groups Aligned to business goals Platform that connects people, process, and technology Information security as a business discipline Useful business-oriented services Focus on the whole space (information) and not just IT security Facilitate and allow modern business models (outsourcing) Business process management Genuinely useful Cost-effective, scalable, and fair Social networking where specialists openly add value Community drives content Workflow Community determines form via demand Open architecture for connectivity Fun and serious (“Wears it all well”) “Anti-widget” Design and style matter FIGURE 9-1.


pages: 298 words: 43,745

Understanding Sponsored Search: Core Elements of Keyword Advertising by Jim Jansen

AltaVista, AOL-Time Warner, barriers to entry, behavioural economics, Black Swan, bounce rate, business intelligence, butterfly effect, call centre, Claude Shannon: information theory, complexity theory, content marketing, correlation does not imply causation, data science, en.wikipedia.org, first-price auction, folksonomy, Future Shock, information asymmetry, information retrieval, intangible asset, inventory management, life extension, linear programming, longitudinal study, machine translation, megacity, Nash equilibrium, Network effects, PageRank, place-making, power law, price mechanism, psychological pricing, random walk, Schrödinger's Cat, sealed-bid auction, search costs, search engine result page, second-price auction, second-price sealed-bid, sentiment analysis, social bookmarking, social web, software as a service, stochastic process, tacit knowledge, telemarketer, the market place, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Vickrey auction, Vilfredo Pareto, yield management

BTL can actually lead to a sale, and BTL promotions are highly measurable, giving marketers valuable insights into their return on investment (ROI) (Source: Wikipedia) (see Chapter BAM!). BtoB/B2B (Business-to-Business): businesses whose primary customers are other businesses (Source: IAB) (see Chapter 6 BAM!). BtoC/B2C (Business-to-Consumer): businesses whose primary customers are consumers (Source: IAB) (see Chapter 6 BAM!). Business intelligence: business practices that foster customer care, loyalty, and/or customer support. Also refers to a category of software and tools designed to gather, store, analyze, and deliver data in a user-friendly format to help organizations make more informed business decisions (Source: modified from WebTrends and IAB) (see Chapter 6 BAM!).


pages: 349 words: 114,038

Culture & Empire: Digital Revolution by Pieter Hintjens

4chan, Aaron Swartz, airport security, AltaVista, anti-communist, anti-pattern, barriers to entry, Bill Duvall, bitcoin, blockchain, Boeing 747, bread and circuses, business climate, business intelligence, business process, Chelsea Manning, clean water, commoditize, congestion charging, Corn Laws, correlation does not imply causation, cryptocurrency, Debian, decentralized internet, disinformation, Edward Snowden, failed state, financial independence, Firefox, full text search, gamification, German hyperinflation, global village, GnuPG, Google Chrome, greed is good, Hernando de Soto, hiring and firing, independent contractor, informal economy, intangible asset, invisible hand, it's over 9,000, James Watt: steam engine, Jeff Rulifson, Julian Assange, Kickstarter, Laura Poitras, M-Pesa, mass immigration, mass incarceration, mega-rich, military-industrial complex, MITM: man-in-the-middle, mutually assured destruction, Naomi Klein, national security letter, Nelson Mandela, new economy, New Urbanism, no silver bullet, Occupy movement, off-the-grid, offshore financial centre, packet switching, patent troll, peak oil, power law, pre–internet, private military company, race to the bottom, real-name policy, rent-seeking, reserve currency, RFC: Request For Comment, Richard Feynman, Richard Stallman, Ross Ulbricht, Russell Brand, Satoshi Nakamoto, security theater, selection bias, Skype, slashdot, software patent, spectrum auction, Steve Crocker, Steve Jobs, Steven Pinker, Stuxnet, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, trade route, transaction costs, twin studies, union organizing, wealth creators, web application, WikiLeaks, Y2K, zero day, Zipf's Law

According to Alex Constantine, author of "Mockingbird: The Subversion Of The Free Press By The CIA", in the 1950s, "some 3,000 salaried and contract CIA employees were eventually engaged in propaganda efforts." Officially, the program was ended in 1976 by incoming CIA Director George H. W. Bush. Inserting teams into existing media companies is one strategy. Another is to create your own business intelligence groups from the ground up. This is how large firms promote legislation, by funding "industry round tables" and "researchers" who push a pre-agreed message. The Spider has undoubtedly invested in many businesses, from armaments to drugs, and media. It's both profitable and convenient. Take as example The Economist, a respected and influential newspaper that was, ironically founded at the height of the patent debate in Britain as an anti-patent free-trade voice.


pages: 390 words: 115,303

Catch and Kill: Lies, Spies, and a Conspiracy to Protect Predators by Ronan Farrow

Airbnb, Bernie Sanders, Black Lives Matter, business intelligence, Citizen Lab, crowdsourcing, David Strachan, Donald Trump, East Village, fake news, forensic accounting, Jeff Bezos, Jeffrey Epstein, Live Aid, messenger bag, NSO Group, Peter Thiel, Plato's cave, Saturday Night Live, Seymour Hersh, Skype

CHAPTER 46 1 a colorful profile: Miriam Shaviv, “IDF Vet Turned Author Teases UK with Mossad Alter Ego,” Times of Israel, February 8, 2013. 2 Dead Cat Bounce: Seth Freedman, Dead Cat Bounce (United Kingdom, London: Cutting Edge Press, 2013), loc 17 of 3658, Kindle. 3 who conducted the interviews, and why: Mark Townsend, “Rose McGowan: “Hollywood Blacklisted Me Because I Got Raped,” Guardian, October 14, 2017. 4 a ready pipeline of trained operatives: Adam Entous and Ronan Farrow, “Private Massod for Hire,” The New Yorker, February 11, 2019. 5 veterans of a secret Israeli intelligence unit: Haaretz staff, “Ex-Mossad Chief Ephraim Halevy Joins Spy Firm Black Cube,” Haaretz, November 11, 2018. 6 once pitched Black Cube’s services: Adam Entous and Ronan Farrow, “Private Massod for Hire,” The New Yorker, February 11, 2019. 7 more than 100 operatives, speaking 30 languages: Yuval Hirshorn, “Inside Black Cube—the ‘Mossad’ of the Business World,” Forbes Israel, June 9, 2018. 8 “the exclusive supplier to major organizations and government ministries”: Hadas Magen, “Black Cube—a “Mossad-style” Business Intelligence Co,” Globes, April 2, 2017. 9 “new information concerning the HW&BC affair”: Email from Sleeper1973, October 31, 2017. CHAPTER 47 1 That last arrangement: Agreement between Boies Schiller Flexner LLP and Black Cube, July 11, 2017. 2 two Black Cube operatives were jailed in Romania: Yuval Hirshorn, “Inside Black Cube—the ‘Mossad’ of the Business World,” Forbes Israel, June 9, 2018. 3 “it’s complicated”: Email from David Boies to Ronan Farrow, November 4, 2017. 4 “via a girl named ‘Ana’”: Email from Sleeper1973 to Ronan Farrow, November 1, 2017.


pages: 395 words: 110,994

The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win by Gene Kim, Kevin Behr, George Spafford

air freight, anti-work, antiwork, Apollo 13, business intelligence, business process, centre right, cloud computing, continuous integration, dark matter, database schema, DevOps, fail fast, friendly fire, Gene Kranz, index card, information security, inventory management, Kanban, Lean Startup, shareholder value, systems thinking, Toyota Production System

Chris and William also get up and follow him. I meet them halfway. “Well?” I ask. “Well,” Wes replies when he’s close enough to speak softly and be heard. “We’ve found our smoking gun. We just discovered that Brent made a change to the production database a couple of weeks ago to support a Phoenix business intelligence module. No one knew about it, let alone documented it. It conflicts with some of the Phoenix database changes, so Chris’ guys are going to need to do some recoding.” “Shit,” I say. “Wait. Which Phoenix module?” “It’s one of Sarah’s projects that we released after the project freeze was lifted,” he replies.


pages: 409 words: 112,055

The Fifth Domain: Defending Our Country, Our Companies, and Ourselves in the Age of Cyber Threats by Richard A. Clarke, Robert K. Knake

"World Economic Forum" Davos, A Declaration of the Independence of Cyberspace, Affordable Care Act / Obamacare, air gap, Airbnb, Albert Einstein, Amazon Web Services, autonomous vehicles, barriers to entry, bitcoin, Black Lives Matter, Black Swan, blockchain, Boeing 737 MAX, borderless world, Boston Dynamics, business cycle, business intelligence, call centre, Cass Sunstein, cloud computing, cognitive bias, commoditize, computer vision, corporate governance, cryptocurrency, data acquisition, data science, deep learning, DevOps, disinformation, don't be evil, Donald Trump, Dr. Strangelove, driverless car, Edward Snowden, Exxon Valdez, false flag, geopolitical risk, global village, immigration reform, information security, Infrastructure as a Service, Internet of things, Jeff Bezos, John Perry Barlow, Julian Assange, Kubernetes, machine readable, Marc Benioff, Mark Zuckerberg, Metcalfe’s law, MITM: man-in-the-middle, Morris worm, move fast and break things, Network effects, open borders, platform as a service, Ponzi scheme, quantum cryptography, ransomware, Richard Thaler, Salesforce, Sand Hill Road, Schrödinger's Cat, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, Snapchat, software as a service, Steven Levy, Stuxnet, technoutopianism, The future is already here, Tim Cook: Apple, undersea cable, unit 8200, WikiLeaks, Y2K, zero day

Cyber War Risk Insurance Act (CWRIA): A proposal made in this book for a Cyber War Risk Insurance Act modeled along the lines of an existing government program to backstop commercial insurance in the event of a major terrorist attack. Data Lake: A virtual repository in which current and perhaps past data is stored. The information contained within a data lake can be queried and is often useful for business intelligence or analytical purposes. Defense Advanced Research Projects Agency (DARPA): A U.S. Defense Department office that funds university and laboratory investigations and experiments into new concepts, and known, inter alia, for funding the research that led to the creation of the internet. Defense Industrial Base (DIB): Those privately owned and operated corporations that manufacture weapons and supporting systems utilized by the armed forces.


pages: 444 words: 118,393

The Nature of Software Development: Keep It Simple, Make It Valuable, Build It Piece by Piece by Ron Jeffries

Amazon Web Services, anti-pattern, bitcoin, business cycle, business intelligence, business logic, business process, c2.com, call centre, cloud computing, continuous integration, Conway's law, creative destruction, dark matter, data science, database schema, deep learning, DevOps, disinformation, duck typing, en.wikipedia.org, fail fast, fault tolerance, Firefox, Hacker News, industrial robot, information security, Infrastructure as a Service, Internet of things, Jeff Bezos, Kanban, Kubernetes, load shedding, loose coupling, machine readable, Mars Rover, microservices, Minecraft, minimum viable product, MITM: man-in-the-middle, Morris worm, move fast and break things, OSI model, peer-to-peer lending, platform as a service, power law, ransomware, revision control, Ruby on Rails, Schrödinger's Cat, Silicon Valley, six sigma, software is eating the world, source of truth, SQL injection, systems thinking, text mining, time value of money, transaction costs, Turing machine, two-pizza team, web application, zero day

The “technical” perspective may even be split into “development” and “operations.” Most of the time, these constituencies look at different measurements collected by different means. Imagine the difficulty in planning when marketing uses tracking bugs on web pages, sales uses conversions reported in a business intelligence tool, operations analyzes log files in Splunk, and development uses blind hope and intuition. Could this crew ever agree on how the system is doing? It’d be much better to integrate the information so all parties can see the same data through similar interfaces. Different constituencies require different perspectives.


pages: 410 words: 119,823

Radical Technologies: The Design of Everyday Life by Adam Greenfield

3D printing, Airbnb, algorithmic bias, algorithmic management, AlphaGo, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, Black Lives Matter, blockchain, Boston Dynamics, business intelligence, business process, Californian Ideology, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, circular economy, cloud computing, Cody Wilson, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, CRISPR, cryptocurrency, David Graeber, deep learning, DeepMind, dematerialisation, digital map, disruptive innovation, distributed ledger, driverless car, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, fulfillment center, gentrification, global supply chain, global village, Goodhart's law, Google Glasses, Herman Kahn, Ian Bogost, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, Jacob Silverman, James Watt: steam engine, Jane Jacobs, Jeff Bezos, Jeff Hawkins, job automation, jobs below the API, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, Kiva Systems, late capitalism, Leo Hollis, license plate recognition, lifelogging, M-Pesa, Mark Zuckerberg, means of production, megacity, megastructure, minimum viable product, money: store of value / unit of account / medium of exchange, natural language processing, Network effects, New Urbanism, Nick Bostrom, Occupy movement, Oculus Rift, off-the-grid, PalmPilot, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, printed gun, proprietary trading, RAND corporation, recommendation engine, RFID, rolodex, Rutger Bregman, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, Shenzhen special economic zone , Sidewalk Labs, Silicon Valley, smart cities, smart contracts, social intelligence, sorting algorithm, special economic zone, speech recognition, stakhanovite, statistical model, stem cell, technoutopianism, Tesla Model S, the built environment, The Death and Life of Great American Cities, The Future of Employment, Tony Fadell, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, vertical integration, Vitalik Buterin, warehouse robotics, When a measure becomes a target, Whole Earth Review, WikiLeaks, women in the workforce

Even if the car hadn’t been booked on the corporate account, it is also equipped with GPS, and that unit’s accuracy buffer has been set such that it correctly identifies the location at the moment it pulls up to the curb, and tags the booking with the name of the house the executive works for. Here can be gleaned solid, actionable business intelligence: both the cycling of particular enterprises and sectors of the economy, and by extension possibly even some insight into the rhythms of something as inchoate and hard to grasp as taste. What might we learn from the American backpacker? The pedometer app on his phone is sophisticated enough to understand his pause of eleven minutes in a location in the Rue de Rivoli as a visit to the WHSmith bookstore, but other facets of his activity through the day slip through holes in the mesh.


pages: 480 words: 122,663

The Art of SQL by Stephane Faroult, Peter Robson

business intelligence, business logic, business process, constrained optimization, continuation of politics by other means, database schema, full text search, invisible hand, it's over 9,000, leftpad, SQL injection, technological determinism

TFP_MNE" ELSE NULL END ) || CASE WHEN 'Y' = 'Y' THEN TO_CHAR ( TRUNC ( t2."ACC_PCI" ) ) ELSE NULL END ) || CASE WHEN 'N' = 'Y' THEN t2."ACC_E2K" ELSE NULL END ) || CASE WHEN 'N' = 'Y' THEN t2."ACC_EXT" ELSE NULL END ) || CASE ... It seems obvious from this sample's select list that at least some "business intelligence" tools invest so much intelligence on the business side that they have nothing left for generating SQL queries. And when the where clause ceases to be trivial—forget about it! Declaring that it is better to avoid joins for performance reasons is quite sensible in this context. Actually, the nearer you are to the "text search in a file" (a.k.a. grep) model, the better.


pages: 468 words: 124,573

How to Build a Billion Dollar App: Discover the Secrets of the Most Successful Entrepreneurs of Our Time by George Berkowski

Airbnb, Amazon Web Services, Andy Rubin, barriers to entry, Black Swan, business intelligence, call centre, crowdsourcing, deal flow, Dennis Tito, disruptive innovation, Dunbar number, en.wikipedia.org, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, growth hacking, iterative process, Jeff Bezos, Jony Ive, Kickstarter, knowledge worker, Lean Startup, loose coupling, Marc Andreessen, Mark Zuckerberg, Mary Meeker, minimum viable product, MITM: man-in-the-middle, move fast and break things, Network effects, Oculus Rift, Paul Graham, QR code, Ruby on Rails, Salesforce, self-driving car, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, SoftBank, software as a service, software is eating the world, Steve Jobs, Steven Levy, subscription business, TechCrunch disrupt, Travis Kalanick, two-pizza team, ubercab, Y Combinator

As you drive towards becoming a robust, revenue-focused company, you need to ensure that you’re making more decisions based on data; you need to take advantage of all the information that you have at hand. While your off-the-shelf analytics solutions are pretty powerful, you will invariably hit a number of limitations. There’s a good chance these limitations are going to become very annoying and will start to affect business decisions. Business intelligence tools such as QlikView give you the ability to generate insight from all the data you have collected on the back-end and pull it quickly into dashboards and reports. Think of it as one level more powerful than your daily analytics tools, and a lot more user-friendly than manipulating raw data with SQL queries (SQL is a special programing language for managing and interrogating data that is stored in relational databases) – and yes, that’s meant to sound hard.


pages: 540 words: 119,731

Samsung Rising: The Inside Story of the South Korean Giant That Set Out to Beat Apple and Conquer Tech by Geoffrey Cain

Andy Rubin, Apple's 1984 Super Bowl advert, Asian financial crisis, autonomous vehicles, Berlin Wall, business intelligence, cloud computing, corporate governance, creative destruction, don't be evil, Donald Trump, double helix, Dynabook, Elon Musk, Fairchild Semiconductor, fake news, fear of failure, Hacker News, independent contractor, Internet of things, John Markoff, Jony Ive, Kickstarter, Mahatma Gandhi, Mark Zuckerberg, megacity, Mikhail Gorbachev, Nelson Mandela, patent troll, Pepsi Challenge, rolodex, Russell Brand, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Superbowl ad, Tim Cook: Apple, Tony Fadell, too big to fail, WikiLeaks, wikimedia commons

His former colleagues would repeat those words, with either favor or disdain: “Eric was an engineer.” But he also had degrees in physics and business from California’s Harvey Mudd College and Harvard Business School. Before Samsung, he’d been CTO of the Dun & Bradstreet Corporation, a firm that crunched data on people’s credit history, and CEO of Pilot Software, a business intelligence vendor. “Now, the CEO wanted me to unify the entire brand under one umbrella,” Eric said. “Samsung’s brand was scattered. Each regional office was doing its own thing. If we wanted to become a global company, we needed to direct these efforts from headquarters.” Samsung needed one giant marketing cannon with which to blast its messages out; right now, the brand sprayed its messages more like a BB gun, spewing little metallic balls one after another, without overarching purpose, direction, and vision.


pages: 509 words: 132,327

Rise of the Machines: A Cybernetic History by Thomas Rid

1960s counterculture, A Declaration of the Independence of Cyberspace, agricultural Revolution, Albert Einstein, Alistair Cooke, Alvin Toffler, Apple II, Apple's 1984 Super Bowl advert, back-to-the-land, Berlin Wall, Bletchley Park, British Empire, Brownian motion, Buckminster Fuller, business intelligence, Charles Babbage, Charles Lindbergh, Claude Shannon: information theory, conceptual framework, connected car, domain-specific language, Douglas Engelbart, Douglas Engelbart, Dr. Strangelove, dumpster diving, Extropian, full employment, game design, global village, Hacker News, Haight Ashbury, Herman Kahn, Howard Rheingold, Ivan Sutherland, Jaron Lanier, job automation, John Gilmore, John Markoff, John Perry Barlow, John von Neumann, Kevin Kelly, Kubernetes, Marshall McLuhan, Menlo Park, military-industrial complex, Mitch Kapor, Mondo 2000, Morris worm, Mother of all demos, Neal Stephenson, new economy, New Journalism, Norbert Wiener, offshore financial centre, oil shale / tar sands, Oklahoma City bombing, operational security, pattern recognition, public intellectual, RAND corporation, Silicon Valley, Simon Singh, Snow Crash, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, systems thinking, technoutopianism, Telecommunications Act of 1996, telepresence, The Hackers Conference, Timothy McVeigh, Vernor Vinge, We are as Gods, Whole Earth Catalog, Whole Earth Review, Y2K, Yom Kippur War, Zimmermann PGP

BlackNet was specifically interested in buying trade secrets (“semiconductors”); production methods in nanotechnology (“the Merkle sleeve bearing”); chemical manufacturing (“fullerines and protein folding”); and design plans for things ranging from children’s toys to cruise missiles (“3DO”). Oh, and BlackNet was also interested in general business intelligence—for instance, on mergers and buyouts. “Join us in this revolutionary—and profitable—venture,” the message concluded.62 May soon claimed credit for the letter. He had come up with BlackNet in the summer of 1993, as an example of what could be done, “an exercise in guerrilla ontology,” as he called it.63 The goal was to find a way to ensure fully anonymous, untraceable, two-way exchanges of information.


Designing Interfaces by Jenifer Tidwell

A Pattern Language, business intelligence, cognitive load, crowdsourcing, Firefox, longitudinal study, school vouchers, seminal paper, social software, social web, sorting algorithm, the long tail, Tony Hsieh, web application

Fitbit What Arrange data displays into a single information-dense page, updated regularly. Show users relevant, actionable information, and let them customize the display as necessary. Use when Your site or application deals with an incoming flow of information from something—web server data, social chatter, news, airline flights, business intelligence information, or financials, for example. Your users would benefit from continuous monitoring of that information. Why This is a familiar and recognizable page style. Dashboards have a long history, both online and in the physical world, and people have well-established expectations about how they work: they show useful information, they update themselves, they usually use graphics to display data, and so on.


pages: 398 words: 31,161

Gnuplot in Action: Understanding Data With Graphs by Philipp Janert

bioinformatics, business intelligence, Debian, general-purpose programming language, iterative process, mandelbrot fractal, pattern recognition, power law, random walk, Richard Stallman, six sigma, sparse data, survivorship bias

I first started using gnuplot when I was a graduate student, and it has become an indispensable part of my toolbox: one of the handful of programs I can’t do without. Recently, I’ve also started to contribute a few features to the gnuplot development version. I provide consulting services specializing in corporate metrics, business intelligence, data analysis, and mathematical modeling through my company, Principal Value, LLC (www.principal-value.com). I also teach classes on software design and data analysis at the University of Washington. I hold a Ph.D. in theoretical physics from the University of Washington. Author online Purchase of Gnuplot in Action includes free access to a private web forum run by Manning Publications where you can make comments about the book, ask technical questions, and receive help from the author and from other users.


pages: 460 words: 131,579

Masters of Management: How the Business Gurus and Their Ideas Have Changed the World—for Better and for Worse by Adrian Wooldridge

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, affirmative action, Alan Greenspan, barriers to entry, behavioural economics, Black Swan, blood diamond, borderless world, business climate, business cycle, business intelligence, business process, carbon footprint, Cass Sunstein, Clayton Christensen, clean tech, cloud computing, collaborative consumption, collapse of Lehman Brothers, collateralized debt obligation, commoditize, company town, corporate governance, corporate social responsibility, creative destruction, credit crunch, crowdsourcing, David Brooks, David Ricardo: comparative advantage, disintermediation, disruptive innovation, do well by doing good, don't be evil, Donald Trump, Edward Glaeser, Exxon Valdez, financial deregulation, Ford Model T, Frederick Winslow Taylor, future of work, George Gilder, global supply chain, Golden arches theory, hobby farmer, industrial cluster, intangible asset, It's morning again in America, job satisfaction, job-hopping, joint-stock company, Joseph Schumpeter, junk bonds, Just-in-time delivery, Kickstarter, knowledge economy, knowledge worker, lake wobegon effect, Long Term Capital Management, low skilled workers, Mark Zuckerberg, McMansion, means of production, Menlo Park, meritocracy, Michael Milken, military-industrial complex, mobile money, Naomi Klein, Netflix Prize, Network effects, new economy, Nick Leeson, Norman Macrae, open immigration, patent troll, Ponzi scheme, popular capitalism, post-industrial society, profit motive, purchasing power parity, radical decentralization, Ralph Nader, recommendation engine, Richard Florida, Richard Thaler, risk tolerance, Ronald Reagan, science of happiness, scientific management, shareholder value, Silicon Valley, Silicon Valley startup, Skype, Social Responsibility of Business Is to Increase Its Profits, Steve Jobs, Steven Levy, supply-chain management, tacit knowledge, technoutopianism, the long tail, The Soul of a New Machine, The Wealth of Nations by Adam Smith, Thomas Davenport, Tony Hsieh, too big to fail, vertical integration, wealth creators, women in the workforce, young professional, Zipcar

As Clive Crook, a former colleague of mine at The Economist, has put it, “CSR is the tribute that capitalism everywhere pays to virtue.”6 This tribute to virtue is paid in gold as well as hot air. No major consultancy is complete without a CSR practice. Some consultancies sell nothing but CSR: a group called the Ethical Corporation provides “business intelligence” on CSR to more than three thousand multinational companies, publishes a CSR-themed magazine and website, puts on a huge conference every year, and compiles an ever-expanding library of case studies on corporate irresponsibility, including studies of Exxon Valdez, Toyota, and McDonald’s.7 There are CSR performance indexes (such as the Dow Jones Sustainability Index); CSR professorships (more than half of U.S.


pages: 567 words: 122,311

Lean Analytics: Use Data to Build a Better Startup Faster by Alistair Croll, Benjamin Yoskovitz

Airbnb, Amazon Mechanical Turk, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, barriers to entry, Bay Area Rapid Transit, Ben Horowitz, bounce rate, business intelligence, call centre, cloud computing, cognitive bias, commoditize, constrained optimization, data science, digital rights, en.wikipedia.org, Firefox, Frederick Winslow Taylor, frictionless, frictionless market, game design, gamification, Google X / Alphabet X, growth hacking, hockey-stick growth, Infrastructure as a Service, Internet of things, inventory management, Kickstarter, lateral thinking, Lean Startup, lifelogging, longitudinal study, Marshall McLuhan, minimum viable product, Network effects, PalmPilot, pattern recognition, Paul Graham, performance metric, place-making, platform as a service, power law, price elasticity of demand, reality distortion field, recommendation engine, ride hailing / ride sharing, rolodex, Salesforce, sentiment analysis, skunkworks, Skype, social graph, social software, software as a service, Steve Jobs, subscription business, telemarketer, the long tail, transaction costs, two-sided market, Uber for X, web application, Y Combinator

When everyone rallies around the OMTM and is given the opportunity to experiment independently to improve it, it’s a powerful force. Solare Focuses on a Few Key Metrics Solare Ristorante is an Italian restaurant in San Diego owned by serial entrepreneur Randy Smerik. Randy has a background in technology and data, once served as the general manager for business intelligence firm Teradata, and has five technology exits under his belt. It’s no surprise that he’s brought his data-driven mindset to the way he runs the business. One evening at the restaurant, Randy’s son Tommy—who manages the bar—yelled out, “24!” Since we’re always looking for stories about business metrics, we asked him what the number meant.


Super Continent: The Logic of Eurasian Integration by Kent E. Calder

"World Economic Forum" Davos, 3D printing, air freight, Asian financial crisis, Bear Stearns, Berlin Wall, blockchain, Bretton Woods, business intelligence, capital controls, Capital in the Twenty-First Century by Thomas Piketty, classic study, cloud computing, colonial rule, Credit Default Swap, cuban missile crisis, deindustrialization, demographic transition, Deng Xiaoping, disruptive innovation, Doha Development Round, Donald Trump, energy transition, European colonialism, export processing zone, failed state, Fall of the Berlin Wall, foreign exchange controls, geopolitical risk, Gini coefficient, high-speed rail, housing crisis, income inequality, industrial cluster, industrial robot, interest rate swap, intermodal, Internet of things, invention of movable type, inventory management, John Markoff, liberal world order, Malacca Straits, Mikhail Gorbachev, mittelstand, money market fund, moral hazard, new economy, oil shale / tar sands, oil shock, purchasing power parity, quantitative easing, reserve currency, Ronald Reagan, seigniorage, Shenzhen special economic zone , smart cities, smart grid, SoftBank, South China Sea, sovereign wealth fund, special drawing rights, special economic zone, Suez canal 1869, Suez crisis 1956, supply-chain management, Thomas L Friedman, trade liberalization, trade route, transcontinental railway, UNCLOS, UNCLOS, union organizing, Washington Consensus, working-age population, zero-sum game

Society for Worldwide Interbank Financial Telecommunication (SWIFT), “RMB Tracker November 2011,” November 25, 2011; “RMB Tracker January 2014,” Janu- Notes to Chapter 10 303 ary 23, 2014; “RMB Tracker January 2015,” January 28, 2015, https://​www​.swift​.com/ ​our​-solutions/​compliance​-and​-shared​-services/​business​-intelligence/​renminbi /​r mb​ -tracker. 45. The October 2016 weights for the five basket currencies were US dollar (41.73 percent); euro (30.93 percent); Chinese RMB (10.92 percent); Japanese yen (8.33 percent); and British pound sterling (8.09 percent). See International Monetary Fund, “IMF Adds Chinese Renminbi to Special Drawing Rights Basket,” IMF News, September 30, 2016, http://​www​.imf​.org/​en/​News/​Articles/​2016/​09/​29/​AM16​-NA093016IMF​-Adds​ -Chinese​-Renminbi​-to​-Special​-Drawing​-Rights​-Basket. 46.


Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Valliappa Lakshmanan, Sara Robinson, Michael Munn

A Pattern Language, Airbnb, algorithmic trading, automated trading system, business intelligence, business logic, business process, combinatorial explosion, computer vision, continuous integration, COVID-19, data science, deep learning, DevOps, discrete time, en.wikipedia.org, Hacker News, industrial research laboratory, iterative process, Kubernetes, machine translation, microservices, mobile money, natural language processing, Netflix Prize, optical character recognition, pattern recognition, performance metric, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, sentiment analysis, speech recognition, statistical model, the payments system, web application

Unlike the other roles discussed here, research scientists spend most of their time prototyping and evaluating new approaches to ML, rather than building out production ML systems. Data analysts evaluate and gather insights from data, then summarize these insights for other teams within their organization. They tend to work in SQL and spreadsheets, and use business intelligence tools to create data visualizations to share their findings. Data analysts work closely with product teams to understand how their insights can help address business problems and create value. While data analysts focus on identifying trends in existing data and deriving insights from it, data scientists are concerned with using that data to generate future predictions and in automating or scaling out the generation of insights.


pages: 497 words: 144,283

Connectography: Mapping the Future of Global Civilization by Parag Khanna

"World Economic Forum" Davos, 1919 Motor Transport Corps convoy, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 9 dash line, additive manufacturing, Admiral Zheng, affirmative action, agricultural Revolution, Airbnb, Albert Einstein, amateurs talk tactics, professionals talk logistics, Amazon Mechanical Turk, Anthropocene, Asian financial crisis, asset allocation, autonomous vehicles, banking crisis, Basel III, Berlin Wall, bitcoin, Black Swan, blockchain, borderless world, Boycotts of Israel, Branko Milanovic, BRICs, British Empire, business intelligence, call centre, capital controls, Carl Icahn, charter city, circular economy, clean water, cloud computing, collateralized debt obligation, commoditize, complexity theory, continuation of politics by other means, corporate governance, corporate social responsibility, credit crunch, crony capitalism, crowdsourcing, cryptocurrency, cuban missile crisis, data is the new oil, David Ricardo: comparative advantage, deglobalization, deindustrialization, dematerialisation, Deng Xiaoping, Detroit bankruptcy, digital capitalism, digital divide, digital map, disruptive innovation, diversification, Doha Development Round, driverless car, Easter island, edge city, Edward Snowden, Elon Musk, energy security, Ethereum, ethereum blockchain, European colonialism, eurozone crisis, export processing zone, failed state, Fairphone, Fall of the Berlin Wall, family office, Ferguson, Missouri, financial innovation, financial repression, fixed income, forward guidance, gentrification, geopolitical risk, global supply chain, global value chain, global village, Google Earth, Great Leap Forward, Hernando de Soto, high net worth, high-speed rail, Hyperloop, ice-free Arctic, if you build it, they will come, illegal immigration, income inequality, income per capita, industrial cluster, industrial robot, informal economy, Infrastructure as a Service, interest rate swap, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, Jane Jacobs, Jaron Lanier, John von Neumann, Julian Assange, Just-in-time delivery, Kevin Kelly, Khyber Pass, Kibera, Kickstarter, LNG terminal, low cost airline, low earth orbit, low interest rates, manufacturing employment, mass affluent, mass immigration, megacity, Mercator projection, Metcalfe’s law, microcredit, middle-income trap, mittelstand, Monroe Doctrine, Multics, mutually assured destruction, Neal Stephenson, New Economic Geography, new economy, New Urbanism, off grid, offshore financial centre, oil rush, oil shale / tar sands, oil shock, openstreetmap, out of africa, Panamax, Parag Khanna, Peace of Westphalia, peak oil, Pearl River Delta, Peter Thiel, Philip Mirowski, Planet Labs, plutocrats, post-oil, post-Panamax, precautionary principle, private military company, purchasing power parity, quantum entanglement, Quicken Loans, QWERTY keyboard, race to the bottom, Rana Plaza, rent-seeking, reserve currency, Robert Gordon, Robert Shiller, Robert Solow, rolling blackouts, Ronald Coase, Scramble for Africa, Second Machine Age, sharing economy, Shenzhen special economic zone , Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, six sigma, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, South China Sea, South Sea Bubble, sovereign wealth fund, special economic zone, spice trade, Stuxnet, supply-chain management, sustainable-tourism, systems thinking, TaskRabbit, tech worker, TED Talk, telepresence, the built environment, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, Tim Cook: Apple, trade route, Tragedy of the Commons, transaction costs, Tyler Cowen, UNCLOS, uranium enrichment, urban planning, urban sprawl, vertical integration, WikiLeaks, Yochai Benkler, young professional, zero day

ESRI MAPPING CENTER http://mappingcenter.​esri.​com/​index.​cfm?​fa=​resources.​cartoFavorites Esri’s Mapping Center provides access to various resources that are used regularly by professional mapmakers and cartographers, enabling its users to create maps using ArcGIS. FIRST MILE GEO https://www.​firstmilegeo.​com/ First Mile Geo is a business intelligence software that enables users to collect, visualize, and monitor data collected online or off-line through mobile, SMS, surveys, or manual sources. Maps, dashboards, indices, and alerts can be generated in multiple languages. FLEETMON http://www.​fleetmon.​com/​live_tracking/​fleetmon_explorer FleetMon is an open database of ships and ports worldwide that uses real-time AIS positioning data to visualize the location and movement of nearly 500,000 vessels, allowing for the analysis of shipping and trade patterns.


pages: 535 words: 149,752

After Steve: How Apple Became a Trillion-Dollar Company and Lost Its Soul by Tripp Mickle

"World Economic Forum" Davos, Airbnb, airport security, Apple II, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, banking crisis, Boeing 747, British Empire, business intelligence, Carl Icahn, Clayton Christensen, commoditize, coronavirus, corporate raider, COVID-19, desegregation, digital map, disruptive innovation, Donald Trump, Downton Abbey, driverless car, Edward Snowden, Elon Musk, Frank Gehry, General Magic , global pandemic, global supply chain, haute couture, imposter syndrome, index fund, Internet Archive, inventory management, invisible hand, John Markoff, Jony Ive, Kickstarter, Larry Ellison, lateral thinking, Mark Zuckerberg, market design, megacity, Murano, Venice glass, Ralph Waldo Emerson, self-driving car, Sheryl Sandberg, Silicon Valley, skeuomorphism, Stephen Fry, Steve Jobs, Steve Wozniak, Steven Levy, stock buybacks, Superbowl ad, supply-chain management, thinkpad, Tim Cook: Apple, Tony Fadell, Travis Kalanick, turn-by-turn navigation, Wayback Machine, WikiLeaks, Y2K

As much as anything else, he saw a troubled business in need of a fix, a challenge that he’d have the authority to address. So he took the job based in Denver, his first home outside the Southeast. He and Coffey, who joined around the same time, went to work identifying cost reductions to turn around a distressed business. Intelligent Electronics had more warehouses than it needed and different software systems for customer orders. It had grown fast by acquiring rivals but had not integrated those businesses well. At the time, some customers wanted Intelligent Electronics to fulfill a single order with networking equipment from Cisco and computers from IBM, but the equipment was often spread across four warehouses in Denver.


pages: 584 words: 149,387

Essential Scrum: A Practical Guide to the Most Popular Agile Process by Kenneth S. Rubin

bioinformatics, business cycle, business intelligence, business logic, business process, continuous integration, corporate governance, fail fast, hiring and firing, index card, inventory management, iterative process, Kanban, Lean Startup, loose coupling, minimum viable product, performance metric, shareholder value, six sigma, tacit knowledge, Y2K, you are the product

The extent of that responsibility would depend on the organization, the specific product, and the skills of the person selected to be the product owner. For example, an organization that is producing a simple unit-conversion application for sale in a mobile device app store will not require as many activities as an organization creating the next major release of its enterprise Business Intelligence product. Therefore, it isn’t practical to universally define the extent of the product owner’s responsibility relative to the Pragmatic Marketing framework. As I will discuss shortly, there are times when the scope of the product owner activities may be too large for any one person to adequately perform.


pages: 504 words: 89,238

Natural language processing with Python by Steven Bird, Ewan Klein, Edward Loper

bioinformatics, business intelligence, business logic, Computing Machinery and Intelligence, conceptual framework, Donald Knuth, duck typing, elephant in my pajamas, en.wikipedia.org, finite state, Firefox, functional programming, Guido van Rossum, higher-order functions, information retrieval, language acquisition, lolcat, machine translation, Menlo Park, natural language processing, P = NP, search inside the book, sparse data, speech recognition, statistical model, text mining, Turing test, W. E. B. Du Bois

Rather than trying to use text like (1) to answer the question directly, we first convert the unstructured data of natural language sentences into the structured data of Table 7-1. Then we reap the benefits of powerful query tools such as SQL. This method of getting meaning from text is called Information Extraction. Information Extraction has many applications, including business intelligence, resume harvesting, media analysis, sentiment detection, patent search, and email scanning. A particularly important area of current research involves the attempt to extract 262 | Chapter 7: Extracting Information from Text structured data out of electronically available scientific literature, especially in the domain of biology and medicine.


pages: 680 words: 157,865

Beautiful Architecture: Leading Thinkers Reveal the Hidden Beauty in Software Design by Diomidis Spinellis, Georgios Gousios

Albert Einstein, barriers to entry, business intelligence, business logic, business process, call centre, continuous integration, corporate governance, database schema, Debian, domain-specific language, don't repeat yourself, Donald Knuth, duck typing, en.wikipedia.org, fail fast, fault tolerance, financial engineering, Firefox, Free Software Foundation, functional programming, general-purpose programming language, higher-order functions, iterative process, linked data, locality of reference, loose coupling, meta-analysis, MVC pattern, Neal Stephenson, no silver bullet, peer-to-peer, premature optimization, recommendation engine, Richard Stallman, Ruby on Rails, semantic web, smart cities, social graph, social web, SPARQL, Steve Jobs, Stewart Brand, Strategic Defense Initiative, systems thinking, the Cathedral and the Bazaar, traveling salesman, Turing complete, type inference, web application, zero-coupon bond

* * * [23] http://restlet.org Data-Driven Applications Once an organization has gone to the trouble of making its data addressable, there are additional benefits beyond enabling the backend systems to cache results and migrate to new technologies in unobtrusive ways. Specifically, we can introduce entirely new classes of data-driven applications and integration strategies. When we can name our data and ask for it in application-friendly ways, we facilitate a level of exploration, business intelligence, and knowledge management that will make most analysts drool when they see it. The Simile Project,[24] a joint effort between the W3C and the MIT CSAIL group, has produced a tremendous body of work demonstrating these ideas and how much drool can actually be produced. Consider the scenario of tracking the efficacy of various marketing strategies on website traffic and sales.


pages: 632 words: 166,729

Addiction by Design: Machine Gambling in Las Vegas by Natasha Dow Schüll

airport security, Albert Einstein, Build a better mousetrap, business intelligence, capital controls, cashless society, commoditize, corporate social responsibility, deindustrialization, dematerialisation, deskilling, emotional labour, Future Shock, game design, impulse control, information asymmetry, inventory management, iterative process, jitney, junk bonds, large denomination, late capitalism, late fees, longitudinal study, means of production, meta-analysis, Nash equilibrium, Panopticon Jeremy Bentham, Paradox of Choice, post-industrial society, postindustrial economy, profit motive, RFID, scientific management, Silicon Valley, Skinner box, Slavoj Žižek, statistical model, the built environment, yield curve, zero-sum game

Graphical data visualization system for casinos (by Mariposa, partnered with IGT). Gamblers are mapped as icons on a casino floor; casinofloor managers can click onthe icons to access their corresponding player preference profiles. Images accessed from Mariposa website, June 2007. A company called Compudigm, now partnered with Bally, created a business intelligence tool called seePOWER that specializes in analyzing data from multiple gamblers to reveal group “tendencies and preferences,” as one press release put it. The technology works by transforming massive amounts of player tracking information into colorful heat maps that represent the collective behavior of patrons in and over time (see fig. 5.3).


pages: 575 words: 171,599

The Billionaire's Apprentice: The Rise of the Indian-American Elite and the Fall of the Galleon Hedge Fund by Anita Raghavan

"World Economic Forum" Davos, airport security, Asian financial crisis, asset allocation, Bear Stearns, Bernie Madoff, Boeing 747, British Empire, business intelligence, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, delayed gratification, estate planning, Etonian, glass ceiling, high net worth, junk bonds, kremlinology, Larry Ellison, locking in a profit, Long Term Capital Management, Marc Andreessen, mass immigration, McMansion, medical residency, Menlo Park, new economy, old-boy network, Ponzi scheme, risk tolerance, rolodex, Ronald Reagan, short selling, Silicon Valley, sovereign wealth fund, stem cell, technology bubble, too big to fail

Kumar was comfortable transgressing the law, but he was far less at ease with the idea of crossing social norms. His pushback came at a trying time in his relationship with Rajaratnam. Ever since his big tip in 2006 on the AMD–ATI Technologies talks, Kumar’s hot streak had gone stone cold. In 2007, he told Rajaratnam that the industry of business intelligence, in which companies use software to mine mountains of information stored in widely available databases, was ripe for consolidation. The tip piqued Rajaratnam’s curiosity. “How do you know?” he asked. Kumar explained that he was providing consulting services to a company called Business Objects, a French-American firm that made intelligence software.


pages: 719 words: 181,090

Site Reliability Engineering: How Google Runs Production Systems by Betsy Beyer, Chris Jones, Jennifer Petoff, Niall Richard Murphy

"Margaret Hamilton" Apollo, Abraham Maslow, Air France Flight 447, anti-pattern, barriers to entry, business intelligence, business logic, business process, Checklist Manifesto, cloud computing, cognitive load, combinatorial explosion, continuous integration, correlation does not imply causation, crowdsourcing, database schema, defense in depth, DevOps, en.wikipedia.org, exponential backoff, fail fast, fault tolerance, Flash crash, George Santayana, Google Chrome, Google Earth, if you see hoof prints, think horses—not zebras, information asymmetry, job automation, job satisfaction, Kubernetes, linear programming, load shedding, loose coupling, machine readable, meta-analysis, microservices, minimum viable product, MVC pattern, no silver bullet, OSI model, performance metric, platform as a service, proprietary trading, reproducible builds, revision control, risk tolerance, side project, six sigma, the long tail, the scientific method, Toyota Production System, trickle-down economics, warehouse automation, web application, zero day

., “Eventually Consistent: Not What You Were Expecting?”, in ACM Queue, vol. 12, no. 1, 2014. [Gra09] P. Graham, “Maker’s Schedule, Manager’s Schedule”, blog post, July 2009. [Gup15] A. Gupta and J. Shute, “High-Availability at Massive Scale: Building Google’s Data Infrastructure for Ads”, in Workshop on Business Intelligence for the Real Time Enterprise, 2015. [Ham07] J. Hamilton, “On Designing and Deploying Internet-Scale Services”, in Proceedings of the 21st Large Installation System Administration Conference, November 2007. [Han94] S. Hanks, T. Li, D. Farinacci, and P. Traina, “Generic Routing Encapsulation over IPv4 networks”, IETF Informational RFC, 1994.


pages: 586 words: 186,548

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, algorithmic bias, Alignment Problem, AlphaGo, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, Big Tech, bitcoin, Boeing 747, Boston Dynamics, business intelligence, business process, call centre, Cambridge Analytica, cloud computing, cognitive bias, Colonization of Mars, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, CRISPR, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, fake news, Fellow of the Royal Society, Flash crash, future of work, general purpose technology, Geoffrey Hinton, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, Hans Rosling, hype cycle, ImageNet competition, income inequality, industrial research laboratory, industrial robot, information retrieval, job automation, John von Neumann, Large Hadron Collider, Law of Accelerating Returns, life extension, Loebner Prize, machine translation, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, Mustafa Suleyman, natural language processing, new economy, Nick Bostrom, OpenAI, opioid epidemic / opioid crisis, optical character recognition, paperclip maximiser, pattern recognition, phenotype, Productivity paradox, radical life extension, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, seminal paper, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, sparse data, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, synthetic biology, systems thinking, Ted Kaczynski, TED Talk, The Rise and Fall of American Growth, theory of mind, Thomas Bayes, Travis Kalanick, Turing test, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working-age population, workplace surveillance , zero-sum game, Zipcar

That demonstration gave them the ability to position themselves well, both internally and externally. With regard to the businesses, I think IBM is in a unique place regarding the way they can capitalize on this kind of AI. It’s very different than the consumer space. IBM can approach the market broadly through business intelligence, data analytics, and optimization. And they can deliver targeted value, for example in healthcare applications. It’s tough to measure how successful they’ve been because it depends on what you count as AI and where you are in the business strategy. We will see how it plays out. As far as the consumer mindshare these days it seems to me like Siri and Amazon’s Alexa are in the limelight.


pages: 603 words: 186,210

Appetite for America: Fred Harvey and the Business of Civilizing the Wild West--One Meal at a Time by Stephen Fried

Albert Einstein, book value, British Empire, business intelligence, centralized clearinghouse, Charles Lindbergh, City Beautiful movement, company town, Cornelius Vanderbilt, disinformation, estate planning, Ford Model T, glass ceiling, Ida Tarbell, In Cold Blood by Truman Capote, indoor plumbing, Livingstone, I presume, Nelson Mandela, new economy, plutocrats, refrigerator car, transcontinental railway, traveling salesman, women in the workforce, Works Progress Administration, young professional

A former president of the American Bankers Association and adviser to Presidents Taft and Hoover, Swinney had put together the deal for Kansas City Union Station, and he still sat on the boards of several major companies. The aging banker had been a friend and close colleague of Ford Harvey—who sat on his board and worked with him on the rebuilding of Kansas City’s streetcar system. He had also known Byron for years. So, when E. F. Swinney said that he held Kitty Harvey’s business intelligence in high esteem—and that, in fact, he thought she had a better business brain than her uncle—people listened. Of course, Swinney had his own civic reasons for keeping Fred Harvey in Kansas City. But he also had spent a lot of time with Kitty as she and Freddie worked at investing the money from their father’s estate.


pages: 636 words: 202,284

Piracy : The Intellectual Property Wars from Gutenberg to Gates by Adrian Johns

active measures, Alan Greenspan, banking crisis, Berlin Wall, British Empire, Buckminster Fuller, business intelligence, Charles Babbage, commoditize, Computer Lib, Corn Laws, demand response, distributed generation, Douglas Engelbart, Douglas Engelbart, Edmond Halley, Ernest Rutherford, Fellow of the Royal Society, full employment, Hacker Ethic, Howard Rheingold, industrial research laboratory, informal economy, invention of the printing press, Isaac Newton, James Watt: steam engine, John Harrison: Longitude, Lewis Mumford, Marshall McLuhan, Mont Pelerin Society, new economy, New Journalism, Norbert Wiener, pirate software, radical decentralization, Republic of Letters, Richard Stallman, road to serfdom, Ronald Coase, software patent, South Sea Bubble, Steven Levy, Stewart Brand, tacit knowledge, Ted Nelson, The Home Computer Revolution, the scientific method, traveling salesman, vertical integration, Whole Earth Catalog

They took this initiative at the very moment when private detective agencies such as Pinkerton’s were coming into their own in America and Britain as entrepreneurial counterparts (and sometimes more) to the professionalizing police forces. Like them, Preston and Abbott had hit upon an opportunity that was not to go away. Their initiative marked the beginning of an alliance between business, intelligence, policing, and intellectual property that would endure long after their victory. Today, private antipiracy policing is a growth industry. It recruits expolicemen, as Preston and Abbott did, and it too has been known to pursue its quarry not just as pirates but as criminal conspirators. The modern pirate hunters propel policy and legislation too.


pages: 706 words: 202,591

Facebook: The Inside Story by Steven Levy

active measures, Airbnb, Airbus A320, Amazon Mechanical Turk, AOL-Time Warner, Apple's 1984 Super Bowl advert, augmented reality, Ben Horowitz, Benchmark Capital, Big Tech, Black Lives Matter, Blitzscaling, blockchain, Burning Man, business intelligence, Cambridge Analytica, cloud computing, company town, computer vision, crowdsourcing, cryptocurrency, data science, deep learning, disinformation, don't be evil, Donald Trump, Dunbar number, East Village, Edward Snowden, El Camino Real, Elon Musk, end-to-end encryption, fake news, Firefox, Frank Gehry, Geoffrey Hinton, glass ceiling, GPS: selective availability, growth hacking, imposter syndrome, indoor plumbing, information security, Jeff Bezos, John Markoff, Jony Ive, Kevin Kelly, Kickstarter, lock screen, Lyft, machine translation, Mahatma Gandhi, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Max Levchin, Menlo Park, Metcalfe’s law, MITM: man-in-the-middle, move fast and break things, natural language processing, Network effects, Oculus Rift, operational security, PageRank, Paul Buchheit, paypal mafia, Peter Thiel, pets.com, post-work, Ray Kurzweil, recommendation engine, Robert Mercer, Robert Metcalfe, rolodex, Russian election interference, Salesforce, Sam Altman, Sand Hill Road, self-driving car, sexual politics, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, Silicon Valley startup, skeuomorphism, slashdot, Snapchat, social contagion, social graph, social software, South of Market, San Francisco, Startup school, Steve Ballmer, Steve Bannon, Steve Jobs, Steven Levy, Steven Pinker, surveillance capitalism, tech billionaire, techlash, Tim Cook: Apple, Tragedy of the Commons, web application, WeWork, WikiLeaks, women in the workforce, Y Combinator, Y2K, you are the product

“We hope to play a critical role in reaching one of Internet.org’s most significant goals—using data more efficiently, so that more people around the world can connect and share,” wrote Rosen. But Facebook’s motivation wasn’t really providing an app to improve phone performance in developing countries. It maintained Onavo’s business model, which was gathering data from deceptively “free” apps to inform its money-making business intelligence operations. When the mobile performance tool no longer served its purpose, Facebook created a different honey trap for user data, Onavo Protect, which delivered what seemed like a bargain: a free “Virtual Private Network” (VPN) that provided more security than public Wi-Fi networks. It takes a certain amount of chutzpah to present people with a privacy tool whose purpose was to gain their data.


pages: 1,085 words: 219,144

Solr in Action by Trey Grainger, Timothy Potter

business intelligence, cloud computing, commoditize, conceptual framework, crowdsourcing, data acquisition, data science, en.wikipedia.org, failed state, fault tolerance, finite state, full text search, functional programming, glass ceiling, information retrieval, machine readable, natural language processing, openstreetmap, performance metric, premature optimization, recommendation engine, web application

Solr in Action, written by longtime Solr power users and community members, Trey and Timothy, covers these important recent Solr features and provides an excellent starting point for those new to Solr. Solr is now used in more places than I could ever have imagined—from integrated library systems to e-commerce platforms, analytics and business intelligence products, content-management systems, internet searches, and more. It’s been rewarding to see Solr grow from a few early adopters to a huge global community of helpful users and active volunteers cooperatively pushing development forward. Solr in Action gives you the knowledge and techniques you need to use Solr’s features that have been under development since 2004.


pages: 927 words: 216,549

Empire of Guns by Priya Satia

banking crisis, British Empire, business intelligence, Corn Laws, cotton gin, deindustrialization, delayed gratification, European colonialism, Fellow of the Royal Society, flying shuttle, hiring and firing, independent contractor, interchangeable parts, invisible hand, Isaac Newton, James Watt: steam engine, joint-stock company, Khyber Pass, Lewis Mumford, Menlo Park, military-industrial complex, Panopticon Jeremy Bentham, rent-seeking, Scramble for Africa, Silicon Valley, spinning jenny, technological determinism, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, transatlantic slave trade, zero-sum game

David Jr.’s second wife was the sister of Charles Lloyd, who married James Farmer’s daughter by Priscilla Plumsted, granddaughter of John Freame. The network of family connections is truly indescribable. The point is that this network married finance to industry—and allowed for exchange of business intelligence between them. Joseph Freame shared his worries about the bank after the Seven Years’ War with Farmer’s daughter “Polly” (Mary), who socialized with the Barclays and the Plumsteds. Agatha Barclay, who became a Gurney in 1773, wrote frequently to Polly, who became a Lloyd in 1774, their correspondence often referring to their parents’ exchanges and meetings.


pages: 933 words: 205,691

Hadoop: The Definitive Guide by Tom White

Amazon Web Services, bioinformatics, business intelligence, business logic, combinatorial explosion, data science, database schema, Debian, domain-specific language, en.wikipedia.org, exponential backoff, fallacies of distributed computing, fault tolerance, full text search, functional programming, Grace Hopper, information retrieval, Internet Archive, Kickstarter, Large Hadron Collider, linked data, loose coupling, openstreetmap, recommendation engine, RFID, SETI@home, social graph, sparse data, web application

Today, Hive is a successful Apache project used by many organizations as a general-purpose, scalable data processing platform. Of course, SQL isn’t ideal for every big data problem—it’s not a good fit for building complex machine learning algorithms, for example—but it’s great for many analyses, and it has the huge advantage of being very well known in the industry. What’s more, SQL is the lingua franca in business intelligence tools (ODBC is a common bridge, for example), so Hive is well placed to integrate with these products. This chapter is an introduction to using Hive. It assumes that you have working knowledge of SQL and general database architecture; as we go through Hive’s features, we’ll often compare them to the equivalent in a traditional RDBMS.


pages: 761 words: 231,902

The Singularity Is Near: When Humans Transcend Biology by Ray Kurzweil

additive manufacturing, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, Benoit Mandelbrot, Bill Joy: nanobots, bioinformatics, brain emulation, Brewster Kahle, Brownian motion, business cycle, business intelligence, c2.com, call centre, carbon-based life, cellular automata, Charles Babbage, Claude Shannon: information theory, complexity theory, conceptual framework, Conway's Game of Life, coronavirus, cosmological constant, cosmological principle, cuban missile crisis, data acquisition, Dava Sobel, David Brooks, Dean Kamen, digital divide, disintermediation, double helix, Douglas Hofstadter, en.wikipedia.org, epigenetics, factory automation, friendly AI, functional programming, George Gilder, Gödel, Escher, Bach, Hans Moravec, hype cycle, informal economy, information retrieval, information security, invention of the telephone, invention of the telescope, invention of writing, iterative process, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, lifelogging, linked data, Loebner Prize, Louis Pasteur, mandelbrot fractal, Marshall McLuhan, Mikhail Gorbachev, Mitch Kapor, mouse model, Murray Gell-Mann, mutually assured destruction, natural language processing, Network effects, new economy, Nick Bostrom, Norbert Wiener, oil shale / tar sands, optical character recognition, PalmPilot, pattern recognition, phenotype, power law, precautionary principle, premature optimization, punch-card reader, quantum cryptography, quantum entanglement, radical life extension, randomized controlled trial, Ray Kurzweil, remote working, reversible computing, Richard Feynman, Robert Metcalfe, Rodney Brooks, scientific worldview, Search for Extraterrestrial Intelligence, selection bias, semantic web, seminal paper, Silicon Valley, Singularitarianism, speech recognition, statistical model, stem cell, Stephen Hawking, Stewart Brand, strong AI, Stuart Kauffman, superintelligent machines, technological singularity, Ted Kaczynski, telepresence, The Coming Technological Singularity, Thomas Bayes, transaction costs, Turing machine, Turing test, two and twenty, Vernor Vinge, Y2K, Yogi Berra

In our reporting on the KurzweilAI.net Web site, we feature one or more dramatic systems almost every day.180 A 2003 study by Business Communications Company projected a $21 billion market by 2007 for AI applications, with average annual growth of 12.2 percent from 2002 to 2007.181 Leading industries for AI applications include business intelligence, customer relations, finance, defense and domestic security, and education. Here is a small sample of narrow AI in action. Military and Intelligence. The U.S. military has been an avid user of AI systems. Pattern-recognition software systems guide autonomous weapons such as cruise missiles, which can fly thousands of miles to find a specific building or even a specific window.182 Although the relevant details of the terrain that the missile flies over are programmed ahead of time, variations in weather, ground cover, and other factors require a flexible level of real-time image recognition.


pages: 496 words: 174,084

Masterminds of Programming: Conversations With the Creators of Major Programming Languages by Federico Biancuzzi, Shane Warden

Benevolent Dictator For Life (BDFL), business intelligence, business logic, business process, cellular automata, cloud computing, cognitive load, commoditize, complexity theory, conceptual framework, continuous integration, data acquisition, Dennis Ritchie, domain-specific language, Douglas Hofstadter, Fellow of the Royal Society, finite state, Firefox, follow your passion, Frank Gehry, functional programming, general-purpose programming language, Guido van Rossum, higher-order functions, history of Unix, HyperCard, industrial research laboratory, information retrieval, information security, iterative process, Ivan Sutherland, John von Neumann, Ken Thompson, Larry Ellison, Larry Wall, linear programming, loose coupling, machine readable, machine translation, Mars Rover, millennium bug, Multics, NP-complete, Paul Graham, performance metric, Perl 6, QWERTY keyboard, RAND corporation, randomized controlled trial, Renaissance Technologies, Ruby on Rails, Sapir-Whorf hypothesis, seminal paper, Silicon Valley, slashdot, software as a service, software patent, sorting algorithm, SQL injection, Steve Jobs, traveling salesman, Turing complete, type inference, Valgrind, Von Neumann architecture, web application

SQL manages persistent data, which has a long lifetime. Enterprises that have an investment in SQL databases are inclined to build on that investment rather than starting over with a different approach. SQL is robust enough to solve real problems. It spans a broad spectrum of usage from business intelligence to transaction processing. It is supported on many platforms and in many processing environments. Despite some criticism about its lack of elegance, SQL has been used successfully by many organizations to develop critical, real-world applications. I believe that this success reflects the origin of the language in an experimental prototype that was responsive to the needs of real users from the earliest days.


The Art of Scalability: Scalable Web Architecture, Processes, and Organizations for the Modern Enterprise by Martin L. Abbott, Michael T. Fisher

always be closing, anti-pattern, barriers to entry, Bernie Madoff, business climate, business continuity plan, business intelligence, business logic, business process, call centre, cloud computing, combinatorial explosion, commoditize, Computer Numeric Control, conceptual framework, database schema, discounted cash flows, Dunning–Kruger effect, en.wikipedia.org, fault tolerance, finite state, friendly fire, functional programming, hiring and firing, Infrastructure as a Service, inventory management, machine readable, new economy, OSI model, packet switching, performance metric, platform as a service, Ponzi scheme, power law, RFC: Request For Comment, risk tolerance, Rubik’s Cube, Search for Extraterrestrial Intelligence, SETI@home, shareholder value, Silicon Valley, six sigma, software as a service, the scientific method, transaction costs, Vilfredo Pareto, web application, Y2K

We guarantee that scalability was on the mind of every bank during the consolidation that occurred after the collapse of the banking industry. The models and approaches that we present in our book are industry agnostic. They have been developed, tested, and proven successful in some of the fastest growing companies of our time and they work not only in front-end customer facing transaction processing systems but back-end business intelligence, enterprise resource planning, and customer relationship management systems. They don’t discriminate by activity, but rather help to guide the thought process on how to separate systems, organizations, and processes to meet the objective of becoming highly scalable and reaching a level of scale that allows your business to operate without concerns regarding customer or end-user demand.


The Art of SEO by Eric Enge, Stephan Spencer, Jessie Stricchiola, Rand Fishkin

AltaVista, barriers to entry, bounce rate, Build a better mousetrap, business intelligence, cloud computing, content marketing, dark matter, en.wikipedia.org, Firefox, folksonomy, Google Chrome, Google Earth, hypertext link, index card, information retrieval, Internet Archive, Larry Ellison, Law of Accelerating Returns, linked data, mass immigration, Metcalfe’s law, Network effects, optical character recognition, PageRank, performance metric, Quicken Loans, risk tolerance, search engine result page, self-driving car, sentiment analysis, social bookmarking, social web, sorting algorithm, speech recognition, Steven Levy, text mining, the long tail, vertical integration, Wayback Machine, web application, wikimedia commons

The list of situations where the brand can limit the strategy is quite long, and the opposite can happen too, where the nature of the brand makes a particular SEO strategy pretty compelling. Ultimately, your goal is to dovetail SEO efforts with branding as seamlessly as possible. Competition Your SEO strategy can also be influenced by your competitors’ strategies, so understanding what they are doing is a critical part of the process for both SEO and business intelligence objectives. There are several scenarios you might encounter: The competitor discovers a unique, highly converting set of keywords. The competitor discovers a targeted, high-value link. The competitor saturates a market segment, justifying your focus elsewhere. Weaknesses appear in the competitor’s strategy, which provide opportunities for exploitation.


pages: 1,266 words: 278,632

Backup & Recovery by W. Curtis Preston

Berlin Wall, business intelligence, business process, database schema, Debian, dumpster diving, failed state, fault tolerance, full text search, job automation, Kickstarter, operational security, rolling blackouts, side project, Silicon Valley, systems thinking, web application

SQL Server 2005 also offers a wealth of additional features such as services for analysis, data integration, notification, and reporting as well as the service broker. Combined, these elements make up a complete relational database system that can be used for simple tasks such as a database-driven web application or for more advanced needs such as data mining, complex business intelligence gathering, specialized reporting and notification, and a host of additional needs. Tip When referring to specific versions, this chapter often uses just the version numbers. For example, 2000 refers to SQL Server 2000, and 2005 refers to SQL Server 2005. When referring to both SQL Server 2000 and 2005, I use the term SQL Server.


pages: 961 words: 302,613

The First American: The Life and Times of Benjamin Franklin by H. W. Brands

always be closing, British Empire, business intelligence, colonial rule, complexity theory, Copley Medal, disinformation, experimental subject, Fellow of the Royal Society, Hacker News, Isaac Newton, joint-stock company, music of the spheres, Republic of Letters, scientific mainstream, South Sea Bubble, Thomas Malthus, trade route

Besides the benefit of finishing the job on schedule, Franklin appreciated the positive impression he was making on the sober and hardworking Quakers. “This industry visible to our neighbors began to give us character and credit,” he remembered. Many of the merchants, who gathered for refreshment and the exchange of business intelligence at the Every-Night Club, wondered at Franklin and Meredith’s boldness in beginning their business when Philadelphia already had two printers and was hardly clamoring for a third. Those without personal knowledge of Franklin asserted that the new enterprise must surely fail. Yet individuals who observed Franklin at work argued a contrary view.