data is the new oil

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pages: 502 words: 107,657

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, call centre, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil,, Erik Brynjolfsson, experimental subject, Google Glasses, happiness index / gross national happiness, job satisfaction, Johann Wolfgang von Goethe, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, 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, 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, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra

The idea is simple, although that doesn’t make it easy. The challenge is tackled by a systematic, scientific means to develop and continually improve prediction—to literally learn to predict. The solution is machine learning—computers automatically developing new knowledge and capabilities by furiously feeding on modern society’s greatest and most potent unnatural resource: data. “Feed Me!”—Food for Thought for the Machine Data is the new oil. —European Consumer Commissioner Meglena Kuneva The only source of knowledge is experience. —Albert Einstein In God we trust. All others must bring data. —William Edwards Deming (a business professor famous for work in manufacturing) Most people couldn’t be less interested in data. It can seem like such dry, boring stuff. It’s a vast, endless regiment of recorded facts and figures, each alone as mundane as the most banal tweet, “I just bought some new sneakers!”

This is the assumption behind the leap of faith an organization takes when undertaking PA. Budgeting the staff and tools for a PA project requires this leap, knowing not what specifically will be discovered and yet trusting that something will be. Sitting on an expert panel at Predictive Analytics World, leading UK consultant Tom Khabaza put it this way: “Projects never fail due to lack of patterns.” With The Data Effect in mind, the scientist rests easy. Data is the new oil. It’s this century’s greatest possession and often considered an organization’s most important strategic asset. Several thought leaders have dubbed it as such—“the new oil”—including European Consumer Commissioner Meglena Kuneva, who also calls it “the new currency of the digital world.” It’s not a hyperbole. In 2012, Apple Inc. overtook Exxon Mobil Corporation, the world’s largest oil company, as the most valuable publicly traded company in the world.

Olofson, Susan Feldman, Steve Conway, Matthew Eastwood, and Natalya Yezhkova, “Worldwide Big Data Technology and Services 2012–2012 Forecast,” ICD Analyze the Future, March 2012, Doc #233485. The Prediction Effect: Tom Khabaza says, “There are always patterns.” Tom Khabaza, “Nine Laws of Data Mining—Part 2,” Data Mining & Predictive Analytics, edited by Tom Khabaza, January 14, 2012. “Personal data is the new oil of the Internet and the new currency of the digital world”: Meglena Kuneva, European Consumer Commissioner, March 2009, “Personal Data: The Emergence of a New Asset Class,” An Initiative of the World Economic Forum, January 2011. Table: Bizarre and Surprising Insights—Consumer Behavior (Chapter 3) Guys literally drool over sports cars: James Warren, “Just the Thought of New Revenue Makes Mouths Water,” New York Times, September 29, 2011.


pages: 713 words: 93,944

Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement by Eric Redmond, Jim Wilson, Jim R. Wilson


Amazon Web Services, create, read, update, delete, data is the new oil, database schema, Debian, domain-specific language,, fault tolerance, full text search, general-purpose programming language, linked data, MVC pattern, natural language processing, node package manager, random walk, recommendation engine, Skype, social graph, web application

From Jim: First, I have to thank my family; Ruthy, your boundless patience and encouragement have been heartwarming. Emma and Jimmy, you’re two smart cookies, and your daddy loves you always. Also a special thanks to all the unsung heroes who monitor IRC, message boards, mailing lists, and bug systems ready to help anyone who needs you. Your dedication to open source keeps these projects kicking. Copyright © 2012, The Pragmatic Bookshelf. Preface It has been said that data is the new oil. If this is so, then databases are the fields, the refineries, the drills, and the pumps. Data is stored in databases, and if you’re interested in tapping into it, then coming to grips with the modern equipment is a great start. Databases are tools; they are the means to an end. Each database has its own story and its own way of looking at the world. The more you understand them, the better you will be at harnessing the latent power in the ever-growing corpus of data at your disposal.

Not-So-Good For: Because of the high degree of interconnectedness between nodes, graph databases are generally not suitable for network partitioning. Spidering the graph quickly means you can’t afford network hops to other database nodes, so graph databases don’t scale out well. It’s likely that if you use a graph database, it’ll be one piece of a larger system, with the bulk of the data stored elsewhere and only the relationships maintained in the graph. 9.2 Making a Choice As we said at the beginning, data is the new oil. We sit upon a vast ocean of data, yet until it’s refined into information, it’s unusable (and with a more crude comparison, there’s a lot of money in data these days). The ease of collecting and ultimately storing, mining, and refining the data out there starts with the database you choose. Deciding which database to choose is often more complex than merely considering which genre maps best to a given domain’s data.


pages: 239 words: 70,206

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


23andMe, Affordable Care Act / Obamacare, Albert Einstein, big data - Walmart - Pop Tarts, bioinformatics, 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, John von Neumann, Mark Zuckerberg, market bubble, meta analysis, meta-analysis, 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!

“Without the technology to analyze the data, it’s useless,” Zhou notes. “Now, it’s getting to be valuable.” In September 2014, Zhou left IBM to start her own company. The idea, she says, is inspired by the work she did at IBM, and researchers there will continue to pursue the underlying technologies she developed in service of corporations. But Zhou has her eye on the consumer market. If data is the new oil, she says, then we are all data wells, and potentially valuable ones. The data-infused profiles of a person’s traits and values, Zhou says, should be exploited by the individual as a kind of currency in exchange for truly personalized products, services, and advice from businesses, with tailored pricing as well. Even a prototype was months away when we spoke just after she departed from IBM, but her ambition is to help alter the terms of trade in digital commerce.


pages: 677 words: 206,548

Future Crimes: Everything Is Connected, Everyone Is Vulnerable and What We Can Do About It by Marc Goodman


23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Bill Joy: nanobots, bitcoin, Black Swan, blockchain, borderless world, Brian Krebs, business process, butterfly effect, call centre, Chelsea Manning, cloud computing, cognitive dissonance, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, data acquisition, data is the new oil, Dean Kamen, disintermediation, don't be evil, double helix, Downton Abbey, Edward Snowden, Elon Musk, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, future of work, game design, Google Chrome, Google Earth, Google Glasses, Gordon Gekko, high net worth, High speed trading, hive mind, Howard Rheingold, hypertext link, illegal immigration, impulse control, industrial robot, Internet of things, Jaron Lanier, Jeff Bezos, job automation, John Harrison: Longitude, Jony Ive, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, Kuwabatake Sanjuro: assassination market, Law of Accelerating Returns, Lean Startup, license plate recognition, litecoin, M-Pesa, Mark Zuckerberg, Marshall McLuhan, Menlo Park, mobile money, more computing power than Apollo, move fast and break things, Nate Silver, national security letter, natural language processing, obamacare, Occupy movement, Oculus Rift, offshore financial centre, optical character recognition, pattern recognition, personalized medicine, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, RAND corporation, ransomware, Ray Kurzweil, refrigerator car, RFID, ride hailing / ride sharing, Rodney Brooks, Satoshi Nakamoto, Second Machine Age, security theater, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart meter, Snapchat, social graph, software as a service, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, supply-chain management, technological singularity, telepresence, telepresence robot, Tesla Model S, The Wisdom of Crowds, Tim Cook: Apple, trade route, uranium enrichment, Wall-E, Watson beat the top human players on Jeopardy!, Wave and Pay, We are Anonymous. We are Legion, web application, WikiLeaks, Y Combinator, zero day

CHAPTER 3: MOORE’S OUTLAWS The World of Exponentials The Crime Singularity Control the Code, Control the World CHAPTER 4: YOU’RE NOT THE CUSTOMER, YOU’RE THE PRODUCT Our Growing Digital World—What They Never Told You The Social Network and Its Inventory—You You’re Leaking—How They Do It The Most Expensive Things in Life Are Free Terms and Conditions Apply (Against You) Mobile Me Pilfering Your Data? There’s an App for That Location, Location, Location CHAPTER 5: THE SURVEILLANCE ECONOMY You Thought Hackers Were Bad? Meet the Data Brokers Analyzing You But I’ve Got Nothing to Hide Privacy Risks and Other Unpleasant Surprises Opening Pandora’s Virtual Box Knowledge Is Power, Code Is King, and Orwell Was Right CHAPTER 6: BIG DATA, BIG RISK Data Is the New Oil Bad Stewards, Good Victims, or Both? Data Brokers Are Poor Stewards of Your Data Too Social Networking Ills Illicit Data: The Lifeblood of Identity Theft Stalkers, Bullies, and Exes—Oh My! Online Threats to Minors Haters Gonna Hate Burglary 2.0 Targeted Scams and Targeted Killings Counterintelligence Implications of Leaked Government Data So No Online Profile Is Better, Right? The Spy Who Liked Me CHAPTER 7: I.T.

LeT simply processed the data the public was leaking and leveraged them in real time to kill more people and outmaneuver authorities. That was terrorism in the digital age circa 2008. What might terrorists do with the technologies available today? What will they do with the technologies of tomorrow? The lesson of Mumbai is that exponential change applies not just for good but for evil as well. Data Is the New Oil Data is constantly being generated by everything around us. Every digital process, sensor, mobile phone, GPS device, car engine, medical lab test, credit card transaction, hotel door lock, report card, and social media exchange produces data. Smart phones are turning human beings into human sensors, generating vast sums of information about us. As a result, children born today will live their entire lives in the shadow of a massive digital footprint, with some 92 percent of infants already having an online presence.


pages: 348 words: 39,850

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 Contents Foreword by Peter Norvig, Google����������������������������������������������������������������� vii About the Author������������������������������������������������������������������������������������������xi Acknowledgments���������������������������������������������������������������������������������������� xiii Introduction���������������������������������������������������������������������������������������������������xv Chapter 1: Chris Wiggins, The New York Times�������������������������������������������������� 1 Chapter 2: Caitlin Smallwood, Netflix ���������������������������������������������������������19 Chapter 3: Yann LeCun, Facebook�����������������������������������������������������������������45 Chapter 4: Erin Shellman, Nordstrom �����������������������������������������������������������67 Chapter 5: Daniel Tunkelang, LinkedIn ���������������������������������������������������������83 Chapter 6: John Foreman, MailChimp ���������������������������������������������������������107 Chapter 7: Roger Ehrenberg, IA Ventures�����������������������������������������������������131 Chapter 8: Claudia Perlich, Dstillery�����������������������������������������������������������151 Chapter 9: Jonathan Lenaghan, PlaceIQ�����������������������������������������������������179 Chapter 10: Anna Smith, Rent the Runway �����������������������������������������������������199 Chapter 11: André Karpištšenko, Planet OS �������������������������������������������������221 Chapter 12: Amy Heineike, Quid�����������������������������������������������������������������239 Chapter 13: Victor Hu, Next Big Sound�����������������������������������������������������������259 Chapter 14: Kira Radinsky, SalesPredict ���������������������������������������������������������273 Chapter 15: Eric Jonas, Neuroscience Research �������������������������������������������������293 Chapter 16: Jake Porway, DataKind ���������������������������������������������������������������319 Index�������������������������������������������������������������������������������������������������������������335 Introduction Data is the new oil! —Clive Humby, dunnhumby1 By 2018, the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge. —McKinsey Report2 The emergence of data science is gathering ever more attention, and it’s no secret that the term data science itself is loaded with controversy about what it means and whether it’s actually a field.

In the interests of full disclosure, I fall into the camp of those who believe that data science is truly an emerging academic discipline and that data scientists as such have proper roles in organizations. Moreover, I believe that each of the subjects I interviewed for this book is indeed a data scientist—and, after having spent time with all of them, I couldn’t be more excited about the future of data science. Michael Palmer, “Data Is the New Oil,” ANA Marketing Maestros blog, November 3, 2006. 2 Susan Lund et al., “Game Changers: Five Opportunities for US Growth and Renewal,” McKinsey Global Institute Report, July 2013. americas/us_game_changers. 1 xvi Introduction Though some of them are wary of the hype that the field is attracting, all sixteen of these data scientists believe in the power of the work they are doing as well as the methods.


pages: 422 words: 104,457

Dragnet Nation: A Quest for Privacy, Security, and Freedom in a World of Relentless Surveillance by Julia Angwin


AltaVista, Ayatollah Khomeini, barriers to entry, bitcoin, Chelsea Manning, clean water, crowdsourcing, cuban missile crisis, data is the new oil, David Graeber, Debian, Edward Snowden, Filter Bubble, Firefox, GnuPG, Google Chrome, Google Glasses, informal economy, Jacob Appelbaum, Julian Assange, market bubble, market design, medical residency, meta analysis, meta-analysis, mutually assured destruction, prediction markets, price discrimination, randomized controlled trial, RFID, Robert Shiller, Ronald Reagan, security theater, Silicon Valley, Silicon Valley startup, Skype, smart meter, Steven Levy, Upton Sinclair, WikiLeaks, Y2K, Zimmermann PGP

Tracking is so crucial to the industry that in 2013 Randall Rothenberg, the president of the Interactive Advertising Bureau, said that if the industry lost its ability to track people, “billions of dollars in Internet advertising and hundreds of thousands of jobs dependent on it would disappear.” Meglena Kuneva, a member of the European Commission, summed it up best in 2009 when she said: “Personal data is the new oil of the Internet and the new currency of the digital world.” * * * If you were to build a taxonomy of trackers it would look something like this: GOVERNMENT • Incidental collectors. Agencies that collect data in their normal course of business, such as state motor vehicle registries and the IRS, but are not directly in the data business. • Investigators. Agencies that collect data about suspects as part of law enforcement investigations, such as the FBI and local police


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, 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,, 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,, 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

As of 2011, there were more than three thousand games on Facebook, collectively weakening Zynga’s individual bargaining power.20 The startup’s response may be to sell, to fight back through multihoming, or to expand into other business arenas. Zynga, for example, now multihomes on Tencent’s QQ social network and on the Apple and Google mobile platforms, as well as offering its own cloud service. HOW PLATFORMS COMPETE (3): LEVERAGING THE VALUE OF DATA One of the clichés of the Internet economy is the saying “Data is the new oil”—and like most clichés, it contains a lot of truth. Data can be a source of enormous value to platform businesses, and well-run firms are using data to shore up their competitive positions in a wide variety of ways. Platform businesses can use data to improve their competitive performance in two general ways—tactically and strategically. An example of tactical data use is in the performance of A/B testing, to optimize particular tools or features of the platform.


pages: 396 words: 117,149

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos


3D printing, Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, crowdsourcing, Danny Hillis, data is the new oil, double helix, Douglas Hofstadter, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, global village, Google Glasses, Gödel, Escher, Bach, information retrieval, job automation, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, lone genius, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, NP-complete, P = NP, PageRank, pattern recognition, phenotype, planetary scale, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman, Second Machine Age, self-driving car, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight

The same dynamic happens in any market where there’s lots of choice and lots of data. The race is on, and whoever learns fastest wins. It doesn’t stop with understanding customers better: companies can apply machine learning to every aspect of their operations, provided data is available, and data is pouring in from computers, communication devices, and ever-cheaper and more ubiquitous sensors. “Data is the new oil” is a popular refrain, and as with oil, refining it is big business. IBM, as well plugged into the corporate world as anyone, has organized its growth strategy around providing analytics to companies. Businesses look at data as a strategic asset: What data do I have that my competitors don’t? How can I take advantage of it? What data do my competitors have that I don’t? In the same way that a bank without databases can’t compete with a bank that has them, a company without machine learning can’t keep up with one that uses it.


pages: 497 words: 144,283

Connectography: Mapping the Future of Global Civilization by Parag Khanna


1919 Motor Transport Corps convoy, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, 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, complexity theory, 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, diversification, Doha Development Round, edge city, Edward Snowden, Elon Musk, energy security, ethereum blockchain, European colonialism, eurozone crisis, failed state, Fall of the Berlin Wall, family office, Ferguson, Missouri, financial innovation, financial repression, 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 robot, informal economy, Infrastructure as a Service, interest rate swap, Internet of things, Isaac Newton, Jane Jacobs, Jaron Lanier, John von Neumann, Julian Assange, Just-in-time delivery, Kevin Kelly, Khyber Pass, Kibera, Kickstarter, labour market flexibility, labour mobility, LNG terminal, low cost carrier, manufacturing employment, mass affluent, megacity, Mercator projection, microcredit, mittelstand, Monroe Doctrine, mutually assured destruction, New Economic Geography, new economy, New Urbanism, offshore financial centre, oil rush, oil shale / tar sands, oil shock, openstreetmap, out of africa, Panamax, Peace of Westphalia, peak oil, Peter Thiel, 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, Tim Cook: Apple, trade route, transaction costs, UNCLOS, uranium enrichment, urban planning, urban sprawl, WikiLeaks, young professional, zero day

Whether these governments seek to monitor, filter, or protect digital flows, the geographic (and legal) location of servers, cables, routers, and data centers now matters as much as the geography of oil pipelines. The differences are crucial, however. Internet data can be replicated infinitely and exist in multiple places at the same time. Additionally, it can be rerouted or smuggled “in” to its destination, while the receiver has the ability to come “out” as well to access it. If data is the new oil, it is certainly much more slippery. It is true that the Internet is no longer a truly borderless, parallel universe. Even Twitter, the world’s most free and unfiltered medium of one-to-many expression, preemptively restricts content banned in various countries, while Google Maps loads tailored maps approved by national authorities based on the user’s server location. Yet even if software or data services have to be customized to national restrictions such as after the EU’s 2015 decision invalidating the “Safe Harbor” agreement with the United States, these represent only partial frictions, not blockages.