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Competing on Analytics: The New Science of Winning by Thomas H. Davenport, Jeanne G. Harris
always be closing, big data - Walmart - Pop Tarts, business intelligence, business process, call centre, commoditize, data acquisition, digital map, en.wikipedia.org, global supply chain, 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 scientific method, traveling salesman, yield management
Smaller companies that can’t afford expensive business intelligence software packages will have available free or inexpensive “open source” tools—though we believe that the most advanced capabilities will continue to be expensive. Increasing use of dedicated “business intelligence appliances” from such vendors as Teradata and Netezza, which are optimized for business intelligence applications and large amounts of data. These are effectively supercomputers that make chewing through large databases and analyses much faster. More automated decisions, as opposed to relying on humans to look at data and make decisions. This approach has been called operational business intelligence, since automated decisions are typically embedded within operational business processes. It might also be called real-time business intelligence, since most automated decisions are made immediately.
For some organizations, it may mean only that central IT groups manage the data and procure and install the needed business intelligence software. For others, it may mean that a central analytical services group assists executives with analysis and decision making. As we’ll discuss in chapter 7, a number of firms have established such groups. From an IT standpoint, another approach to enterprise-level management of analytics is the establishment of a business intelligence competency center, or BICC. According to SAS, a BICC is defined as “a cross-functional team with a permanent, formal organizational structure. It is owned and staffed by the [company] and has defined tasks, roles, responsibilities and processes for supporting and promoting the effective use of business intelligence across the organization.”5 In the business intelligence survey of 220 firms described earlier, 23 percent of respondents said their firm already had a BICC.
Versions of this data-oriented focus were referred to as OLAP (online analytical processing) and later data warehousing. 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.
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, software as a service, statistical model, supply-chain management, the market place
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. xv This page intentionally left blank SECTION Introduction and market overview 1 This page intentionally left blank CHAPTER Introducing BI 1 Why is there a need for this book? Many books exist that identify how to get the most out of analytics or how to develop an open source business intelligence (OSBI) solution based on specific development or solution requirements.
This choice means looking at whether an organization will implement a full BI solution, consider a best-of-breed alternative, look at SaaS, or customize an analytics environment through the use of OS. 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. While information can be stored within the data warehouse even if it isn’t being used within a specific data mart, the way in which data is captured, stored, and processed is outside the scope of this book as the aim here is to give you a very broad understanding of BI and how each component fits within a bigger whole.
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.
Big Data Analytics: Turning Big Data Into Big Money by Frank J. Ohlhorst
algorithmic trading, bioinformatics, business intelligence, business process, call centre, cloud computing, create, read, update, delete, data acquisition, DevOps, fault tolerance, linked data, natural language processing, Network effects, pattern recognition, performance metric, personalized medicine, RFID, sentiment analysis, six sigma, smart meter, statistical model, supply-chain management, Watson beat the top human players on Jeopardy!, web application
Titles in the Wiley and SAS Business Series include: Activity-Based Management for Financial Institutions: Driving Bottom-Line Results by Brent Bahnub Advanced Business Analytics: Creating Business Value from Your Data by Jean Paul Isson and Jesse Harriott Branded! 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.
See Business analytics (BA) BackType Backup systems Batch processing Behavioral analytics Benefits analysis Best practices anomalies expediency-accuracy tradeoff high-value opportunities focus in-memory processing project management processes project prerequisites thinking big worst practice avoidance BI. See Business intelligence (BI) Big Data and Big Data analytics analysis categories application platforms best practices business case development challenges classifications components defined evolution of examples of 4Vs of goal setting introduction investment in path to phases of potential of privacy issues processing role of security (See Security) sources of storage team development technologies (See Technologies) value of visualizations Big Science BigSheets Bigtable Bioinformatics Biomedical industry Blekko Business analytics (BA) Business case best practices data collection and storage options elements of introduction Business intelligence (BI) as Big Data analytics foundation Big Data analytics team incorporation Big Data impact defined extract, transform, and load (ETL) information technology and in-memory processing limitations of marketing campaigns risk analysis storage capacity issues unstructured data visualizations Business leads Business logic Business objectives Business rules C Capacity of storage systems Cassandra Census data CERN Citi Classification of data Cleaning Click-stream data Cloud computing Cloudera Combs, Nick Commodity hardware Common Crawl Corpus Communication Competition Compliance Computer security officers (CSOs) Consulting firms Core capabilities, data analytics team Costs Counterintelligence mind-set CRUD (create, retrieve, update, delete) applications Cryptographic keys Culture, corporate Customer needs Cutting, Doug D Data defined growth in volume of value of See also Big Data and Big Data analytics Data analysis categories challenges complexity of as critical skill for team members data accuracy evolution of importance of process technologies Database design Data classification Data discovery Data extraction Data integration technologies value creation Data interpretation Data manipulation Data migration Data mining components as critical skill for team members defined examples methods technologies Data modeling Data protection.
For more information about Wiley products, visit www.wiley.com. 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.
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, disruptive innovation, Edward Snowden, Erik Brynjolfsson, intermodal, Internet of things, Jeff Bezos, knowledge worker, lifelogging, Mark Zuckerberg, move fast and break things, 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
The general activity of making sense of data has been called decision support, executive support, online analytical processing, business intelligence, analytics, and now big data (see table 1-3).4 There are certainly some new elements in each generation of terminology, but I’m not sure that things have evolved enough to be worthy of six generations. Ta b l e 1 - 3 Terminology for using and analyzing data Term Time frame Specific meaning Decision support 1970–1985 Use of data analysis to support decision making Executive support 1980–1990 Focus on data analysis for decisions by senior executives Online analytical processing (OLAP) 1990–2000 Software for analyzing multidimensional data tables Business intelligence 1989–2005 Tools to support datadriven decisions, with emphasis on reporting Analytics 2005–2010 Focus on statistical and mathematical analysis for decisions Big data 2010–present Focus on very large, unstructured, fast-moving data Chapter_01.indd 10 03/12/13 3:24 AM Why Big Data Is Important to You and Your Organization 11 What makes big data somewhat worthy of a new term are the new and more voluminous forms of data that it involves—2.5 quintillion (that’s 2.5 followed by eighteen zeros) bytes per day generated around the world, by one estimate.5 Less structured data types, as I’ve suggested, are even more worthy of a new term and approach.
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. I won’t go into great detail on this topic, because I’ve coauthored a book called Keeping Up with the Quants that deals with it extensively.4 I don’t think there are major differences between the communication and trusted adviser skills of small data quants and data scientists; these skills are highly necessary in both jobs.
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. In the best cases, these environments have helped companies understand their customer purchase and behavior patterns across channels and relationships, streamline sales processes, optimize Chapter_05.indd 128 03/12/13 1:04 PM Technology for Big Data 129 Figure 5-4 A typical data warehouse environment ERP Reporting CRM OLAP Legacy Third-party apps Data warehouse Ad hoc Modeling Source: SAS Best Practices.
Digital Accounting: The Effects of the Internet and Erp on Accounting by Ashutosh Deshmukh
accounting loophole / creative accounting, AltaVista, business continuity plan, business intelligence, business process, call centre, computer age, conceptual framework, corporate governance, data acquisition, dumpster diving, fixed income, hypertext link, interest rate swap, inventory management, iterative process, late fees, 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 management, supply-chain management software, telemarketer, transaction costs, value at risk, web application, Y2K
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.
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 intelligence •Business 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.
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, business intelligence, business process, call centre, cloud computing, data acquisition, discrete time, inventory management, iterative process, job automation, knowledge worker, performance metric, platform as a service, side project, zero-sum game
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. We drive stakes in the ground regarding the goals of data warehousing and business intelligence in this chapter, while observing the uncanny similarities between the responsibilities of a DW/BI manager and those of a publisher.
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.
It's the definitive guide. Intended Audience This book is intended for data warehouse and business intelligence designers, implementers, and managers. In addition, business analysts and data stewards who are active participants in a DW/BI initiative will find the content useful. Even if you're not directly responsible for the dimensional model, we believe it is important for all members of a project team to be comfortable with dimensional modeling concepts. The dimensional model has an impact on most aspects of a DW/BI implementation, beginning with the translation of business requirements, through the extract, transformation and load (ETL) processes, and finally, to the unveiling of a data warehouse through business intelligence applications. Due to the broad implications, you need to be conversant in dimensional modeling regardless of whether you are responsible primarily for project management, business analysis, data architecture, database design, ETL, BI applications, or education and support.
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, informal economy, knowledge economy, knowledge worker, semantic web, 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. OLAP multidimensional tools usually keep a generous supply of metadata about their content, for example, information about how data has been defined to drill down and roll up.
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.
Data-Ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else by Steve Lohr
"Robert Solow", 23andMe, Affordable Care Act / Obamacare, Albert Einstein, big data - Walmart - Pop Tarts, bioinformatics, business cycle, business intelligence, call centre, cloud computing, computer age, conceptual framework, Credit Default Swap, crowdsourcing, Daniel Kahneman / Amos Tversky, Danny Hillis, data is the new oil, David Brooks, East Village, Edward Snowden, Emanuel Derman, Erik Brynjolfsson, everywhere but in the productivity statistics, Frederick Winslow Taylor, Google Glasses, impulse control, income inequality, indoor plumbing, industrial robot, informal economy, Internet of things, invention of writing, Johannes Kepler, John Markoff, John von Neumann, lifelogging, Mark Zuckerberg, market bubble, meta analysis, meta-analysis, money market fund, natural language processing, obamacare, pattern recognition, payday loans, personalized medicine, precision agriculture, pre–internet, Productivity paradox, RAND corporation, rising living standards, Robert Gordon, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, 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, unbanked and underbanked, underbanked, Von Neumann architecture, Watson beat the top human players on Jeopardy!
Big data is such a market, and IBM recognized that years before the term became popular. 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. “Information,” he wrote, “is now being generated and utilized at an ever-increasing rate because of the accelerated pace and scope of human activities.”
The purpose, he wrote, was to digest and present information to guide decision making. “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. The new headquarters, far smaller, opened in 1997, makes a different statement, tucked into nature rather than rising above it.
It owns eight and manages them all, mostly in New York, with one each in Chicago, Miami, and Washington, DC; its brands include the Affinia hotels, and one-of-a-kind properties like The Surrey and The Benjamin. 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. The current data program at Denihan had its origins in the late 1990s.
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, blockchain, business climate, business intelligence, business process, call centre, chief data officer, Claude Shannon: information theory, commoditize, conceptual framework, crowdsourcing, dark matter, data acquisition, digital twin, discounted cash flows, disintermediation, diversification, en.wikipedia.org, endowment effect, Erik Brynjolfsson, full employment, informal economy, intangible asset, Internet of things, linked data, Lyft, Nash equilibrium, Network effects, new economy, obamacare, performance metric, profit motive, recommendation engine, RFID, semantic web, smart meter, Snapchat, software as a service, source of truth, supply-chain management, 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. Beyond Basic Business Intelligence “From what I have been able to determine, we have over 100 distinct internal BI implementations producing some 15,000 reports, most weekly, some monthly or quarterly,” confided the new CIO of a Big-4 systems integrator.
, author. 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. By blending and analyzing data from its millions of customers across thirteen different databases, along with census, ethnicity, and population migration data, with the help of self-service data integration and analytics software from Alteryx, it developed attrition models to analyze customers by spend level, purchase history, and other dimensions to identify and target high-value multi-channel customers.
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, database schema, double helix, en.wikipedia.org, epigenetics, fault tolerance, Firefox, Hans Rosling, housing crisis, information retrieval, lake wobegon effect, longitudinal study, Mars Rover, natural language processing, openstreetmap, prediction markets, profit motive, semantic web, sentiment analysis, Simon Singh, social graph, SPARQL, speech recognition, statistical model, supply-chain management, text mining, Vernor Vinge, web application
In addition to analyses, we started to build simple products using historical data, including an internal project to aggregate features of sponsored group members that proved popular with brand advertisers. 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? E. F. Codd first proposed the relational model for data in 1970, and IBM had a working prototype of a relational database management system (RDBMS) by the mid-1970s.
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.
Applied Artificial Intelligence: A Handbook for Business Leaders by Mariya Yao, Adelyn Zhou, Marlene Jia
Airbnb, Amazon Web Services, artificial general intelligence, autonomous vehicles, business intelligence, business process, call centre, chief data officer, computer vision, conceptual framework, en.wikipedia.org, future of work, industrial robot, Internet of things, iterative process, Jeff Bezos, job automation, Marc Andreessen, natural language processing, new economy, pattern recognition, performance metric, price discrimination, randomized controlled trial, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, skunkworks, software is eating the world, source of truth, speech recognition, statistical model, strong AI, technological singularity
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. Similarly, if BI finds that your employees are bored because their skills are being underutilized, HR can use that knowledge to adjust individual advancement plans, increasing employee satisfaction in the process.
Obstacles and Opportunities Current Obstacles What AI Can Do for Enterprise Functions 13. 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. Customer Support Conversational Agents Social Listening Customer Churn Lifetime Value 20.
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.
Data Mining: Concepts, Models, Methods, and Algorithms by Mehmed Kantardzić
Albert Einstein, 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
RapidMiner Publisher: Rapid-I (http://rapid-i.com) Rapid-I provides software, solutions, and services in the fields of predictive analytics, data mining, and text mining. 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. SNNS Publisher: University of Stuttart (http://www.nada.kth.se/∼orre/snns-manual/) SNNS is a publicly available software.
Some other notable features include C++ source code generation, customized components through DLLs, a comprehensive macro language, and Visual Basic accessibility through OLE Automation. The tool runs on all Windows platforms. 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. (MacOS) MATLAB NN Toolbox Vendor: Mathworks Inc.
Data Mining: Concepts and Techniques: Concepts and Techniques by Jiawei Han, Micheline Kamber, Jian Pei
bioinformatics, business intelligence, business process, Claude Shannon: information theory, cloud computing, computer vision, correlation coefficient, cyber-physical system, database schema, discrete time, distributed generation, finite state, information retrieval, iterative process, knowledge worker, linked data, natural language processing, Netflix Prize, Occam's razor, pattern recognition, performance metric, phenotype, random walk, recommendation engine, RFID, semantic web, sentiment analysis, 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. Using characterization mining techniques, we can better understand features of each customer group and develop customized customer reward programs. 1.6.2.
In this context, different clustering methods may generate different clusterings on the same data set. 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. Moreover, consider a consultant company with a large number of projects. To improve project management, clustering can be applied to partition projects into categories based on similarity so that project auditing and diagnosis (to improve project delivery and outcomes) can be conducted effectively.
Keeping Up With the Quants: Your Guide to Understanding and Using Analytics by Thomas H. Davenport, Jinho Kim
Black-Scholes formula, business intelligence, business process, call centre, computer age, correlation coefficient, correlation does not imply causation, Credit Default Swap, en.wikipedia.org, feminist movement, Florence Nightingale: pie chart, forensic accounting, global supply chain, 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, Robert Shiller, self-driving car, sentiment analysis, six sigma, Skype, statistical model, supply-chain management, text mining, the scientific method, Thomas Davenport
If you don’t have a lot of mathematical or statistical skills, it may also be the stage that is most likely to be entrusted to others who do have the quantitative abilities you need (see “How to Find a Quant”). 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.
For that approach, you will need software that focuses primarily on reporting. Dashboards, scorecards, and alerts are all forms of reporting. 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.
Considering that employers’ demand for MSA graduates has been increasing, it is natural that this type of degree program will be opened at other universities as well. 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. If you practice the following quantitative habits, you’ll eventually find that you’ve developed a quantitative attitude.
Zero History by William Gibson
“I’ve leased two floors of offices here, but they’re very busy now. 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. “I’m sending you both to Chicago. We think the Hounds designer is there.”
But the Festos are genius. We opted for their sheer strangeness, the organic movement, modeled from nature. 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. “That’s not a problem. I wasn’t planning on sending you immediately. Say four more days.
“You look,” said Bigend, “like a foxhunting spiv. His grasp of contradiction is brilliantly subversive.” “Is there wifi?” “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. “A bent former major in the Special Forces, who does illegal arms deals? I think that might be something else.”
The Start-Up of You: Adapt to the Future, Invest in Yourself, and Transform Your Career by Reid Hoffman, Ben Casnocha
Airbnb, Andy Kessler, Black Swan, business intelligence, Cal Newport, Clayton Christensen, commoditize, David Brooks, Donald Trump, en.wikipedia.org, fear of failure, follow your passion, future of work, game design, Jeff Bezos, job automation, Joi Ito, late fees, lateral thinking, Marc Andreessen, Mark Zuckerberg, Menlo Park, out of africa, Paul Graham, paypal mafia, Peter Thiel, recommendation engine, Richard Bolles, risk tolerance, rolodex, shareholder value, side project, Silicon Valley, Silicon Valley startup, social web, Steve Jobs, Steve Wozniak, Tony Hsieh, transaction costs
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. It’s people who help you understand your assets, aspirations, and the market realities; it’s people who help you vet and get introduced to possible allies and trust connections; it’s people who help you track the risk attached to a given opportunity.
Entrepreneurs deal with these uncertainties, changes, and constraints head-on. 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. They are valuable no matter your career stage. They are urgent whether you’re just out of college, a decade into the workforce and angling for that next big move, or launching a brand-new career later in life.
Industry 4.0: The Industrial Internet of Things by Alasdair Gilchrist
3D printing, additive manufacturing, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, business intelligence, business process, chief data officer, cloud computing, connected car, cyber-physical system, deindustrialization, DevOps, digital twin, fault tolerance, 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, pattern recognition, peer-to-peer, platform as a service, pre–internet, race to the bottom, RFID, Skype, smart cities, smart grid, smart meter, smart transportation, software as a service, stealth mode startup, supply-chain management, trade route, undersea cable, 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. Retail Retailers, like most businesses, suffer IT costs and overheads, which directly affects their profits, as they must pass these costs onto the customer as part of the cost of goods.
Say you contract an offshore company to produce one million shirts. 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. M2M learning is very important and sometimes very simple, for example a multiple-choice exam.
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. Furthermore, it uses predictive analysis to ask what will happen. And finally, it uses prescriptive analytics to ask what you should do (see Figure 4-7).
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, bioinformatics, Brewster Kahle, business intelligence, dark matter, Donald Davies, Douglas Engelbart, Douglas Engelbart, full text search, HyperCard, hypertext link, information retrieval, Internet Archive, joint-stock company, knowledge worker, natural language processing, pre–internet, profit motive, 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?,” you’ll appreciate this savvy, nononsense approach to using the Internet to solve everyday business problems and stay one step ahead of the competition. 2000/448 pp/softbound/ISBN 0-910965-35-8 $29.95 net.people The Personalities and Passions Behind the Web Sites By Eric C.
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, bitcoin, business intelligence, business process, chief data officer, clean water, cloud computing, computer vision, continuous integration, crowdsourcing, data is the new oil, digital twin, distributed ledger, don't be evil, Elon Musk, en.wikipedia.org, facts on the ground, Google Glasses, income inequality, Infrastructure as a Service, Internet of things, Masdar, microservices, Minecraft, platform as a service, ransomware, RFID, ride hailing / ride sharing, risk tolerance, 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
One popular form of data portal is the open data portal that many cities have. 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.
If freshness is most important, latency is crucial, and optimized solutions for that should be chosen. 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.
Londongrad: From Russia With Cash; The Inside Story of the Oligarchs by Mark Hollingsworth, Stewart Lansley
Berlin Wall, Big bang: deregulation of the City of London, Bob Geldof, business intelligence, corporate governance, corporate raider, credit crunch, crony capitalism, Donald Trump, energy security, Etonian, F. W. de Klerk, income inequality, kremlinology, mass immigration, mega-rich, Mikhail Gorbachev, offshore financial centre, paper trading, plutocrats, Plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, rent-seeking, Ronald Reagan, Skype, Sloane Ranger
Documents were written in code and then shredded after distribution to the client. 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. That is why you should always look for the device inside your television set.
As well as investigations by the Russian state, many companies were suing Yukos and Curtis was the custodian of its secrets. 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. Vast fees were offered for revelations about the secret, complex ownership structure.
He began fabricating bizarre conspiracy theories and death threats in the hope that Berezovsky would rehire him. 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. By networking the ‘Circuit’, as it was known, Litvinenko secured occasional assignments and offered advice to certain European law enforcement agencies.
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, full text search, information retrieval, Internet Archive, Internet of things, linked data, NP-complete, peer-to-peer, performance metric, random walk, recommendation engine, RFID, semantic web, Silicon Valley, social intelligence, software as a service, SPARQL, 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.
In general, OLTP applications are best described by relatively frequent updates, short and simple transactions accessing usually a small portion of the database. 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. This reduces the effort to maintain data consistency.
With the accession to new markets, NoSQL systems are also facing the needs of new clients. 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.
European Founders at Work by Pedro Gairifo Santos
business intelligence, cloud computing, crowdsourcing, fear of failure, full text search, information retrieval, inventory management, iterative process, Jeff Bezos, Joi Ito, Lean Startup, Mark Zuckerberg, natural language processing, pattern recognition, pre–internet, recommendation engine, Richard Stallman, Silicon Valley, Skype, slashdot, Steve Jobs, Steve Wozniak, subscription business, technology bubble, web application, Y Combinator
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.” That alteration enabled us to keep selling in a difficult economy. 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.
I knew we had to make some critical decisions. First of all, we needed to shift the centre of gravity of the company from France to the US. 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. When the technology bubble burst, we emerged relatively unscathed because by then we were a large, stable, profitable company with thousands of customers.
US, 235 France, 234–235 freelancer relationship, 240 LinkedIn, 226 next generation founders, 238–239 online community creation, 230–231 pivot board, 234 problems and solution, 228–230 RapidSite, 226 Silicon Valley, 230, 238 social networking campaign, 227 technology evolution, 231–232 Twitter, 233, 236 Ublog, 226 US lessons, 227–228 US vs. France business, 239–240 venture capital fund raise, 235 Wi-Fi sharing software, Skype, 233 WordPress, 226 Liautaud, Bernard. Business Objects, 49 Atlas Ventures, 50 business intelligence and analytics, 55 California, marketing head, 53 Coface, 50 cost-cutting mode, 54 Crystal Reports, 52, 53 entrepreneur needs, 56 European investors, 54 European market, 51 European vs. US venture, 56 Goldman Sachs, 51 Innovacom, France Telecom, 50 Michel (Jean), Oracle interface, 49 Schwartz (John), CEO, 55–56 key lessons, success, 51 NASDAQ, 50 near-death experience, 53 new hire risk, 51 Partech International, 50 product marketing, Oracle, 49 SAP deal, 55 strong execution model, 57 US subsidiary and French company, internal issues, 52 M, N, O Moross, Richard.
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel
Albert Einstein, algorithmic trading, Amazon Mechanical Turk, 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, en.wikipedia.org, Erik Brynjolfsson, Everything should be made as simple as possible, experimental subject, Google Glasses, happiness index / gross national happiness, job satisfaction, Johann Wolfgang von Goethe, lifelogging, 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, speech recognition, statistical model, Steven Levy, 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
Eric advocates quite clearly how some choices are predictably more profitable than others—and I agree!” —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.” —Jon Francis, Senior Data Scientist, Nike “Predictive analytics is the key to unlocking new value at a previously unimaginable economic scale.
—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.” —Zhou Yu, Online-to-Store Analyst, Google “[Predictive Analytics is] an engaging, humorous introduction to the world of the data scientist.
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.
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, 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, self-driving car, Silicon Valley, smart cities, smart grid, software as a service, Steve Jobs, web application, Y Combinator, yield management
Maybe in certain domains or certain specific applications, but I think there’s a lot of opportunity, I mean a ton of opportunity there. 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. For example, RFID tags are very low cost, don’t need a battery, and have an infinite life.
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? In addition, what are the core growth segments? How much of the value will be captured by existing players?
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, Donald Trump, double helix, dumpster diving, East Village, feminist movement, forensic accounting, fudge factor, hiring and firing, Internet Archive, longitudinal study, Lyft, mandatory minimum, meta analysis, meta-analysis, pink-collar, Ponzi scheme, Robert Hanssen: Double agent, Ronald Reagan, Silicon Valley, Skype, Snapchat, telemarketer, theory of mind, Thomas Kuhn: the structure of scientific revolutions
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.
I supposed that counted as lying by omission, but I liked to think I was protecting them from unnecessary worry. 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.
Only Humans Need Apply: Winners and Losers in the Age of Smart Machines by Thomas H. Davenport, Julia Kirby
AI winter, Andy Kessler, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, basic income, Baxter: Rethink Robotics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, commoditize, conceptual framework, dark matter, David Brooks, deliberate practice, deskilling, digital map, disruptive innovation, Douglas Engelbart, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, fixed income, 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, 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, knowledge worker, labor-force participation, lifelogging, longitudinal study, loss aversion, Mark Zuckerberg, Narrative Science, natural language processing, Norbert Wiener, nuclear winter, pattern recognition, performance metric, Peter Thiel, precariat, quantitative trading / quantitative ﬁnance, Ray Kurzweil, Richard Feynman, risk tolerance, Robert Shiller, Robert Shiller, 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, transaction costs, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, Works Progress Administration, Zipcar
But a human was still required to create and interpret the analysis. 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. Newer Ways to Support Humans We won’t march through every cell of this matrix; we trust you get the idea of it well enough to fill in some of the blanks in our narrative.
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.
That means lots of reports, sliced and diced in different ways for different people. 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. Now, however, DataXu has worked out a way to use rigorous and frequent—but still small—experiments to see if an ad or promotion is working.
Hacking Growth: How Today's Fastest-Growing Companies Drive Breakout Success by Sean Ellis, Morgan Brown
Airbnb, Amazon Web Services, barriers to entry, Ben Horowitz, bounce rate, business intelligence, business process, correlation does not imply causation, crowdsourcing, DevOps, disruptive innovation, Elon Musk, game design, Google Glasses, Internet of things, inventory management, iterative process, Jeff Bezos, Khan Academy, Kickstarter, Lean Startup, Lyft, Mark Zuckerberg, market design, minimum viable product, Network effects, Paul Graham, Peter Thiel, Ponzi scheme, recommendation engine, ride hailing / ride sharing, side project, Silicon Valley, Silicon Valley startup, Skype, Snapchat, software as a service, Steve Jobs, subscription business, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, working poor, Y Combinator, young professional
Growth teams don’t necessarily replace more traditional departments, but rather complement them, and help them optimize their approaches. 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 larger firms that must contend with existing structures and cultures resistant to change, small teams can be set up independently and even for finite projects, like perhaps the launch of a new product or a specific marketing channel, such as mobile.
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.
The selections are based on a combination of a customer’s search history and buying habits, and data about the habits of other shoppers like that customer. 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.
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, autonomous vehicles, big data - Walmart - Pop Tarts, business intelligence, call centre, Clayton Christensen, cloud computing, commoditize, crowdsourcing, disintermediation, diversification, Douglas Engelbart, Douglas Engelbart, future of work, Google Glasses, Google X / Alphabet X, Infrastructure as a Service, intangible asset, Internet of things, invention of writing, inventory management, iterative process, Jeff Bezos, job satisfaction, Kevin Kelly, Kickstarter, late fees, Lyft, Mark Zuckerberg, Oculus Rift, pirate software, ride hailing / ride sharing, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, six sigma, software as a service, software patent, Steve Jobs, subscription business, 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. The internal team reviews this data every twenty minutes and reviews daily trends each morning.
Macy’s has moved much faster than many of its competitors to leverage digital technologies. 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. This includes not only the ones your company currently relates to—your customers, employees, and investors—but also those that you want to build relationships with down the road.
Big Data Glossary by Pete Warden
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. It’s a sign of where data processing solutions are headed, as we get better at building interfaces and moving to higher and more powerful abstraction levels.
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, David Heinemeier Hansson, eurozone crisis, Firefox, Florence Nightingale: pie chart, game design, Google Earth, Hans Rosling, information asymmetry, Internet Archive, John Snow's cholera map, Julian Assange, linked data, moral hazard, MVC pattern, New Journalism, openstreetmap, Ronald Reagan, Ruby on Rails, Silicon Valley, social graph, SPARQL, text mining, web application, WikiLeaks
Instead these new labels are just ways of characterizing a shift that has been gaining strength over decades. 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. Every member has it’s own set of preferences and this great variety is both our strength and our weakness.
Bank 3.0: Why Banking Is No Longer Somewhere You Go but Something You Do by Brett King
3D printing, additive manufacturing, Airbus A320, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, asset-backed security, augmented reality, barriers to entry, 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, M-Pesa, Mark Zuckerberg, mass affluent, Metcalfe’s law, microcredit, mobile money, more computing power than Apollo, Northern Rock, Occupy movement, 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, Tim Cook: Apple, transaction costs, underbanked, US Airways Flight 1549, web application
See also (http://www.businesswire.com/news/home/20111102005712/en/Phone-Bank, http://whatjapanthinks.com/2010/03/20/almost-two-thirds-use-net-banking-in-japan/) 15 For a definition of the Information Age, see http://en.wikipedia.org/wiki/Information_Age 16 A.H. 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.
Don’t opt for a simple banner ad approach here as all you’ll do is upset your most valuable audience. 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”.
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, Donald Trump, Edward Snowden, Fall of the Berlin Wall, housing crisis, illegal immigration, index card, megacity, Mikhail Gorbachev, new economy, offshore financial centre, Pearl River Delta, Port of Oakland, RAND corporation, Ronald Reagan, Silicon Valley, South China Sea, special economic zone, stem cell, union organizing, young professional, éminence grise
One of its triumphs was the $700 million contract it signed with China Mobile Communications Corporation, whose networks cover thirty Chinese provinces including Canton, Zhejiang, Fujian, Jiangsu and Shandong. 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.
Like twenty-first-century China, it has time on its side. 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. One could write an entire book about the company, which has in fact published several books itself, celebrating its multiple successes; these can be found in any Chinese bookstore.
Unlike the countries out of which other operators work, in China there is no control over data protection. 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, invisible hand, popular electronics, side project, Silicon Valley, Silicon Valley startup, William Shockley: the traitorous eight
(The refund didn't go far enough: Busicom died in bankruptcy a few years later.) 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. Aiming to power the Datapoint terminal, TI's Gary Boone designed and patented his own take on a microprocessor, finishing soon enough to demonstrate a working model in March 1971.
Recalling the secretive ways of Magnavox, Fosnough taped paper over the hall windows of the small lab while he built his prototype controller. 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. Fosnough reckoned that Essex competitors would be sure to copy the concept.
The Secret World of Oil by Ken Silverstein
business intelligence, clean water, corporate governance, corporate raider, Donald Trump, energy security, Exxon Valdez, failed state, Google Earth, offshore financial centre, oil shock, paper trading, rolodex, Ronald Reagan, WikiLeaks, Yom Kippur War
Nye also offered advice to Saif Gaddafi, the colonel’s son, on the dissertation he wrote for the London School of Economics. 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.”
The Genius Within: Unlocking Your Brain's Potential by David Adam
Albert Einstein, business intelligence, cognitive bias, Flynn Effect, job automation, John Conway, knowledge economy, lateral thinking, Mark Zuckerberg, meta analysis, meta-analysis, placebo effect, randomized controlled trial, 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.
But it also goes further and explicitly positions them as superior measures of mental and cognitive abilities, different from and more important than IQ (which they’re not). 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’. They are presented as independent of ‘academic’ intelligence – it doesn’t matter how you did in tests and exams at school or if a teacher or friend was once rude about your brain power, you can still make something of yourself.
CTOs at Work by Scott Donaldson, Stanley Siegel, Gary Donaldson
Amazon Web Services, bioinformatics, business intelligence, business process, call centre, centre right, cloud computing, computer vision, connected car, crowdsourcing, data acquisition, distributed generation, domain-specific language, glass ceiling, orbital mechanics / astrodynamics, pattern recognition, Pluto: dwarf planet, QR code, Richard Feynman, Ruby on Rails, shareholder value, Silicon Valley, Skype, smart grid, smart meter, software patent, thinkpad, web application, zero day, zero-sum game
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. 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.
Donaldson: As CTO, what do you see are transformative and emerging technologies that need to be tracked? Cherches: Definitely cloud, definitely virtualization. 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. S. Donaldson: How about cyber security? Cherches: Cyber security—well, that's a big subject. I always talk about one day walking into the office and everything is wiped out.
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, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Ben Horowitz, bioinformatics, bitcoin, Black Swan, blockchain, Burning Man, business intelligence, business process, call centre, chief data officer, Chris Wanstrath, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, commoditize, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, Dean Kamen, dematerialisation, discounted cash flows, disruptive innovation, distributed ledger, Edward Snowden, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, 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, means of production, 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, prediction markets, profit motive, publish or perish, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Silicon Valley, skunkworks, Skype, smart contracts, Snapchat, social software, software is eating the world, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, subscription business, supply-chain management, TaskRabbit, telepresence, telepresence robot, Tony Hsieh, transaction costs, Travis Kalanick, Tyler Cowen: Great Stagnation, uber lyft, urban planning, 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. What, exactly, should learning Dashboards track?
Hence the need for the chief data officer, whose primary focus is managing data, finding the actionable information within, and then delivering it quickly, securely and in a useful form to every stakeholder in the organization. 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).
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, database schema, DevOps, en.wikipedia.org, Firefox, Flash crash, Gini coefficient, illegal immigration, iterative process, labor-force participation, loose coupling, natural language processing, Netflix Prize, quantitative trading / quantitative ﬁnance, recommendation engine, selection bias, sentiment analysis, statistical model, supply-chain management, survivorship bias, text mining, too big to fail, web application
These were the types of systems that I encountered as I started on my path to develop models for predicting homicides and shootings. 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. It was assumed if a small piece of data lacked veracity, all of the data needed to be disqualified.
We don’t know what we have, and we’ve never done any analysis before. We have an idea of what we have, but we’ve never done any analysis before. 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.
Squeezed: Why Our Families Can't Afford America by Alissa Quart
Affordable Care Act / Obamacare, Airbnb, Automated Insights, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, business intelligence, Donald Trump, Downton Abbey, East Village, Elon Musk, full employment, future of work, gig economy, glass ceiling, haute couture, income inequality, Jaron Lanier, job automation, late capitalism, Lyft, minimum wage unemployment, moral panic, new economy, nuclear winter, obamacare, Ponzi scheme, post-work, precariat, price mechanism, rent control, ride hailing / ride sharing, school choice, sharing economy, Silicon Valley, Skype, Snapchat, surplus humans, TaskRabbit, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, union organizing, universal basic income, upwardly mobile, wages for housework, women in the workforce, working poor
A former staff attorney at one document review mill—who bitterly called the employees “doc monkeys”—described Dickensian scenes of workers being bussed deep into Ohio or Pennsylvania. 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. When e-discovery systems, like the ones on display at a legal tech fair, get more sophisticated, even these jobs will be gone, but first they will linger on for a few years, while lawyers are paid less and less.
., 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. See Second act industry Careforce, 254 “Caregiver penalty,” 15–17 Care work automation of, 243–47 devaluation framework, 76–77, 128–30 ethics of, 260–62 loneliness of, 203–5 “love and money” framework, 77–78 reframing of, 259–61, 285n Care worker cooperatives and unions, 158–59, 259–60 Carle, Eric, 7 Carrere, Emmanuel, 288n Castillo, Bonnie, 233–35 Center for American Progress, 177 Center for Family Life (CFL), 158–59 Center for WorkLife Law, 13, 257 Cerasoli, Mary-Faith, 57–58 Chain migration, 120 Chicago Police Department, 177 Child care.
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, 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, crowdsourcing, cryptocurrency, demand response, disintermediation, don't be evil, en.wikipedia.org, fault tolerance, fiat currency, financial innovation, Google Glasses, high net worth, informal economy, 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, Satoshi Nakamoto, Silicon Valley, smart cities, social intelligence, software as a service, Steve Jobs, strong AI, Stuxnet, trade route, unbanked and underbanked, underbanked, upwardly mobile, 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.
Service Design Patterns: Fundamental Design Solutions for SOAP/WSDL and RESTful Web Services by Robert Daigneau
Amazon Web Services, business intelligence, business process, continuous integration, create, read, update, delete, en.wikipedia.org, fault tolerance, loose coupling, MITM: man-in-the-middle, MVC pattern, pull request, RFC: Request For Comment, Ruby on Rails, software as a service, web application
Unfortunately, this shifts the burden over to the client, and clients may have to use a Message Translator [EIP] to convert their messages to the canonical form. 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 enables the service’s contract and canonical data model to vary independently.
ESBs may also perform other generic functions like authentication, authorization, and logging on behalf of target services. 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.
PostgreSQL 9 Admin Cookbook: Over 80 Recipes to Help You Run an Efficient PostgreSQL 9. 0 Database by Simon Riggs, Hannu Krosing
This activity is where you might reconsider current index choices. Many of these activities are mentioned in this chapter or throughout the rest of the cookbook. 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. So, we are specifically avoiding all discussion on ETL tools, EAI tools, inter-database migration, data warehousing strategies, and so on.
PostgreSQL Administration Essentials by Hans-Jurgen Schonig
I would like to thank Packt Publishing for giving me the opportunity to review this wonderful book and for helping me learn new things. Sheldon E. 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. Steve Perkins is the author of the book Hibernate Search by Example, Packt Publishing, and has over 15 years of experience working with enterprise Java.
The Industries of the Future by Alec Ross
23andMe, 3D printing, Airbnb, algorithmic trading, AltaVista, Anne Wojcicki, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, bioinformatics, bitcoin, blockchain, Brian Krebs, British Empire, business intelligence, call centre, carbon footprint, cloud computing, collaborative consumption, connected car, corporate governance, Credit Default Swap, cryptocurrency, David Brooks, disintermediation, Dissolution of the Soviet Union, distributed ledger, Edward Glaeser, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, fiat currency, future of work, global supply chain, Google X / Alphabet X, industrial robot, Internet of things, invention of the printing press, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Joi Ito, Kickstarter, knowledge economy, knowledge worker, lifelogging, litecoin, M-Pesa, Marc Andreessen, Mark Zuckerberg, Mikhail Gorbachev, mobile money, money: store of value / unit of account / medium of exchange, Nelson Mandela, new economy, 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, The Future of Employment, Travis Kalanick, underbanked, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, Y Combinator, young professional
“Data: The Raw Material of the Information Age” and “The Geography of Future Markets” (chapters 5 and 6) examine both the expansiveness that big data will allow and the constraints that geopolitics will place on the global marketplace. 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. In the 20th century, the dominant divide between political systems and markets was along the axis of left versus right.
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. Academics have likened it to both a microscope and telescope—a tool that allows us to both examine smaller details than could previously be observed and to see data at a larger scale, revealing correlations that were previously too distant for us to notice.
The Einstein of Money: The Life and Timeless Financial Wisdom of Benjamin Graham by Joe Carlen
Albert Einstein, asset allocation, Bernie Madoff, 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, laissez-faire capitalism, margin call, means of production, Norman Mailer, oil shock, post-industrial society, price anchoring, price stability, reserve currency, Robert Shiller, Robert Shiller, the scientific method, Vanguard fund, young professional
As Graham and Dodd wrote in Security Analysis: Deliberate falsification of the data is rare; most of the misrepresentation flows from the use of accounting artifices which it is the function of the capable analyst to detect.51 Indeed, much of Security Analysis pertains to deciphering financial statements. 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….
NumPy Cookbook by Ivan Idris
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. I would like to take this opportunity to thank the reviewers and the team at Packt for making this book possible.
Blackwater: The Rise of the World's Most Powerful Mercenary Army by Jeremy Scahill
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, Kickstarter, Naomi Klein, private military company, Project for a New American Century, Robert Bork, Ronald Reagan, school choice, school vouchers, stem cell, urban planning, 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. In February 2003, as the United States prepared to invade Iraq, a producer at CNN’s Spanish-language channel contacted Pizarro and asked him to come to the network’s Washington bureau to apply for a possible position with the network as a commentator on the war.
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, computer vision, continuous integration, en.wikipedia.org, general-purpose programming language, Guido van Rossum, information retrieval, Internet of things, invention of the printing press, iterative process, 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.
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, Everything should be made as simple as possible, Hans Rosling, Kickstarter, lateral thinking, Richard Feynman, Silicon Valley, 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).
The Dhandho Investor: The Low-Risk Value Method to High Returns by Mohnish Pabrai
asset allocation, backtesting, beat the dealer, Black-Scholes formula, business intelligence, call centre, cuban missile crisis, discounted cash flows, Edward Thorp, Exxon Valdez, fixed income, hiring and firing, index fund, inventory management, Mahatma Gandhi, merger arbitrage, passive investing, price mechanism, Silicon Valley, time value of money, transaction costs, zero-sum game
See also specific principles Discounted cash flow calculations Distressed businesses, investing in Dollar-cost averaging, Magic Formula and Dow Jones Industrial Average, declines of Durable moats, investing in businesses with E EDGAR system Efficient Market Theory Einstein, Albert Existing businesses, investing in F Fama, Eugene Few bets/big bets/infrequent bets, investing and Financial information sources/business publications Ford Motor Company Fortune Fortune’s Formula (Poundstone) French, Ken Frontline Ltd. 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).
Python Network Programming Cookbook by M. Omar Faruque Sarker
I would also like to thank my Google Summer of Code mentor, Patirica Tressel. 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. www.PacktPub.com Support files, eBooks, discount offers, and more You might want to visit www.PacktPub.com for support files and downloads related to your book.
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, Flash crash, Googley, Grace Hopper, Infrastructure as a Service, Innovator's Dilemma, inventory management, Julian Assange, knowledge worker, Mark Zuckerberg, Nicholas Carr, rolodex, 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
You can imagine, as an employee, if I have that information at my fingertips, I’m a lot better equipped to answer those questions. One of the things we’ve started doing is tweeting what the status is on our outages. 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. But there are sensors that allow us to look at our equipment and better tell if you’ve got a transformer that’s getting ready to blow.
., 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.
I'm Feeling Lucky: The Confessions of Google Employee Number 59 by Douglas Edwards
Albert Einstein, AltaVista, Any sufficiently advanced technology is indistinguishable from magic, barriers to entry, book scanning, Build a better mousetrap, Burning Man, business intelligence, call centre, commoditize, crowdsourcing, don't be evil, Elon Musk, fault tolerance, Googley, gravity well, invisible hand, Jeff Bezos, job-hopping, John Markoff, Kickstarter, Marc Andreessen, Menlo Park, microcredit, music of the spheres, Network effects, PageRank, performance metric, pets.com, Ralph Nader, risk tolerance, second-price auction, side project, Silicon Valley, Silicon Valley startup, slashdot, stem cell, Superbowl ad, Y2K
My respect for our two capricious, obstinate, provocative, and occasionally juvenile founders increased tenfold that day. 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. Eric, on the other hand, was the voice of corporate pragmatism. These grand schemes would have to be paid for somehow.
John Barabino, who would head the syndication effort once the AOL deal was completed, became part of the team the day he was hired in February 2002. 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.
97 Things Every Programmer Should Know by Kevlin Henney
A Pattern Language, active measures, business intelligence, commoditize, continuous integration, crowdsourcing, database schema, deliberate practice, domain-specific language, don't repeat yourself, Donald Knuth, fixed income, general-purpose programming language, Grace Hopper, index card, inventory management, job satisfaction, loose coupling, Silicon Valley, sorting algorithm, The Wisdom of Crowds
He first worked as a programmer in 1980 writing applications for estate agents and solicitors in compiled BASIC on an Apple IIe. 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.
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, blockchain, Build a better mousetrap, business cycle, business intelligence, business process, call centre, cloud computing, cognitive dissonance, connected car, death of newspapers, digital twin, double entry bookkeeping, Elon Musk, factory automation, fiat currency, Internet of things, inventory management, iterative process, Jeff Bezos, Kevin Kelly, Lean Startup, Lyft, manufacturing employment, minimum viable product, natural language processing, Network effects, Nicholas Carr, nuclear winter, pets.com, profit maximization, race to the bottom, ride hailing / ride sharing, Sand Hill Road, shareholder value, Silicon Valley, skunkworks, smart meter, social graph, software as a service, spice trade, Steve Ballmer, Steve Jobs, subscription business, Tim Cook: Apple, transport as a service, Uber and Lyft, uber lyft, Y2K, Zipcar
As SurveyMonkey continues to grow, I look forward to seeing its next smart acquisition. 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. Subscription businesses need to constantly be optimizing revenue through pricing. In our experience, we see this philosophy reflected by companies that, generally, update their pricing at least annually (which means that they’re thinking about pricing constantly throughout the year).
Numpy Beginner's Guide - Third Edition by Ivan Idris
algorithmic trading, business intelligence, Conway's Game of Life, correlation coefficient, Debian, discrete time, en.wikipedia.org, general-purpose programming language, Khan Academy, p-value, random walk, reversible computing, time value of money
Birwatkar Reviewers Proofreader Alexandre Devert Safs Editng Davide Fiacconi Ardo Illaste Indexer Rekha Nair Commissioning Editor Amarabha Banerjee Graphics Sheetal Aute Acquisiton Editors Jason Monteiro Shaon Basu Usha Iyer Producton Coordinator Rebecca Youe Aparna Bhagat Content Development Editor Cover Work Aparna Bhagat Neeshma Ramakrishnan Technical Editor Rupali R. 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/ . I would like to take this opportunity to thank the reviewers and the team at Packt Publishing for making this book possible.
Everydata: The Misinformation Hidden in the Little Data You Consume Every Day by John H. Johnson
Affordable Care Act / Obamacare, Black Swan, business intelligence, Carmen Reinhart, cognitive bias, correlation does not imply causation, Daniel Kahneman / Amos Tversky, 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, 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. Dr. Johnson is a frequent presenter on economic topics and the use of data, and has also authored numerous papers across his areas of expertise.
Sex, Lies, and Pharmaceuticals: How Drug Companies Plan to Profit From Female Sexual Dysfunction by Ray Moynihan, Barbara Mintzes
A key aim was to win widespread acceptance of the idea that a woman’s common sexual difficulties might be the sign of a treatable dysfunction. 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. However, a leaked copy described how drug companies were ‘expanding the patient pool’ by using marketing campaigns to change public perceptions about things that used to be considered part of normal life.
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, business intelligence, call centre, crowdsourcing, information retrieval, Internet of things, performance metric, QR code, recommendation engine, RFID, search engine result page, semantic web, Silicon Valley, social graph, social web, speech recognition, text mining, 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. Josh Clark is a designer, developer, and author specializing in mobile design strategy and user experience.
The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers by Ben Horowitz
Airbnb, Ben Horowitz, business intelligence, cloud computing, financial independence, Google Glasses, hiring and firing, Isaac Newton, Jeff Bezos, Marc Andreessen, Mark Zuckerberg, move fast and break things, move fast and break things, new economy, nuclear winter, Peter Thiel, Productivity paradox, random walk, Ronald Reagan, Silicon Valley, six sigma, Steve Ballmer, Steve Jobs
FINAL THOUGHT You might think that so much time spent on promotions and titles places too much importance and focus on silly formalisms. The opposite is true. 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. When I was a CEO, this was one of the most difficult lessons for me to learn.
The Happiness Industry: How the Government and Big Business Sold Us Well-Being by William Davies
1960s counterculture, Airbnb, business intelligence, corporate governance, dematerialisation, experimental subject, Exxon Valdez, Frederick Winslow Taylor, Gini coefficient, income inequality, intangible asset, invisible hand, joint-stock company, lifelogging, market bubble, mental accounting, nudge unit, Panopticon Jeremy Bentham, Philip Mirowski, profit maximization, randomized controlled trial, Richard Thaler, road to serfdom, Ronald Coase, Ronald Reagan, science of happiness, 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 intelligence, Social Responsibility of Business Is to Increase Its Profits, Steve Jobs, The Chicago School, The Spirit Level, theory of mind, urban planning, Vilfredo Pareto
Over the next four years, it would open further offices in India, South Africa, Australia, Canada and Japan. 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.
Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies by Reid Hoffman, Chris Yeh
activist fund / activist shareholder / activist investor, Airbnb, Amazon Web Services, autonomous vehicles, bitcoin, blockchain, Bob Noyce, business intelligence, Chuck Templeton: OpenTable:, cloud computing, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, database schema, discounted cash flows, Elon Musk, Firefox, forensic accounting, George Gilder, global pandemic, Google Hangouts, Google X / Alphabet X, hydraulic fracturing, Hyperloop, inventory management, Isaac Newton, Jeff Bezos, Joi Ito, Khan Academy, late fees, Lean Startup, Lyft, M-Pesa, Marc Andreessen, margin call, Mark Zuckerberg, minimum viable product, move fast and break things, move fast and break things, Network effects, Oculus Rift, oil shale / tar sands, Paul Buchheit, Paul Graham, Peter Thiel, pre–internet, recommendation engine, ride hailing / ride sharing, Sam Altman, Sand Hill Road, Saturday Night Live, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart grid, social graph, software as a service, software is eating the world, speech recognition, stem cell, Steve Jobs, subscription business, Tesla Model S, thinkpad, transaction costs, transport as a service, Travis Kalanick, Uber for X, uber lyft, web application, winner-take-all economy, Y Combinator, yellow journalism
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, barriers to entry, Berlin Wall, big data - Walmart - Pop Tarts, Black Swan, book scanning, business intelligence, business process, call centre, cloud computing, computer age, correlation does not imply causation, dark matter, double entry bookkeeping, Eratosthenes, Erik Brynjolfsson, game design, 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, Mark Zuckerberg, 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, post-materialism, random walk, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, smart grid, smart meter, social graph, speech recognition, Steve Jobs, Steven Levy, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Thomas Davenport, Turing test, Watson beat the top human players on Jeopardy!
It’s not that the system doesn’t know the actual total; it’s that as the scale increases, showing the exact figure is less important. 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.
The Four: How Amazon, Apple, Facebook, and Google Divided and Conquered the World by Scott Galloway
activist fund / activist shareholder / activist investor, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Amazon Web Services, Apple II, autonomous vehicles, barriers to entry, Ben Horowitz, Bernie Sanders, big-box store, Bob Noyce, Brewster Kahle, business intelligence, California gold rush, cloud computing, commoditize, cuban missile crisis, David Brooks, disintermediation, don't be evil, Donald Trump, Elon Musk, follow your passion, future of journalism, future of work, global supply chain, Google Earth, Google Glasses, Google X / Alphabet X, Internet Archive, invisible hand, Jeff Bezos, Jony Ive, Khan Academy, longitudinal study, Lyft, Mark Zuckerberg, meta analysis, 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, Robert Shiller, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, shareholder value, Silicon Valley, Snapchat, software is eating the world, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Stewart Brand, supercomputer in your pocket, Tesla Model S, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, undersea cable, Whole Earth Catalog, winner-take-all economy, working poor, young professional
So, if not naturally great, what behaviors help achieve the extra 10 percent? The fundamentals won’t change. 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. It’s an interesting environment to observe people and witness how their core personalities drive success and failure.
How to Fix Copyright by William Patry
A Declaration of the Independence of Cyberspace, barriers to entry, big-box store, borderless world, business cycle, business intelligence, citizen journalism, cloud computing, commoditize, creative destruction, crowdsourcing, death of newspapers, en.wikipedia.org, facts on the ground, Frederick Winslow Taylor, George Akerlof, Gordon Gekko, haute cuisine, informal economy, invisible hand, Joseph Schumpeter, Kickstarter, knowledge economy, lone genius, means of production, moral panic, new economy, road to serfdom, Ronald Coase, Ronald Reagan, semantic web, shareholder value, Silicon Valley, The Chicago School, The Wealth of Nations by Adam Smith, trade route, transaction costs, trickle-down economics, winner-take-all economy, zero-sum game
Moreover, as the Hargreaves review in the UK observed in May 2011, research has found “no evidence that changes since the launch of the original Napster ﬁle sharing in 1999/2000 have affected the quantity of new recorded music or artists coming to market.”34 In too many EFFECTIVE GLOBAL COPYRIGHT LAWS 261 cases, then, the problem arises from copyright owners’ lack of interest in fulﬁlling market demand. No law can cure this failure. But there is hope for the future. 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.
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson, Andrew McAfee
"Robert Solow", 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, 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, British Empire, business cycle, business intelligence, business process, call centre, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, corporate governance, creative destruction, crowdsourcing, David Ricardo: comparative advantage, digital map, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, 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, global village, 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, 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, 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, pattern recognition, Paul Samuelson, payday loans, post-work, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Rodney Brooks, Ronald Reagan, 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 Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen: Great Stagnation, Vernor Vinge, 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.”
Python for Finance by Yuxing Yan
asset-backed security, business cycle, business intelligence, capital asset pricing model, constrained optimization, correlation coefficient, distributed generation, diversified portfolio, implied volatility, market microstructure, P = NP, p-value, quantitative trading / quantitative ﬁnance, Sharpe ratio, 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. WIXESYS' power tools include revolutionary Excel and Outlook add-ons available at http://spearian.com.
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, autonomous vehicles, Bakken shale, barriers to entry, business cycle, business intelligence, Carmen Reinhart, central bank independence, cloud computing, corporate governance, creative destruction, crowdsourcing, demographic dividend, deskilling, disintermediation, disruptive innovation, distributed generation, 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, 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 skilled workers, Lyft, M-Pesa, mass immigration, megacity, mobile money, Mohammed Bouazizi, Network effects, new economy, New Urbanism, oil shale / tar sands, oil shock, old age dependency ratio, openstreetmap, peer-to-peer lending, pension reform, private sector deleveraging, purchasing power parity, quantitative easing, recommendation engine, Report Card for America’s Infrastructure, RFID, ride hailing / ride sharing, 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, supply-chain management, 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.
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, discrete time, disruptive innovation, George Gilder, Google Earth, Infrastructure as a Service, Internet Archive, Internet of things, invisible hand, knowledge economy, late capitalism, lifelogging, linked data, longitudinal study, Masdar, means of production, Nate Silver, natural language processing, openstreetmap, pattern recognition, platform as a service, recommendation engine, RFID, semantic web, sentiment analysis, 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, the scientific method, The Signal and the Noise by Nate Silver, transaction costs
In the case of lists, these are mostly rented and in many cases the renter does not receive the list, with a third-party service bureau preparing and sending mail on their behalf (CIPPIC 2006). 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).
The End of College: Creating the Future of Learning and the University of Everywhere by Kevin Carey
Albert Einstein, barriers to entry, Bayesian statistics, Berlin Wall, business cycle, business intelligence, carbon-based life, Claude Shannon: information theory, complexity theory, David Heinemeier Hansson, declining real wages, deliberate practice, discrete time, disruptive innovation, double helix, Douglas Engelbart, Douglas Engelbart, Downton Abbey, Drosophila, Firefox, Frank Gehry, Google X / Alphabet X, informal economy, invention of the printing press, inventory management, John Markoff, Khan Academy, Kickstarter, low skilled workers, Lyft, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, natural language processing, Network effects, open borders, pattern recognition, Peter Thiel, pez dispenser, 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
It turned out that women in different parts of the country have different preferences for bra colors and sizes. 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.
Running Money by Andy Kessler
Andy Kessler, Apple II, bioinformatics, Bob Noyce, British Empire, business intelligence, buy and hold, buy low sell high, call centre, Corn Laws, Douglas Engelbart, family office, full employment, 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, knowledge worker, Leonard Kleinrock, Long Term Capital Management, mail merge, Marc Andreessen, margin call, market bubble, Maui Hawaii, Menlo Park, Metcalfe’s law, 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, Toyota Production System, zero-sum game
I made a note to myself to avoid NetZero. “I gotta go,” I told him. “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. The stock imploded, the CEO not only ﬁred but forced to renege on some huge charitable donations he’d made with Microstrategy shares. 178 Running Money “What’s with these software companies?”
Netflixed: The Epic Battle for America's Eyeballs by Gina Keating
activist fund / activist shareholder / activist investor, barriers to entry, business intelligence, collaborative consumption, corporate raider, inventory management, Jeff Bezos, late fees, Mark Zuckerberg, McMansion, Menlo Park, 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, telemarketer, X Prize
The favor with which Hastings treated Kilgore set up a rivalry with McCarthy, who correctly saw Kilgore as the competition for the top job at Netflix. 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.
Martin Kleppmann-Designing Data-Intensive Applications. The Big Ideas Behind Reliable, Scalable and Maintainable Systems-O’Reilly (2017) by Unknown
active measures, Amazon Web Services, bitcoin, blockchain, business intelligence, business process, c2.com, cloud computing, collaborative editing, commoditize, conceptual framework, cryptocurrency, database schema, DevOps, distributed ledger, Donald Knuth, Edward Snowden, Ethereum, ethereum blockchain, fault tolerance, finite state, Flash crash, full text search, general-purpose programming language, informal economy, information retrieval, Internet of things, iterative process, John von Neumann, Kubernetes, loose coupling, Marc Andreessen, microservices, natural language processing, Network effects, 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, statistical model, undersea cable, web application, WebSocket, wikimedia commons
For example, if your data is a table of sales transactions, then analytic queries might be: • What was the total revenue of each of our stores in January? • 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) .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.
At the same time, by having the extensibility of being able to run arbi‐ trary code and read data in arbitrary formats, they retain their flexibility advantage. 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. Reusable implementations are emerging: for example, Mahout implements various algorithms for machine learning on top of MapReduce, Spark, and Flink, while MADlib implements similar functionality inside a relational MPP database (Apache HAWQ) . 428 | Chapter 10: Batch Processing Also useful are spatial algorithms such as k-nearest neighbors , which searches for items that are close to a given item in some multi-dimensional space—a kind of simi‐ larity search.
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann
active measures, Amazon Web Services, bitcoin, blockchain, business intelligence, business process, c2.com, cloud computing, collaborative editing, commoditize, conceptual framework, cryptocurrency, database schema, DevOps, distributed ledger, Donald Knuth, Edward Snowden, Ethereum, ethereum blockchain, fault tolerance, finite state, Flash crash, full text search, general-purpose programming language, informal economy, information retrieval, Infrastructure as a Service, Internet of things, iterative process, John von Neumann, Kubernetes, loose coupling, Marc Andreessen, microservices, natural language processing, Network effects, 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, statistical model, undersea cable, web application, WebSocket, wikimedia commons
For example, if your data is a table of sales transactions, then analytic queries might be: What was the total revenue of each of our stores in January? 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) .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.
At the same time, by having the extensibility of being able to run arbitrary code and read data in arbitrary formats, they retain their flexibility advantage. 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. Reusable implementations are emerging: for example, Mahout implements various algorithms for machine learning on top of MapReduce, Spark, and Flink, while MADlib implements similar functionality inside a relational MPP database (Apache HAWQ) .
Top Secret America: The Rise of the New American Security State by Dana Priest, William M. Arkin
airport security, business intelligence, dark matter, drone strike, friendly fire, Google Earth, hiring and firing, illegal immigration, immigration reform, index card, Julian Assange, profit motive, RAND corporation, Ronald Reagan, 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. He’s been following construction projects, job migrations, corporate moves.
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, business cycle, business intelligence, Cass Sunstein, Chelsea Manning, citizen journalism, cloud computing, cognitive dissonance, collective bargaining, conceptual framework, corporate social responsibility, Deng Xiaoping, digital Maoism, don't be evil, Filter Bubble, Firefox, future of journalism, illegal immigration, Jaron Lanier, Jeff Bezos, John Markoff, 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, Richard Stallman, Ronald Reagan, sharing economy, Silicon Valley, Silicon Valley startup, Skype, Steve Crocker, Steven Levy, WikiLeaks
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.
The Trade of Queens by Charles Stross
And then it was booby-trapped and staked out by the FTO. . "Stop right there!" Mike flipped the organizer open and turned to the address divider. "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. It was followed by an odd double beep: some kind of tape position marker, probably.
Forty Signs of Rain by Kim Stanley Robinson
bioinformatics, business intelligence, double helix, experimental subject, Intergovernmental Panel on Climate Change (IPCC), phenotype, prisoner's dilemma, Ronald Reagan, social intelligence, stem cell, the scientific method, zero-sum game
Bantam Books® is a registered trademark of Random House, Inc., 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
WEconomy: You Can Find Meaning, Make a Living, and Change the World by Craig Kielburger, Holly Branson, Marc Kielburger, Sir Richard Branson, Sheryl Sandberg
Airbnb, Albert Einstein, barriers to entry, blood diamonds, business intelligence, business process, carbon footprint, clean water, cleantech, Colonization of Mars, corporate social responsibility, Downton Abbey, Elon Musk, energy transition, family office, future of work, global village, inventory management, James Dyson, job satisfaction, Kickstarter, market design, meta analysis, meta-analysis, microcredit, Nelson Mandela, Occupy movement, pre–internet, shareholder value, sharing economy, Silicon Valley, Snapchat, Steve Jobs, telemarketer, The Fortune at the Bottom of the Pyramid, working poor, Y Combinator
Adding numbers to social impact can elevate it from a knee-jerk emotional act to a results-driven investment. 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. They don't track action plans because they don't expect to gain anything from them.
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, corporate governance, crowdsourcing, Danny Hillis, Deng Xiaoping, don't be evil, Douglas Engelbart, Douglas Engelbart, Electric Kool-Aid Acid Test, European colonialism, Frederick Winslow Taylor, 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, linked data, Livingstone, I presume, lone genius, Menlo Park, Mother of all demos, Norman Mailer, out of africa, packet switching, profit motive, RAND corporation, Ray Kurzweil, Scramble for Africa, self-driving car, semantic web, Silicon Valley, speech recognition, Steve Jobs, Stewart Brand, Ted Nelson, The Death and Life of Great American Cities, the scientific method, Thomas L Friedman, urban planning, Vannevar Bush, Whole Earth Catalog
And, like so many other information technologies, the card catalog delivers its real value at scale. 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.
The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity by Amy Webb
Ada Lovelace, AI winter, Airbnb, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, Asilomar, autonomous vehicles, Bayesian statistics, Bernie Sanders, bioinformatics, blockchain, Bretton Woods, business intelligence, Cass Sunstein, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Deng Xiaoping, distributed ledger, don't be evil, Donald Trump, Elon Musk, Filter Bubble, Flynn Effect, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Inbox Zero, Internet of things, Jacques de Vaucanson, Jeff Bezos, Joan Didion, job automation, John von Neumann, knowledge worker, Lyft, Mark Zuckerberg, Menlo Park, move fast and break things, move fast and break things, natural language processing, New Urbanism, one-China policy, optical character recognition, packet switching, pattern recognition, personalized medicine, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Rodney Brooks, Rubik’s Cube, Sand Hill Road, Second Machine Age, self-driving car, 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, technological singularity, The Coming Technological Singularity, 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 experience was built using generative algorithms to design entirely new worlds for human avatars to explore, evolutionary algorithms for rendering, and deep learning to make all the necessary computations. 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. Within many large companies, human workers have been released from low-level cognitive tasks, while AIs assist staff in certain knowledge fields.
The King of Oil by Daniel Ammann
accounting loophole / creative accounting, anti-communist, Ayatollah Khomeini, banking crisis, Berlin Wall, Boycotts of Israel, business intelligence, buy low sell high, energy security, family office, Johann Wolfgang von Goethe, Mikhail Gorbachev, Nelson Mandela, oil shock, peak oil, purchasing power parity, Ronald Reagan, 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? Who is behind the invitation? What is the reason for the invitation? Does it make sense to you?’
Amazon: How the World’s Most Relentless Retailer Will Continue to Revolutionize Commerce by Natalie Berg, Miya Knights
3D printing, Airbnb, Amazon Web Services, augmented reality, Bernie Sanders, big-box store, business intelligence, cloud computing, Colonization of Mars, commoditize, computer vision, connected car, Donald Trump, Doomsday Clock, Elon Musk, gig economy, Internet of things, inventory management, invisible hand, Jeff Bezos, market fragmentation, new economy, pattern recognition, Ponzi scheme, pre–internet, QR code, race to the bottom, recommendation engine, remote working, sensor fusion, sharing economy, Skype, supply-chain management, TaskRabbit, trade route, underbanked, urban planning, white picket fence
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. Many of the problems we face have no textbook solutions, and so we – happily – invent new approaches… So, how has Amazon’s success tracked so closely against the rise of digital retail in the wake of consumer technology adoption?
Legacy: Gangsters, Corruption and the London Olympics by Michael Gillard
For starters, he has no hard-boiled law enforcement background and is more likely to have a row with a sommelier than a punchy informant in a dive bar. 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. Hill was already on the case when he learned of Mike Law’s freedom of information request.
SQL Hacks by Andrew Cumming, Gordon Russell
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. By chance, no rows in the supplied data set involve a location of East, so Excel does not show the missing column.
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, Apple II, artificial general intelligence, asset allocation, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, congestion charging, crowdsourcing, cryptocurrency, deskilling, different worldview, disruptive innovation, distributed generation, distributed ledger, double helix, drone strike, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, fiat currency, financial exclusion, Flash crash, Flynn Effect, future of work, gig economy, Google Glasses, Google X / Alphabet X, Hans Lippershey, 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, 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, Network effects, new economy, obamacare, Occupy movement, Oculus Rift, off grid, packet switching, pattern recognition, peer-to-peer, Ray Kurzweil, RFID, ride hailing / ride sharing, Robert Metcalfe, 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, social graph, software as a service, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, TaskRabbit, technological singularity, telemarketer, telepresence, telepresence robot, Tesla Model S, The Future of Employment, Tim Cook: Apple, trade route, Travis Kalanick, Turing complete, Turing test, uber lyft, undersea cable, urban sprawl, V2 rocket, Watson beat the top human players on Jeopardy!, white picket fence, WikiLeaks
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.
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, Plutocrats, private military company, RFID, special economic zone, speech recognition, Stewart Brand, telemarketer, the scientific method, young professional
The Major stood along the rear wall, ostensibly a back-office troll. 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. Most of their employees were economists, researchers, and mathematicians. And very few of them had any idea what they were really doing here: preserving the global economy.
Rush Hour: How 500 Million Commuters Survive the Daily Journey to Work by Iain Gately
Albert Einstein, autonomous vehicles, Beeching cuts, blue-collar work, Boris Johnson, British Empire, business intelligence, business process, business process outsourcing, 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, Elon Musk, extreme commuting, global pandemic, Google bus, Henri Poincaré, Hyperloop, Jeff Bezos, lateral thinking, low skilled workers, Marchetti’s constant, postnationalism / post nation state, Ralph Waldo Emerson, remote working, self-driving car, Silicon Valley, stakhanovite, Steve Jobs, telepresence, Tesla Model S, 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. ‘One-handed convenience’ is the contemporary mantra. A commuter should be able to grip a burger or taco in one hand without spilling bits of it into their lap.
Trees on Mars: Our Obsession With the Future by Hal Niedzviecki
"Robert Solow", Ada Lovelace, agricultural Revolution, Airbnb, Albert Einstein, anti-communist, big data - Walmart - Pop Tarts, big-box store, business intelligence, Colonization of Mars, computer age, crowdsourcing, David Brooks, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Flynn Effect, Google Glasses, hive mind, Howard Zinn, if you build it, they will come, income inequality, 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, Mark Zuckerberg, Marshall McLuhan, 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, Ronald Reagan, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, TaskRabbit, technological singularity, technoutopianism, Ted Kaczynski, Thomas L Friedman, Uber and Lyft, uber lyft, 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. In the first week of May last year, Richmond had no homicides compared with three homicides in the same week the prior year.
Beautiful security by Andy Oram, John Viega
Albert Einstein, Amazon Web Services, business intelligence, business process, call centre, cloud computing, corporate governance, credit crunch, crowdsourcing, defense in depth, Donald Davies, en.wikipedia.org, fault tolerance, Firefox, loose coupling, Marc Andreessen, market design, MITM: man-in-the-middle, Monroe Doctrine, new economy, Nicholas Carr, Nick Leeson, Norbert Wiener, 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, statistical model, Steven Levy, The Wisdom of Crowds, Upton Sinclair, web application, web of trust, zero day, Zimmermann PGP
My money is on the mass collection, sharing, and analysis of information and benchmarking. 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.
Understanding Sponsored Search: Core Elements of Keyword Advertising by Jim Jansen
AltaVista, barriers to entry, Black Swan, bounce rate, business intelligence, butterfly effect, call centre, Claude Shannon: information theory, complexity theory, correlation does not imply causation, en.wikipedia.org, first-price auction, information asymmetry, information retrieval, intangible asset, inventory management, life extension, linear programming, longitudinal study, megacity, Nash equilibrium, Network effects, PageRank, place-making, price mechanism, psychological pricing, random walk, Schrödinger's Cat, sealed-bid auction, search engine result page, second-price auction, second-price sealed-bid, sentiment analysis, social web, software as a service, stochastic process, 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!). Button: can have a couple of meanings depending on the context (Source: IAB) (see Chapter 2 model): • clickable graphic that contains certain functionality, such as taking one someplace or executing a program • buttons can also be ads.
Culture & Empire: Digital Revolution by Pieter Hintjens
4chan, airport security, AltaVista, anti-communist, anti-pattern, barriers to entry, Bill Duvall, bitcoin, blockchain, business climate, business intelligence, business process, Chelsea Manning, clean water, commoditize, congestion charging, Corn Laws, correlation does not imply causation, cryptocurrency, Debian, Edward Snowden, failed state, financial independence, Firefox, full text search, German hyperinflation, global village, GnuPG, Google Chrome, greed is good, Hernando de Soto, hiring and firing, informal economy, intangible asset, invisible hand, James Watt: steam engine, Jeff Rulifson, Julian Assange, Kickstarter, M-Pesa, mass immigration, mass incarceration, mega-rich, MITM: man-in-the-middle, mutually assured destruction, Naomi Klein, national security letter, Nelson Mandela, new economy, New Urbanism, Occupy movement, offshore financial centre, packet switching, patent troll, peak oil, pre–internet, private military company, race to the bottom, rent-seeking, reserve currency, RFC: Request For Comment, Richard Feynman, Richard Stallman, Ross Ulbricht, 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.
Reactive Messaging Patterns With the Actor Model: Applications and Integration in Scala and Akka by Vaughn Vernon
A Pattern Language, business intelligence, business process, cloud computing, cognitive dissonance, domain-specific language, en.wikipedia.org, fault tolerance, finite state, 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.
The Fifth Domain: Defending Our Country, Our Companies, and Ourselves in the Age of Cyber Threats by Richard A. Clarke, Robert K. Knake
A Declaration of the Independence of Cyberspace, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, Amazon Web Services, autonomous vehicles, barriers to entry, bitcoin, Black Swan, blockchain, borderless world, business cycle, business intelligence, call centre, Cass Sunstein, cloud computing, cognitive bias, commoditize, computer vision, corporate governance, cryptocurrency, data acquisition, DevOps, don't be evil, Donald Trump, Edward Snowden, Exxon Valdez, global village, immigration reform, Infrastructure as a Service, Internet of things, Jeff Bezos, Julian Assange, Kubernetes, Mark Zuckerberg, Metcalfe’s law, MITM: man-in-the-middle, move fast and break things, move fast and break things, Network effects, open borders, platform as a service, Ponzi scheme, ransomware, Richard Thaler, 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, Tim Cook: Apple, undersea cable, 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.
The Art of SQL by Stephane Faroult, Peter Robson
CODDIV" WHEN 'tfp' = 'ac' THEN t2."CODACT" WHEN 'tfp' = 'gsd' THEN t2."GSD_MNE" WHEN 'tfp' = 'tfp' THEN t2."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. And one understands why having a "date dimension" makes sense, because having a date column in the fact table and expecting that the query tool will transform references to "Q1" into "between January 1 and March 31" to perform an index range scan requires the kind of faith you usually lose when you stop believing in the Tooth Fairy.
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, barriers to entry, Black Swan, business intelligence, call centre, crowdsourcing, disruptive innovation, en.wikipedia.org, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, iterative process, Jeff Bezos, Jony Ive, Kickstarter, knowledge worker, Lean Startup, loose coupling, Marc Andreessen, Mark Zuckerberg, minimum viable product, MITM: man-in-the-middle, move fast and break things, move fast and break things, Network effects, Oculus Rift, Paul Graham, QR code, Ruby on Rails, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, software as a service, software is eating the world, Steve Jobs, Steven Levy, Travis Kalanick, 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.
Samsung Rising: The Inside Story of the South Korean Giant That Set Out to Beat Apple and Conquer Tech by Geoffrey Cain
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, fear of failure, Internet of things, John Markoff, Jony Ive, Kickstarter, Mahatma Gandhi, Mark Zuckerberg, megacity, Mikhail Gorbachev, Nelson Mandela, patent troll, rolodex, shareholder value, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Superbowl ad, Tim Cook: Apple, too big to fail, WikiLeaks, wikimedia commons
He had a graduate degree in engineering from UCLA. 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.
Radical Technologies: The Design of Everyday Life by Adam Greenfield
3D printing, Airbnb, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, blockchain, business intelligence, business process, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, cloud computing, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, cryptocurrency, David Graeber, dematerialisation, digital map, disruptive innovation, distributed ledger, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, global supply chain, global village, Google Glasses, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, James Watt: steam engine, Jane Jacobs, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, late capitalism, 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, Occupy movement, Oculus Rift, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, RAND corporation, recommendation engine, RFID, rolodex, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, 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, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, Whole Earth Review, WikiLeaks, women in the workforce
The fashion executive had her assistant book an Uber for her; while there’s certainly something to be inferred from the fact that she splurged on the Mercedes as usual instead of economizing with a cheaper booking, there’s still some question as to whether this signifies her own impression of her status, or the assistant’s. 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.
Designing Interfaces by Jenifer Tidwell
patternID=image-browsing Dashboard Figure 2-16. 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.
Gnuplot in Action: Understanding Data With Graphs by Philipp Janert
bioinformatics, business intelligence, Debian, general-purpose programming language, iterative process, mandelbrot fractal, pattern recognition, random walk, Richard Stallman, six sigma, survivorship bias
About the author My education is in physics, and I’ve worked as technology consultant, software engineer, technical lead, and project manager, for small startups and in large corporate environments, both in the U.S. and overseas. 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.
Masters of Management: How the Business Gurus and Their Ideas Have Changed the World—for Better and for Worse by Adrian Wooldridge
affirmative action, barriers to entry, Black Swan, blood diamonds, borderless world, business climate, business cycle, business intelligence, business process, carbon footprint, Cass Sunstein, Clayton Christensen, cloud computing, collaborative consumption, collapse of Lehman Brothers, collateralized debt obligation, commoditize, corporate governance, corporate social responsibility, creative destruction, credit crunch, crowdsourcing, David Brooks, David Ricardo: comparative advantage, disintermediation, disruptive innovation, don't be evil, Donald Trump, Edward Glaeser, Exxon Valdez, financial deregulation, Frederick Winslow Taylor, future of work, George Gilder, global supply chain, industrial cluster, intangible asset, job satisfaction, job-hopping, joint-stock company, Joseph Schumpeter, 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, mobile money, Naomi Klein, Netflix Prize, Network effects, new economy, Nick Leeson, Norman Macrae, patent troll, Ponzi scheme, popular capitalism, post-industrial society, profit motive, purchasing power parity, Ralph Nader, recommendation engine, Richard Florida, Richard Thaler, risk tolerance, Ronald Reagan, science of happiness, shareholder value, Silicon Valley, Silicon Valley startup, Skype, Social Responsibility of Business Is to Increase Its Profits, Steve Jobs, Steven Levy, supply-chain management, technoutopianism, The Wealth of Nations by Adam Smith, Thomas Davenport, Tony Hsieh, too big to fail, wealth creators, women in the workforce, young professional, Zipcar
China has created a Chinese Federation for Corporate Social Responsibility. 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.
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, en.wikipedia.org, Firefox, Frederick Winslow Taylor, frictionless, frictionless market, game design, Google X / Alphabet X, Infrastructure as a Service, Internet of things, inventory management, Kickstarter, lateral thinking, Lean Startup, lifelogging, longitudinal study, Marshall McLuhan, minimum viable product, Network effects, pattern recognition, Paul Graham, performance metric, place-making, platform as a service, recommendation engine, ride hailing / ride sharing, rolodex, sentiment analysis, skunkworks, Skype, social graph, social software, software as a service, Steve Jobs, subscription business, telemarketer, transaction costs, two-sided market, Uber for X, web application, Y Combinator
Everyone in your organization should be inspired and encouraged to experiment. 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. “Every day, my staff tells me the ratio of staff costs to gross revenues for the previous day,” he explained.
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, Apple II, Apple's 1984 Super Bowl advert, back-to-the-land, Berlin Wall, British Empire, Brownian motion, Buckminster Fuller, business intelligence, Charles Lindbergh, Claude Shannon: information theory, conceptual framework, connected car, domain-specific language, Douglas Engelbart, Douglas Engelbart, dumpster diving, Extropian, full employment, game design, global village, Haight Ashbury, Howard Rheingold, Jaron Lanier, job automation, John Markoff, John von Neumann, Kevin Kelly, Kubernetes, Marshall McLuhan, Menlo Park, Mitch Kapor, Mother of all demos, new economy, New Journalism, Norbert Wiener, offshore financial centre, oil shale / tar sands, pattern recognition, RAND corporation, Silicon Valley, Simon Singh, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, technoutopianism, Telecommunications Act of 1996, telepresence, The Hackers Conference, Vernor Vinge, Whole Earth Catalog, Whole Earth Review, Y2K, Yom Kippur War, Zimmermann PGP
“Any other juicy stuff is always welcome,” the anonymous voice added. 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.
Super Continent: The Logic of Eurasian Integration by Kent E. Calder
3D printing, air freight, Asian financial crisis, Berlin Wall, blockchain, Bretton Woods, business intelligence, capital controls, Capital in the Twenty-First Century by Thomas Piketty, 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, failed state, Fall of the Berlin Wall, Gini coefficient, 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, smart cities, smart grid, South China Sea, sovereign wealth fund, special drawing rights, special economic zone, supply-chain management, Thomas L Friedman, trade liberalization, trade route, transcontinental railway, UNCLOS, UNCLOS, union organizing, Washington Consensus, working-age population, zero-sum game
See Michael Selby-Green, “‘Europe Should Not Allow the US to Act Over Our Head’: Germany Is Challenging the US’s Financial Monopoly as the Iran Row Deepens,” Business Insider, August 22, 2018, https://www.businessinsider.com/germany-wants-european -rival-to-us-backed-swift-payment-system-2018-8; and Mehreen Khan and Jim Brunsden, “Juncker vows to turn euro into reserve currency to rival US dollar,” Financial Times, September 12, 2018, https://www.ft.com/content/7358f396-b66d-11e8-bbc3-ccd7de085ffe. 44. 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.
Connectography: Mapping the Future of Global Civilization by Parag Khanna
"Robert Solow", 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, 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, charter city, 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 map, disruptive innovation, diversification, Doha Development Round, edge city, Edward Snowden, Elon Musk, energy security, Ethereum, ethereum blockchain, European colonialism, eurozone crisis, failed state, Fall of the Berlin Wall, family office, Ferguson, Missouri, financial innovation, financial repression, fixed income, forward guidance, global supply chain, global value chain, global village, Google Earth, Hernando de Soto, high net worth, 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 cost carrier, low earth orbit, manufacturing employment, mass affluent, mass immigration, megacity, Mercator projection, Metcalfe’s law, microcredit, mittelstand, Monroe Doctrine, mutually assured destruction, 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, plutocrats, Plutocrats, post-oil, post-Panamax, private military company, purchasing power parity, QWERTY keyboard, race to the bottom, Rana Plaza, rent-seeking, reserve currency, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Coase, Scramble for Africa, Second Machine Age, sharing economy, 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, TaskRabbit, 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, transaction costs, UNCLOS, uranium enrichment, urban planning, urban sprawl, WikiLeaks, young professional, zero day
ESRI http://storymaps.arcgis.com/en/ Esri’s Story Map apps can be customized to produce thematic visual stories such as how rapid urban migration has given rise to a world of megacities. 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.
Natural language processing with Python by Steven Bird, Ewan Klein, Edward Loper
bioinformatics, business intelligence, conceptual framework, Donald Knuth, elephant in my pajamas, en.wikipedia.org, finite state, Firefox, Guido van Rossum, information retrieval, Menlo Park, natural language processing, P = NP, search inside the book, speech recognition, statistical model, text mining, Turing test
In this chapter we take a different approach, deciding in advance that we will only look for very specific kinds of information in text, such as the relation between organizations and locations. 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. Information Extraction Architecture Figure 7-1 shows the architecture for a simple information extraction system.
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, game design, impulse control, information asymmetry, inventory management, iterative process, jitney, large denomination, late capitalism, late fees, longitudinal study, means of production, meta analysis, meta-analysis, Nash equilibrium, Panopticon Jeremy Bentham, post-industrial society, postindustrial economy, profit motive, RFID, Silicon Valley, Slavoj Žižek, statistical model, the built environment, yield curve, zero-sum game
At the top right of the screen, casino floor managers may click “get live data” to view Helen’s play at any given moment. Figure 5.2. 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). The visualizations are created by downloading tracked information nightly to a data warehouse, where it is “scrubbed” through whatever parameters a particular casino specifies, a process that primes it to answer certain kinds of questions: What days of week and times of day do women in their thirties with children tend to play?
Beautiful Architecture: Leading Thinkers Reveal the Hidden Beauty in Software Design by Diomidis Spinellis, Georgios Gousios
Albert Einstein, barriers to entry, business intelligence, business process, call centre, continuous integration, corporate governance, database schema, Debian, domain-specific language, don't repeat yourself, Donald Knuth, en.wikipedia.org, fault tolerance, Firefox, general-purpose programming language, iterative process, linked data, locality of reference, loose coupling, meta analysis, meta-analysis, MVC pattern, 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, traveling salesman, Turing complete, type inference, web application, zero-coupon bond
* * *  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, 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. We might need to pull information in from a spreadsheet, a database, and several log files or reports from web analytics software.
The Billionaire's Apprentice: The Rise of the Indian-American Elite and the Fall of the Galleon Hedge Fund by Anita Raghavan
airport security, Asian financial crisis, asset allocation, Bernie Madoff, British Empire, business intelligence, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, delayed gratification, estate planning, Etonian, glass ceiling, high net worth, kremlinology, 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
Rajaratnam wanted Kumar’s morally challenged job description expanded to include verbal subterfuge. 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. Naively, or perhaps simply because it was easier this way for him to rationalize his behavior, Kumar believed that Rajaratnam would buy stocks in three or four companies in the sector and hopefully in a year they would rise and Rajaratnam would make a profit.
Appetite for America: Fred Harvey and the Business of Civilizing the Wild West--One Meal at a Time by Stephen Fried
Albert Einstein, British Empire, business intelligence, centralized clearinghouse, Charles Lindbergh, City Beautiful movement, estate planning, glass ceiling, In Cold Blood by Truman Capote, indoor plumbing, Livingstone, I presume, Nelson Mandela, new economy, plutocrats, 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. He was impressed with her. She was an agile, creative investor. Besides stocks and bonds, she would invest in the work of inventors and sell at just the right time.
Site Reliability Engineering: How Google Runs Production Systems by Betsy Beyer, Chris Jones, Jennifer Petoff, Niall Richard Murphy
Air France Flight 447, anti-pattern, barriers to entry, business intelligence, business process, Checklist Manifesto, cloud computing, combinatorial explosion, continuous integration, correlation does not imply causation, crowdsourcing, database schema, defense in depth, DevOps, en.wikipedia.org, fault tolerance, Flash crash, George Santayana, Google Chrome, Google Earth, information asymmetry, job automation, job satisfaction, Kubernetes, linear programming, load shedding, loose coupling, meta analysis, meta-analysis, microservices, minimum viable product, MVC pattern, performance metric, platform as a service, revision control, risk tolerance, side project, six sigma, the scientific method, Toyota Production System, trickle-down economics, web application, zero day
Glass, Facts and Fallacies of Software Engineering, Addison-Wesley Professional, 2002. [Gol14] W. Golab et al., “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. [Hic11] M. Hickins, “Tape Rescues Google in Lost Email Scare”, in Digits, Wall Street Journal, 1 March 2011.
Architects of Intelligence by Martin Ford
3D printing, agricultural Revolution, AI winter, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, bitcoin, business intelligence, business process, call centre, cloud computing, cognitive bias, Colonization of Mars, computer vision, correlation does not imply causation, crowdsourcing, DARPA: Urban Challenge, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Fellow of the Royal Society, Flash crash, future of work, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Rosling, ImageNet competition, income inequality, industrial robot, information retrieval, job automation, John von Neumann, Law of Accelerating Returns, life extension, Loebner Prize, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, natural language processing, new economy, optical character recognition, pattern recognition, phenotype, Productivity paradox, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, Ted Kaczynski, 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, zero-sum game, Zipcar
competition and demonstrated to the public a really palpable AI capability, all that excitement and momentum helped IBM to organize and integrate all their technology under a single brand. 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. Whether or not they’re providing good value on the business side is a question I can’t answer.
Piracy : The Intellectual Property Wars from Gutenberg to Gates by Adrian Johns
active measures, banking crisis, Berlin Wall, British Empire, Buckminster Fuller, business intelligence, commoditize, 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, informal economy, invention of the printing press, Isaac Newton, James Watt: steam engine, John Harrison: Longitude, Marshall McLuhan, Mont Pelerin Society, new economy, New Journalism, Norbert Wiener, pirate software, Republic of Letters, Richard Stallman, road to serfdom, Ronald Coase, software patent, South Sea Bubble, Steven Levy, Stewart Brand, Ted Nelson, the scientific method, traveling salesman, Whole Earth Catalog
But nothing resembling their drilled “commandoes” had ever been put into the field before. 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. In the early years of the twentieth century, the private antipiracy police raised serious questions about everyday rights and freedoms – questions that many at the time, including prominent legal officers, viewed as seriously as any concerning piracy itself.
Facebook: The Inside Story by Steven Levy
active measures, Airbnb, Airbus A320, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, augmented reality, Ben Horowitz, blockchain, Burning Man, business intelligence, cloud computing, computer vision, crowdsourcing, cryptocurrency, don't be evil, Donald Trump, East Village, Edward Snowden, El Camino Real, Elon Musk, Firefox, Frank Gehry, glass ceiling, indoor plumbing, Jeff Bezos, John Markoff, Jony Ive, Kevin Kelly, Kickstarter, Lyft, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Menlo Park, Metcalfe’s law, MITM: man-in-the-middle, move fast and break things, move fast and break things, natural language processing, Network effects, Oculus Rift, PageRank, Paul Buchheit, paypal mafia, Peter Thiel, pets.com, post-work, Ray Kurzweil, recommendation engine, Robert Mercer, Robert Metcalfe, rolodex, Sam Altman, Sand Hill Road, self-driving car, sexual politics, Shoshana Zuboff, side project, Silicon Valley, Silicon Valley startup, slashdot, Snapchat, social graph, social software, South of Market, San Francisco, Startup school, Steve Ballmer, Steve Jobs, Steven Levy, Steven Pinker, Tim Cook: Apple, web application, WikiLeaks, women in the workforce, Y Combinator, Y2K
“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.
Hadoop: The Definitive Guide by Tom White
Amazon Web Services, bioinformatics, business intelligence, combinatorial explosion, database schema, Debian, domain-specific language, en.wikipedia.org, fault tolerance, full text search, Grace Hopper, information retrieval, Internet Archive, Kickstarter, linked data, loose coupling, openstreetmap, recommendation engine, RFID, SETI@home, social graph, 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. Installing Hive In normal use, Hive runs on your workstation and converts your SQL query into a series of MapReduce jobs for execution on a Hadoop cluster.
Empire of Guns by Priya Satia
banking crisis, British Empire, business intelligence, Corn Laws, deindustrialization, delayed gratification, European colonialism, Fellow of the Royal Society, hiring and firing, interchangeable parts, invisible hand, Isaac Newton, James Watt: steam engine, joint-stock company, Khyber Pass, Menlo Park, Panopticon Jeremy Bentham, rent-seeking, Scramble for Africa, Silicon Valley, spinning jenny, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, transatlantic slave trade, zero-sum game
David Sr.’s daughter Lucy married Samuel Galton Jr., drawing him eventually in the banking direction. 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. Goldsmiths’ business remained inextricable from that of their fellow metallurgists; they dealt in the precious metals that were considered money but also in plated ware and metal toys, which tied them to the hardware trades.
Solr in Action by Trey Grainger, Timothy Potter
business intelligence, cloud computing, commoditize, conceptual framework, crowdsourcing, data acquisition, en.wikipedia.org, failed state, fault tolerance, finite state, full text search, glass ceiling, information retrieval, natural language processing, openstreetmap, performance metric, premature optimization, recommendation engine, web application
The NoSQL feature set was also expanded to include transaction logs, update durability, optimistic concurrency, and atomic updates. 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. With Solr in Action in hand, you too are now well equipped to join the global community and help take Solr to new heights!
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, 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, 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, disintermediation, double helix, Douglas Hofstadter, en.wikipedia.org, epigenetics, factory automation, friendly AI, George Gilder, Gödel, Escher, Bach, informal economy, information retrieval, 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, Norbert Wiener, oil shale / tar sands, optical character recognition, pattern recognition, phenotype, premature optimization, 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, Silicon Valley, Singularitarianism, speech recognition, statistical model, stem cell, Stephen Hawking, Stewart Brand, strong AI, superintelligent machines, technological singularity, Ted Kaczynski, telepresence, The Coming Technological Singularity, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Y2K, Yogi Berra
I have been inundated with thousands of compelling examples. 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.
Masterminds of Programming: Conversations With the Creators of Major Programming Languages by Federico Biancuzzi, Shane Warden
Benevolent Dictator For Life (BDFL), business intelligence, business process, cellular automata, cloud computing, commoditize, complexity theory, conceptual framework, continuous integration, data acquisition, domain-specific language, Douglas Hofstadter, Fellow of the Royal Society, finite state, Firefox, follow your passion, Frank Gehry, general-purpose programming language, Guido van Rossum, HyperCard, information retrieval, iterative process, John von Neumann, Larry Wall, linear programming, loose coupling, Mars Rover, millennium bug, NP-complete, Paul Graham, performance metric, Perl 6, QWERTY keyboard, RAND corporation, randomized controlled trial, Renaissance Technologies, Ruby on Rails, Sapir-Whorf hypothesis, Silicon Valley, slashdot, software as a service, software patent, sorting algorithm, Steve Jobs, traveling salesman, Turing complete, type inference, Valgrind, Von Neumann architecture, web application
The SQL standard has also served to focus the industry’s attention and resources, providing a common framework in which individuals and companies could develop tools, write books, teach courses, and provide consulting services. 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 process, call centre, cloud computing, combinatorial explosion, commoditize, Computer Numeric Control, conceptual framework, database schema, discounted cash flows, en.wikipedia.org, fault tolerance, finite state, friendly fire, hiring and firing, Infrastructure as a Service, inventory management, new economy, packet switching, performance metric, platform as a service, Ponzi scheme, 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. Book Organization and Structure We’ve divided the book into four parts.
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, dark matter, en.wikipedia.org, Firefox, Google Chrome, Google Earth, hypertext link, index card, information retrieval, Internet Archive, Law of Accelerating Returns, linked data, mass immigration, Metcalfe’s law, Network effects, optical character recognition, PageRank, performance metric, risk tolerance, search engine result page, self-driving car, sentiment analysis, social web, sorting algorithm, speech recognition, Steven Levy, text mining, 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. Understanding the strengths and weaknesses of your competition from an SEO perspective is a significant part of devising your own SEO strategy.
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, side project, Silicon Valley, web application
SQL Server 2005, the currently shipping version at this writing, has grown beyond the standard basic database package that includes only the database engine. 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. Overview of SQL Server As with any piece of complex software, SQL Server comes in multiple configurations.
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, experimental subject, Fellow of the Royal Society, Isaac Newton, joint-stock company, music of the spheres, Republic of Letters, scientific mainstream, South Sea Bubble, Thomas Malthus, trade route
Quite often this required working till nearly midnight; in at least one instance, when a slip reduced two set pages to ruin, he worked well into the next morning. 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. Patrick Baird, a surgeon who passed Franklin’s shop regularly, explained that Franklin’s devotion to work excelled anything he had ever seen.