chief data officer

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pages: 374 words: 94,508

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

3D printing, Affordable Care Act / Obamacare, banking crisis, 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,, 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

We’re also starting to see the introduction of novel roles which acknowledge information’s emergence as a key economic asset, including those for the curation, harvesting, or “wrangling” of data sources, for specialized information technologies, for productizing or otherwise directly monetizing information, and for engineering information into digital business solutions. In addition, there have been sightings out there in the wild of “data journalists,” “algorithm librarians,” “information attorneys,” “digital ethicists,” and even “digital prophets,” “hackers in residence,” and rumors of an ominously titled “lord of dark data” somewhere out there. The Chief Data Officer: Foresight, Not Fad The chief data officer role is foresight, not fad. To demonstrate, let’s start with looking at the path to the emergence of the chief data officer role itself. I often speak to individuals including other executives who scoff at the notion of needing another chief somethingorother. Advances in business and management science have always required new kinds of specialist leaders. In the 1940s and 1950s, companies rarely had an executive head of human resources.

—Judd Williams, Chief Information Officer, NCAA Laney’s work redefines information as a true strategic asset, and shows how we CDOs can be instrumental in unlocking new ways for companies to grow and be relevant in the new connected modern economy. —Rajeev Kapur, Chief Data Officer, Kimberly-Clark We will one day look back at Doug’s work and say, it is the groundbreaking work that firmly put data and data leadership in the middle of the business arena not as the white elephant, but as the phoenix: a formal player at the boardroom table. —Althea Davis, Chief Data Officer, ABN AMRO Doug Laney has put together a smart, practical book that applies traditional rules of business economics to the emerging information marketplace. Infonomics is an excellent field guide to knowing what actions can be taken to better measure, manage, and monetize your company’s data assets now and in the future.

—Gokula Mishra, Senior Director, Global Data & Analytics, Supply Chain, McDonald’s Corporation Through a myriad of relevant examples, Doug successfully brings together data management, analytics, and economics in a book that offers practical guidelines to manage, improve, and monetize an organization’s data assets. The book is not only a must read for Chief Data Officers, but for any other executive interested in succeeding in the Information Age. —Leandro Dallemule, Chief Data Officer, AIG Thank you, Doug, for an engaging read and for giving Data a well-deserved “seat at the table.” This is a must have book, not only for CDOs, CIOs, and Data Strategists but, for any executive interested in creating a data driven, info-saavy company. Laney serves up a treasure trove of insights and observations, while also asking some embarrassingly basic questions that beg to be answered by most companies.

pages: 292 words: 85,151

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

23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, 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,, 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

One example: large software implementations, such as ERP systems, are being replaced to a certain degree by specialized SaaS startups that align horizontally with other software offerings via open APIs. As ExOs scale beyond their traditional boundaries, the number of integration and data handoff points is set to explode, making fault traceability increasingly difficult. CDO – Chief Data Officer Brad Peters, co-founder and chairman of Birst and a columnist at, has defined the chief data officer as a newest C-Level profession. Throughout the course of this book we’ve mentioned data extensively: billions of sensors churning out data for algorithms, Big Data solutions, data-driven decisions and value (or Lean) metrics. All organizations today have a dire need to manage and make sense of all this data and to somehow do so without breaching privacy and security laws and customer trust.

Step 2: Join or Create Relevant MTP Communities Step 3: Compose a Team Step 4: Breakthrough Idea Step 5: Build a Business Model Canvas Step 6: Find a Business Model Step 7: Build the MVP Step 8: Validate Marketing and Sales Step 9: Implement SCALE and IDEAS Step 10: Establish the Culture Step 11: Ask Key Questions Periodically Step 12: Building and Maintaining a Platform In Concert Lessons for Enterprise ExOs (EExOs) Chapter Seven: ExOs and Mid-Market Companies Example 1: TED Example 2: GitHub Example 3: Coyote Logistics Example 4: Studio Roosegaarde Retrofitting an ExO Example 5: GoPro Chapter Eight: ExOs for Large Organizations Transform Leadership Education Board Management Implement Diversity Skills and Leadership Partner with, Invest in or Acquire ExOs Disrupt[X]—set up Edge ExOs Inspire ExOs at the Edge Hire a Black Ops Team Copy Google[X] Partner with Accelerators, Incubators and Hackerspaces ExO Lite (The Gentle Cycle) Migrate towards an MTP Community & Crowd Algorithms Engagement Dashboards Experimentation Social Technologies Conclusion Chapter Nine: Big Companies Adapt The Coca-Cola Company – Exponential Pop Haier – Higher and Higher Xiaomi – Showing You and Me The Guardian – Guarding Journalism General Electric – General Excellence Amazon – Clearing the Rainforest of “No” Zappos – Zapping Boredom ING Direct Canada (now Tangerine) – BankING Autonomy Google Ventures – The Almost Perfect EExO Growing with the Crowd Chapter Ten: The Exponential Executive CEO – Chief Executive Officer CMO – Chief Marketing Officer CFO – Chief Financial Officer CTO/CIO – Chief Technology Officer/Chief Information Officer CDO – Chief Data Officer CIO – Chief Innovation Officer COO – Chief Operating Officer CLO - Chief Legal Officer CHRO - Chief Human Resources Officer The World’s Most Important Job Epilogue: A New Cambrian Explosion Afterword Appendix A: What is your Exponential Quotient? Appendix B: Sources and Inspirations About the Authors Acknowledgements Foreword Welcome to a time of exponential change, the most amazing time ever to be alive.

In 2007, Wired magazine editors Gary Wolf and Kevin Kelly created the Quantified Self (QS) movement, which focuses on self-tracking tools. The first Quantified Self conference was held in May 2011, and today the QS community has more than 32,000 members in thirty-eight countries. Many new devices have been spun out of this movement. One of them is Spire, a QS device that measures respiration. Singularity University alumnus Francesco Mosconi is the chief data officer of Spire. The analytics and software he has written are all about real-time feedback regarding breath and how it relates to stress and focus—not unlike the way sensor feedback in a BMW’s traction control system reduces wheel slip. With more than seven billion mobile phones in use globally, many equipped with a high-resolution camera, anything and everything can be recorded in real time, from a baby’s first words to the events of the Arab Spring.

pages: 161 words: 39,526

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,, 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

Retrieved from (45) Zilis, S. & Cham, J. (2016, November 7) The Current State of Machine Intelligence 3.0. O’Reilly. Retrieved from (46) Leaper, B. (2014, July). The Rise of the Chief Data Officer. Wired. Retrieved from (47) Purdy, M., & Daugherty, P. (2016) Artificial intelligence is the future of growth.(2016). Retrieved from (48) Ng, A. (2017, April 21). Hiring Your First Chief AI Officer. Harvard Business Review. Retrieved from (49) Interview with Marina Kalika, Director of Solutions Marketing at Nuance.

Regardless of whether they are your primary AI champion, CIOs will likely play a vital role in implementing AI in an organization due to the need to develop and integrate infrastructure to support AI. ML systems and data mining systems require complex storage, networking, and computing systems that will require the CIO’s input to implement in many enterprises. CDO Since data touches all aspects of enterprises, Chief Data Officers (CDOs) are becoming increasingly common,(46) but their mandate is more often the security, regulation, and governance of enterprise data. Depending on their focus, they typically report to CIOs, CFOs, Chief Risk Officers (CRO), or Chief Security Officers (CSO). Companies that have the CDO report directly to the CEO tend to value data and analytics more highly than those that don’t. Many enterprises started investing in centralized data infrastructure and capabilities less than five years ago, which means many new CDOs are still occupied with the monumental task of laying out their company-wide data initiatives.

For example, Product Management, Sales, and Marketing all want chatbot data on customer interactions, but there is no clear owner when the results are important to all three units. As a result of the need to manage data that is increasing in scope and complexity and being generated by multiple business units across an organization, new jobs specializing in the care and feeding of shared data have appeared. Chief Data Officer (CDOs) and Chief Data Scientist positions are now becoming common in companies, especially those interested in championing new AI investments. For companies that do not have the capacity or the desire to tackle data silos on their own, companies like Maana, Alation, and Tamr offer ML-powered data unification and cataloguing services. Analytics Now that all of the available data has been unearthed and cleaned, you are now ready to leverage the fruits of your labor into sweet, sweet knowledge.

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

We make use of structured and unstructured data, opensource and traditional analytics tools. We’re working on traditional insurance analytics issues like pricing optimization, and some exotic big data problems in collaboration with MIT. It was and will continue to be an integrated approach.”5 We’re already beginning to see more roles of this type, with a ­variety of specific titles. One variation is the chief data officer (CDO) role, which is pretty common in large banks. In ­principle, I think Chapter_06.indd 142 03/12/13 12:24 PM What It Takes to Succeed with Big Data   143 it is a fine idea to combine the responsibility for data m ­ anagement and governance with the application of data—that is, ­ analytics. In practice, however, most of the CDO incumbents seem to spend the great majority of their time on data management and not much on analytics.

Founded in 1812 as City Bank of New York, the financial services conglomerate has evolved to serve 200 million consumer and institutional customers across 160 countries. The wider the company’s reach, the greater the role of big data in its strategy. The subject of corporate information—its integration, its quality, and its growing volumes—was a natural by-product of executive-level conversations around regulatory and competitive demands. In 2010, the company established a Chief Data Office. Shortly thereafter, Chapter_08.indd 187 03/12/13 12:57 PM 188 big data @ work the company downloaded Hadoop and began reengineering computation-heavy data transformations using the big data ­ ­environment. A major focus of the Hadoop implementation is cost reduction. The firm’s plans include expanding that environment to refine its understanding of customer relationships and behaviors.

See analytics business intelligence (BI), 7, 10, 10t, 14, 18, 23, 93, 102, 124, 128, 129, 130 business models, 41–42, 57, 168, 173, 188 business-to-business (B2B) firms, 42t, 43, 45–46 business-to-business-to-consumer (B2B2C) firms, 43, 46 business view, in big data stack, 119t, 123 Caesars Entertainment, 42, 179–180 Cafarella, Mike, 157 Capital One, 42 Carolinas HealthCare, 121, 122 cars driving data on, 52, 198 self-driving, 35, 41, 42, 65, 83, 148 Carter, John, 143 casino industry, 42, 179–180 Charles Schwab Corporation, 143 chief analytics officers, 143, 202 chief data officers (CDOs), 142–143 chief science officers, 142 Chrysler, 83 CIA, 19 Cisco Systems, 47 Citigroup, 187–188 Index.indd 219 Cleveland, William S., 195 cloud-based computing, 55, 89, 117, 163, 169, 192, 200, 208 Cloudera Hadoop, 115 commitment, culture of, 148 communication skills, 88, 92, 93, 99, 102–103 Competing on Analytics (Davenport and Harris), 2, 43 Compute Engine, 163 Concept 2, 12 conservative approach to big data ­adoption, 80, 81 consultants, data scientists as, 81, 98–99, 103–104, 112, 209 consumer products companies, 42, 42t, 43, 46, 54, 71, 82 Consumers Union, 67 Corporate Insight, 109 cost-reduction, 21, 60–63, 145 Coursera, 41 cows, data from, 11–12 credit card data, 37, 38, 42, 42t, 46, 164 culture for big data in organizations, 147–149, 152 customer relationship management (CRM), 54, 129f customers banking industry and, 9, 44, 49, 133 big data’s effect on relationships with, 26–27 business-to-business (B2B) firms and, 43, 45–46 business-to-business-to-consumer (B2B2C) firms and, 43, 46 data-based products and services for, 16, 23–24, 26, 66, 106, 155, 195 as focus of big data efforts, 16 future scenario of big data’s effect on relationships with, 35–38, 41–42, 58 identification of dissatisfaction and possible attrition of, 23, 48, 67, 68, 72, 78, 96, 179, 180, 181, 191 intermediaries reporting information about, 46 managers’ attention to, 21 marketing efforts targeted to, 27, 55, 63–64, 65, 67, 72, 79, 107, 108–109, 128, 142, 144, 179, 180, 197 media and entertainment firms and, 48, 49 03/12/13 2:04 PM 220 Index customers (continued) multichannel relationships with, 51, 67, 177, 186 Netflix Prize’s focus on, 16, 22, 66 overachievers and, 42, 42t, 46 regulatory environment for data from, 27 research on website behavior of, 164 sentiment analysis of, 17, 27, 107, 118, 123 service transaction histories from, 23 sharing data with, 167–168 social media and, 48, 50–51, 107 travel industry and, 75–76 underachievers and, 42t, 43–44 unstructured data from, 51, 67, 68, 69, 180, 186 volume of data warehoused from, 116–117, 168 Cutting, Doug, 157 CycleOps, 12 dashboards, 109, 128, 129, 130, 137, 167, 185, 198 data in big data stack, 119t, 121–122 success of big data initiatives and, 136–138 data disadvantaged organizations, 42t, 43 data discovery process big data strategy and, 70–72, 74–75, 75f, 84 enterprise orientation for, 139 focus of architecture on, 20, 201 GE’s experience with, 75 leadership and, 140 management orientation toward, 18–19 model generation for, 64 moderately aggressive approach to big data and, 82 objectives and, 75, 75f, 84 research on, 3 responsibility locus for, 76–77, 77f technical platform for, 131, 201 Data Lab product, 160 data mining, 122–123, 128, 183, 184 data production process big data strategy and, 70, 72–75, 75f, 84 data scientists and teams and, 201 enterprise orientation for, 139 Index.indd 220 GE’s experience with, 74–75 highly ambitious approach to big data and, 83 moderately aggressive approach to big data and, 82 objectives and, 75, 75f, 84 responsibility locus for, 76–77, 77f technical platform for, 74, 127, 129–130, 132, 133, 201 Data Science Central, 97 data scientists activities performed by, 15, 137–138, 148, 159–160, 199 analysts differentiated from, 15 background to, 86–87, 196–197 business expert traits of, 88 classic model of, 87–97 collaboration by, 165–167, 173, 176 development of products and services and, 16, 18, 20, 24, 61–62, 65, 66, 71, 79–80, 106, 161 education and training of, 14, 91, 92, 104, 184, 209 future for, 110–111 hacker traits of, 88–91 horizontal versus vertical, 97–99 job growth for, 111, 111f, 184–185 in large companies, 201 LinkedIn’s use of, 158, 160, 161 motivation of, 106 organizational structure with, 16, 61, 82, 140, 141, 142, 152, 153, 158, 173, 180, 187, 202, 207, 209 quantitative analyst traits of, 88, 93–97 research on, 3 retention of, 104–106, 112, 161 role of, 14, 209 scientist traits of, 88, 91–92 skills of, 71, 79, 88, 145, 147, 182–184, 185 sources of, for hiring, 101–105 start-ups using, 16, 157–158 team approach using, 99–101, 165–167, 181, 201, 209 traits of, 87, 88 trusted adviser traits of, 88, 92–93 data visualization, 124–125, 125f Davis, Jim, 163–164 DB2, 183.

pages: 398 words: 86,855

Bad Data Handbook by Q. Ethan McCallum

Amazon Mechanical Turk, asset allocation, barriers to entry, Benoit Mandelbrot, business intelligence, cellular automata, chief data officer, Chuck Templeton: OpenTable:, cloud computing, cognitive dissonance, combinatorial explosion, commoditize, conceptual framework, database schema, DevOps,, 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

He earned his Ph.D. in economics from Syracuse University and his undergraduate degree in economics from the University of Wisconsin at Madison. Brett Goldstein is the Commissioner of the Department of Innovation and Technology for the City of Chicago. He has been in that role since June of 2012. Brett was previously the city’s Chief Data Officer. In this role, he lead the city’s approach to using data to help improve the way the government works for its residents. Before coming to City Hall as Chief Data Officer, he founded and commanded the Chicago Police Department’s Predictive Analytics Group, which aims to predict when and where crime will happen. Prior to entering the public sector, he was an early employee with OpenTable and helped build the company for seven years. He earned his BA from Connecticut College, his MS in criminal justice at Suffolk University, and his MS in computer science at University of Chicago.

pages: 464 words: 127,283

Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia by Anthony M. Townsend

1960s counterculture, 4chan, A Pattern Language, Airbnb, Amazon Web Services, anti-communist, Apple II, Bay Area Rapid Transit, Burning Man, business process, call centre, carbon footprint, charter city, chief data officer, clean water, cleantech, cloud computing, computer age, congestion charging, connected car, crack epidemic, crowdsourcing, DARPA: Urban Challenge, data acquisition, Deng Xiaoping, digital map, Donald Davies, East Village, Edward Glaeser, game design, garden city movement, Geoffrey West, Santa Fe Institute, George Gilder, ghettoisation, global supply chain, Grace Hopper, Haight Ashbury, Hedy Lamarr / George Antheil, hive mind, Howard Rheingold, interchangeable parts, Internet Archive, Internet of things, Jacquard loom, Jane Jacobs, jitney, John Snow's cholera map, Joi Ito, Khan Academy, Kibera, Kickstarter, knowledge worker, load shedding, M-Pesa, Mark Zuckerberg, megacity, mobile money, mutually assured destruction, new economy, New Urbanism, Norbert Wiener, Occupy movement, off grid, openstreetmap, packet switching, Panopticon Jeremy Bentham, Parag Khanna, patent troll, Pearl River Delta, place-making, planetary scale, popular electronics, RFC: Request For Comment, RFID, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, smart grid, smart meter, social graph, social software, social web, special economic zone, Steve Jobs, Steve Wozniak, Stuxnet, supply-chain management, technoutopianism, Ted Kaczynski, telepresence, The Death and Life of Great American Cities, too big to fail, trade route, Tyler Cowen: Great Stagnation, undersea cable, Upton Sinclair, uranium enrichment, urban decay, urban planning, urban renewal, Vannevar Bush, working poor, working-age population, X Prize, Y2K, zero day, Zipcar

Applying CompStat techniques to other aspects of government like trash collection and pothole repairs saved the city at least $100 million during Mayor Martin O’Malley’s first term in office.36 One former official puts the savings as high as a half-billion dollars for his entire administration, which ended in 2007.37 Not bad for a system that cost just $20,000 to set up and $350,000 a year to run.38 Tolva’s vision has a convincing air of inevitability. When I asked him to speculate on what big data means for cities in the future, his response was quick and terse. “Governing and policy making based on what the vital signs are telling us, not anecdote,” he said.39 Perhaps not surprisingly, his partner in reinventing Chicago’s government as a data-driven enterprise is himself a crime mapper. The country’s first municipal chief data officer, Brett Goldstein was brought over from the Chicago Police Department where, Tolva says, “he was crunching huge amounts of past crime data to nightly redeploy squads based on probability curves of incidents.” But in his new role Goldstein can look beyond just police reports, at the many other socioeconomic indicators that can help suss out the conditions that foster crime. Tolva believes it will take a culture change in city government to realize the full potential of bigger data and deeper analytics.

They lack the capacity to even negotiate controls over the data streams generated by their citizens as they interact with private vendors’ technologies. Watchdog groups will need to step in and identify where the crucial conflicts lie. (And in fact, the Electronic Frontier Foundation is doing just this on behalf of a number of transit agencies being sued by another transit-arrival patent troll, Luxembourg-based ArrivalStar).18 Cities will need regular audits, perhaps conducted by a chief privacy officer or chief data officer charged with extending public control over government- and citizen-generated data. An intriguing option is to hand off this data to a trust equipped to manage it on behalf of citizens, covering its costs—and possibly generating a revenue stream for the city—by licensing the data. A growing number of start-ups and open-source projects, like the Personal Locker project started by Jeremie Miller, are exploring ways for individuals to control and even pool their private data to trade with companies.

pages: 157 words: 53,125

The Fifth Risk by Michael Lewis

Albert Einstein, Bernie Sanders, chief data officer, cloud computing, Donald Trump, Ferguson, Missouri, Silicon Valley, the new new thing, uranium enrichment

But unlike the Red Sox, it had made little effort to exploit the value in it. The images from the radar stations, for instance. They were on tapes in a basement of a NOAA office in Asheville, North Carolina. To get the data into a form he could use, Friedberg paid NOAA to put it on hard drives and ship them to him. He then moved the data, for free, to the cloud. “That was the first data set we were able to get onto the cloud,” said Ed Kearns, chief data officer at NOAA. “David showed Google and Amazon and Microsoft that there was a business case for taking it. Until we got it up, no one was able to reprocess the data.” Of course, without cloud computing there would have been no place to put the radar data. But once it was on the cloud it was generally accessible and could be used for any purpose. (Ornithologists at Cornell University would soon be using it to study bird migrations.)

Digital Transformation at Scale: Why the Strategy Is Delivery by Andrew Greenway,Ben Terrett,Mike Bracken,Tom Loosemore

Airbnb, bitcoin, blockchain, butterfly effect, call centre, chief data officer, choice architecture, cognitive dissonance, cryptocurrency, Diane Coyle,, G4S, Internet of things, Kevin Kelly, Kickstarter, loose coupling, M-Pesa, minimum viable product, nudge unit, performance metric, ransomware, Silicon Valley, social web, the market place, The Wisdom of Crowds

Before working in government, Ben was Design Director at Wieden + Kennedy and co-founder of The Newspaper Club. He is a Governor of the University of the Arts London, a member of the HS2 Design Panel and an advisor to the London Design Festival. He was inducted into the Design Week Hall of Fame in 2017. Mike Bracken was appointed Executive Director of Digital for the UK government in 2011 and the Chief Data Officer in 2014. He was responsible for overseeing and improving the government’s digital delivery of public services. After government, he sat on the board of the Co-operative Group as Chief Digital Officer. Before joining the civil service, Mike ran transformations in a variety of sectors in more than a dozen countries, including as Digital Development Director at Guardian News & Media. He was named UK Chief Digital Officer of the year in 2014 and awarded a CBE.

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,, 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

Data governance has to do with the decision processes and frameworks around data, master data management has to do with the operational processes, and data quality has to do with how to improve the integrity and consistency of the data. Data governance Just as other forms of governance, data governance has to do with policies, processes, and decisions. In data governance, we look for who has what authority to create, change, and view specific types of data. For governance to work, we need someone to be responsible and make decisions. There will typically be a Chief Data Officer or CDO at the top. There will also be a governance board with key stakeholders. First it needs to be determined at a general logical level what data exists and is of relevance to the organization. What are the key concepts that exist, like buildings, persons, payments, devices, and so on? Once this has been determined, the responsibility for each concept has to be delegated to an owner. The data owner is the one responsible for where the master source is, what the different entities are, and how they are defined.

pages: 296 words: 78,631

Hello World: Being Human in the Age of Algorithms by Hannah Fry

23andMe, 3D printing, Air France Flight 447, Airbnb, airport security, augmented reality, autonomous vehicles, Brixton riot, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, Douglas Hofstadter, Elon Musk, Firefox, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta analysis, meta-analysis, pattern recognition, Peter Thiel, RAND corporation, ransomware, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, Shai Danziger, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche

The only difference is that it will mix in the pregnancy-related coupons with other more generic items so that the customers don’t notice they’ve been targeted. An advertisement for a crib might appear opposite some wine glasses. Or a coupon for baby clothes will run alongside an ad for some cologne. Target is not alone in using these methods. Stories of what can be inferred from your data rarely hit the press, but the algorithms are out there, quietly hiding behind the corporate front lines. About a year ago, I got chatting to a chief data officer of a company that sells insurance. They had access to the full detail of people’s shopping habits via a supermarket loyalty scheme. In their analysis, they’d discovered that home cooks were less likely to claim on their home insurance, and were therefore more profitable. It’s a finding that makes good intuitive sense. There probably isn’t much cross­over between the group of people who are willing to invest time, effort and money in creating an elaborate dish from scratch and the group who would let their children play football in the house.

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

The harsh financial reality is that the better the data analytical skills, the more likely the company can produce the algorithms required to distil information from their vast data lakes. However, this is not just any information but information that returns true value, aligned to the business strategy and goals. That requires data scientists with expert business knowledge regarding the company strategy and short-medium-long term goals. This is why there is a new C-suite position called the Chief Data Officer. Commitment to Innovation A company adopting IIOT has to make a commitment to innovation, as well as taking a long-term perspective to the IIoT project’s return on investments. Funding will be required for the capital outlay for sensors, devices, machines, and systems. Funding and patience will be required as performing the data capture and configuring the analytics’ parameters and algorithms might not result in immediate results; success may take some time to realize.

Mindf*ck: Cambridge Analytica and the Plot to Break America by Christopher Wylie

4chan, affirmative action, Affordable Care Act / Obamacare, availability heuristic, Berlin Wall, Bernie Sanders, big-box store, Boris Johnson, British Empire, call centre, Chelsea Manning, chief data officer, cognitive bias, cognitive dissonance, colonial rule, computer vision, conceptual framework, cryptocurrency, Daniel Kahneman / Amos Tversky, desegregation, Dominic Cummings, Donald Trump, Downton Abbey, Edward Snowden, Elon Musk, Etonian, first-past-the-post, Google Earth, housing crisis, income inequality, indoor plumbing, information asymmetry, Internet of things, Julian Assange, Lyft, Marc Andreessen, Mark Zuckerberg, Menlo Park, move fast and break things, move fast and break things, Network effects, new economy, obamacare, Peter Thiel, Potemkin village, recommendation engine, Renaissance Technologies, Robert Mercer, Ronald Reagan, Rosa Parks, Sand Hill Road, Scientific racism, Shoshana Zuboff, side project, Silicon Valley, Skype, uber lyft, unpaid internship, Valery Gerasimov, web application, WikiLeaks, zero-sum game

He would use the name of an actual minister and throw in enough factual detail about Sri Lankan politics that Nix and the other executives would buy into the whole scenario. Channel 4 had to do a huge amount of detailed advance research, because any misstep could potentially blow the whole sting up. The carrot for Cambridge Analytica was 5 percent of the value of the man’s assets, if they succeeded in getting the (imaginary) funds released. We knew Alexander wouldn’t be able to resist. At the first two meetings, Ranjan met with chief data officer Alexander Tayler and managing director Mark Turnbull in private rooms at a hotel near Westminster. The executives pitched Cambridge Analytica’s data analysis work and suggested intelligence-gathering services, but nothing concrete came out of the meetings. They seemed cagey, hedging in how they talked about what Cambridge Analytica really did. Channel 4 was frustrated, but we had an idea for how to fix it.

pages: 385 words: 111,113

Augmented: Life in the Smart Lane by Brett King

23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, 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

He is the chairman of Witkit, Everblaze, and GINET, and director of a venture fund. He is a graduate of MIT and attended graduate school at Harvard’s Kennedy School of Government. JP Rangaswami Born in Calcutta, JP Rangaswami (@jobsworth) read economics and worked as a financial journalist before changing careers over three decades ago to enter that strange space where society, technology and banking converge. Now 58, Rangaswami works as chief data officer at a major financial institution, having previously been chief scientist and chief information officer at a number of global institutions. He is Adjunct Professor at the School of Electronics and Computer Science at the University of Southampton. In addition, he is a Fellow of the British Computer Society, a Fellow of the Royal Society of the Arts and Venture Partner at Anthemis. Rangaswami is a popular keynote speaker, having given a popular TED Talk—Information Is Food, and can be found blogging at

pages: 421 words: 110,406

Platform Revolution: How Networked Markets Are Transforming the Economy--And How to Make Them Work for You by Sangeet Paul Choudary, Marshall W. van Alstyne, Geoffrey G. Parker

3D printing, Affordable Care Act / Obamacare, Airbnb, Alvin Roth, Amazon Mechanical Turk, Amazon Web Services, Andrei Shleifer, Apple's 1984 Super Bowl advert, autonomous vehicles, barriers to entry, big data - Walmart - Pop Tarts, bitcoin, blockchain, business cycle, business process, buy low sell high, chief data officer, Chuck Templeton: OpenTable:, clean water, cloud computing, connected car, corporate governance, crowdsourcing, data acquisition, data is the new oil, digital map, discounted cash flows, disintermediation, Edward Glaeser, Elon Musk,, Erik Brynjolfsson, financial innovation, Haber-Bosch Process, High speed trading, information asymmetry, Internet of things, inventory management, invisible hand, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, Khan Academy, Kickstarter, Lean Startup, Lyft, Marc Andreessen, market design, Metcalfe’s law, multi-sided market, Network effects, new economy, payday loans, peer-to-peer lending, Peter Thiel,, pre–internet, price mechanism, recommendation engine, RFID, Richard Stallman, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Satoshi Nakamoto, self-driving car, shareholder value, sharing economy, side project, Silicon Valley, Skype, smart contracts, smart grid, Snapchat, software is eating the world, Steve Jobs, TaskRabbit, The Chicago School, the payments system, Tim Cook: Apple, transaction costs, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, winner-take-all economy, zero-sum game, Zipcar

In a provocative act designed to shed light on the issue, a young woman named Jennifer Lyn Morone has incorporated herself in order to assert an ownership interest in the data stream that she generates.35 Companies that profit from the use and sale of personal data, of course, are unlikely to find Morone’s gesture either amusing or persuasive. But the issue is not going to disappear. J. P. Rangaswami, chief data officer for Deutsche Bank, predicts: As we learn more about the value of personal and collective information, our approach to such information will mirror our natural motivations. We will learn to develop and extend these rights. The most important change will be to do with collective (sometimes, but not always, public) information. We will learn to value it more; we will appreciate the trade-offs between personal and collective information; we will allow those learnings to inform us when it comes to mores, conventions, and legislation.36 In a world where data is widely described as “the new oil,” it’s clear that the issue of data ownership will need to be resolved through some combination of regulatory action, court rulings, and industry self-regulation.37 Each new scandal involving the release of sensitive information, such as the 2014 disclosure that Sony Pictures had leaked access to the viewing history of millions of users, is likely to increase the push to establish ownership rights over user data.38 Such ownership rights would give victims a legal course of action after data breaches occur; the theory is that, given high enough liability, firms will take data security more seriously and act to prevent future leaks.39 In some niche markets, agreements over data ownership are already being developed.

pages: 482 words: 121,173

Tools and Weapons: The Promise and the Peril of the Digital Age by Brad Smith, Carol Ann Browne

Affordable Care Act / Obamacare, AI winter, airport security, Albert Einstein, augmented reality, autonomous vehicles, barriers to entry, Berlin Wall, Boeing 737 MAX, business process, call centre, Celtic Tiger, chief data officer, cloud computing, computer vision, corporate social responsibility, Donald Trump, Edward Snowden,, immigration reform, income inequality, Internet of things, invention of movable type, invention of the telephone, Jeff Bezos, Mark Zuckerberg, minimum viable product, national security letter, natural language processing, Network effects, new economy, pattern recognition, precision agriculture, race to the bottom, ransomware, Ronald Reagan, Rubik’s Cube, school vouchers, self-driving car, Shoshana Zuboff, Silicon Valley, Skype, speech recognition, Steve Ballmer, Steve Jobs, The Rise and Fall of American Growth, Tim Cook: Apple, WikiLeaks, women in the workforce

We also need to develop data-sharing approaches that will create effective opportunities for companies, communities, and countries large and small to reap the benefits from data. In short, we need to democratize AI and the data on which it relies. So how do we create a bigger opportunity for smaller players in a world where large quantities of data matter? One person who may have the answer is Matthew Trunnell. Trunnell is the chief data officer at the Fred Hutchinson Cancer Research Center, a leading cancer research center in Seattle named for a hometown hero who pitched ten seasons for the Detroit Tigers and managed three major league baseball teams. In 1961, Fred Hutchinson took the Cincinnati Reds to the World Series. Sadly, Fred’s successful baseball career and life were cut short when he died of cancer in 1964 at the age of forty-five.5 His brother, Bill Hutchinson, was a surgeon who treated Fred’s cancer.

pages: 660 words: 141,595

Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking by Foster Provost, Tom Fawcett

Albert Einstein, Amazon Mechanical Turk, big data - Walmart - Pop Tarts, bioinformatics, business process, call centre, chief data officer, Claude Shannon: information theory, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, data acquisition, David Brooks,, Erik Brynjolfsson, Gini coefficient, information retrieval, intangible asset, iterative process, Johann Wolfgang von Goethe, Louis Pasteur, Menlo Park, Nate Silver, Netflix Prize, new economy, p-value, pattern recognition, placebo effect, price discrimination, recommendation engine, Ronald Coase, selection bias, Silicon Valley, Skype, speech recognition, Steve Jobs, supply-chain management, text mining, The Signal and the Noise by Nate Silver, Thomas Bayes, transaction costs, WikiLeaks

Data Science for Business Foster Provost Tom Fawcett Beijing • Cambridge • Farnham • Köln • Sebastopol • Tokyo Praise “A must-read resource for anyone who is serious about embracing the opportunity of big data.” — Craig Vaughan Global Vice President at SAP “This timely book says out loud what has finally become apparent: in the modern world, Data is Business, and you can no longer think business without thinking data. Read this book and you will understand the Science behind thinking data.” — Ron Bekkerman Chief Data Officer at Carmel Ventures “A great book for business managers who lead or interact with data scientists, who wish to better understand the principals and algorithms available without the technical details of single-disciplinary books.” — Ronny Kohavi Partner Architect at Microsoft Online Services Division “Provost and Fawcett have distilled their mastery of both the art and science of real-world data analysis into an unrivalled introduction to the field.”