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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, bioinformatics, bitcoin, Black Swan, blockchain, Burning Man, business intelligence, business process, call centre, chief data officer, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, Dean Kamen, dematerialisation, discounted cash flows, distributed ledger, Edward Snowden, Elon Musk, en.wikipedia.org, ethereum blockchain, Galaxy Zoo, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, Hyperloop, industrial robot, Innovator's Dilemma, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loose coupling, loss aversion, Lyft, Mark Zuckerberg, market design, means of production, minimum viable product, natural language processing, Netflix Prize, Network effects, new economy, Oculus Rift, offshore financial centre, p-value, PageRank, pattern recognition, Paul Graham, 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, Tyler Cowen: Great Stagnation, urban planning, WikiLeaks, winner-take-all economy, X Prize, Y Combinator
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 Forbes.com, 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.
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, data acquisition, Edward Snowden, Erik Brynjolfsson, intermodal, Internet of things, Jeff Bezos, knowledge worker, Mark Zuckerberg, move fast and break things, Narrative Science, natural language processing, Netflix Prize, New Journalism, recommendation engine, RFID, self-driving car, sentiment analysis, Silicon Valley, smart grid, smart meter, social graph, sorting algorithm, statistical model, Tesla Model S, text mining
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.
Bad Data Handbook by Q. Ethan McCallum
Amazon Mechanical Turk, asset allocation, barriers to entry, Benoit Mandelbrot, business intelligence, cellular automata, chief data officer, cloud computing, cognitive dissonance, combinatorial explosion, conceptual framework, database schema, 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, sentiment analysis, statistical model, supply-chain management, 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.
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, 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, Jacquard loom, Jane Jacobs, jitney, John Snow's cholera map, Khan Academy, Kibera, knowledge worker, load shedding, M-Pesa, Mark Zuckerberg, megacity, mobile money, mutually assured destruction, new economy, New Urbanism, Norbert Wiener, Occupy movement, openstreetmap, packet switching, patent troll, 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 Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, 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, 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.
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, 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 skilled workers, millennium bug, pattern recognition, 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, web application, WebRTC, WebSocket, 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.
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, 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 process, buy low sell high, chief data officer, clean water, cloud computing, connected car, corporate governance, crowdsourcing, data acquisition, data is the new oil, discounted cash flows, disintermediation, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, financial innovation, Haber-Bosch Process, High speed trading, Internet of things, inventory management, invisible hand, Jean Tirole, Jeff Bezos, jimmy wales, Khan Academy, Kickstarter, Lean Startup, Lyft, market design, multi-sided market, Network effects, new economy, payday loans, peer-to-peer lending, Peter Thiel, pets.com, pre–internet, price mechanism, recommendation engine, RFID, Richard Stallman, ride hailing / ride sharing, 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, two-sided market, Uber and Lyft, Uber for X, winner-take-all economy, 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.
Data Scientists at Work by Sebastian Gutierrez
Albert Einstein, algorithmic trading, bioinformatics, bitcoin, business intelligence, chief data officer, clean water, cloud computing, computer vision, continuous integration, correlation does not imply causation, crowdsourcing, data is the new oil, DevOps, domain-specific language, follow your passion, full text search, informal economy, information retrieval, Infrastructure as a Service, inventory management, iterative process, linked data, Mark Zuckerberg, microbiome, Moneyball by Michael Lewis explains big data, move fast and break things, natural language processing, Network effects, nuclear winter, optical character recognition, pattern recognition, Paul Graham, personalized medicine, Peter Thiel, pre–internet, quantitative hedge fund, quantitative trading / quantitative ﬁnance, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman, self-driving car, side project, Silicon Valley, Skype, software as a service, speech recognition, statistical model, Steve Jobs, stochastic process, technology bubble, text mining, the scientific method, web application
You as a data scientist will need to learn to understand the business you are in much better, as well as learn to interact with other domains and with people from other disciplines. I think it is Chevron who has executives paired with data scientists so that when there is any big investment decision to be made, they work together to select the best course of action. Similarly, these days, you are starting to see chief analytical officers next to chief data officers. The chief analytical officer represents the change that any major decision throughout the organization is now getting made by everyone in the organization, with data scientists and machine learning methods being the means and tools to make those better decisions. You are now seeing data scientists being planted into product teams, instead of being an isolated island or a group of experts throwing wise words at other groups.