Data Platforms’ Control of User Information Creates Barriers to Entry for Potential Competitors Lacking That Data
As a handful of data platforms generate massive amounts of user data, the barriers to entry rise since potential competitors have little data themselves to entice advertisers compared to the incumbents who have both the concentrated processing power and supply of user data to dominate particular sectors.
While control of user data has not been central to traditional debates on competition and antitrust, regulators are increasingly noting the threat of such control to competition. In January 2013, after the FTC dismissed one antitrust approach to Google, Federal Trade Commissioner J. Thomas Rosch criticized his colleagues for not even investigating Google’s collection of user data in its antitrust proceedings, since Google’s “gathering of information about the characteristics of a consumer [may be] done. . . to maintain a monopoly or near-monopoly position.”[i] In Europe Joaquín Almunia, Vice President of the European Commission responsible for Competition Policy, argued in a recent speech that “exclusive access to personal data in a given market could give rise to concentration concerns.”[ii] Germany’s economy minister Sigmar Gabriel has referred to the big data platforms as engaged in “brutal information capitalism” that may be threatening competition. [iii]
The network of users built by big data platforms produce its own network effects as the data produced grows exponentially with the users searching, linking, commenting and producing shared content. Apple made sure that each iPhone sold added to its growing data trove about use of those phones by its users, Facebook and Twitter meticulously tracks sharing across its network, and Google learns more about what users want to find as they track each search and click across their network.
Controlling More Data Increasingly Trumps Other Forms of Innovation
Increasingly, analyzing the maximum quantity of data and finding useful correlations within that data is trumping other forms of insight and innovation, making it even harder for any startup with the proverbial better idea to win out against the pure quantities of data held by incumbents.
One often-cited example of how brute force data analysis beats other forms of insight is the evolution of computer chess-playing computers. Decades ago the assumption was that computers needed to in some way develop a deep and complex analysis of the game to beat human players. Instead, programmers found it was more effective just to feed in massive amounts of information about recorded chess games and let the computer search for past game positions that match whatever game it’s currently playing, then make the move that worked in the past. Similarly, translation software has largely abandoned algorithms that try to understand how language works in favor of feeding in massive amounts of previously translated material (the United Nations is a favorite source for this) where the software just finds a phrase fragment from past translations to help translate a current document, the method for example used by Google Translate to become so successful.[iv]
People don’t always think of Wal-Mart as a big data company, but it pioneered massive data analysis in the retail sector, using massive analysis of sales data to better coordinate logistics and restocking planning as a competitive advantage. When Amazon launched, it could borrow many of Wal-Mart’s tools (and some of its programmers) to strengthen its own logistics; it then used what users recommended to compare shoppers to other similar shoppers and recommend new purchases. Amazon found human editors recommending products could not begin to match the brute force data analysis and the result was that today, a third of all Amazon sales are from its data-driven recommendation system—and competitors without similar volumes of consumer data have been driven out of business across the country.[v]
For Google, its network delivers not just data on each individual user, but also the cumulative data on how similar users behave. This allows Google to anticipate a user’s interests (and the returns on advertising) based not just on each user’s own previous actions, but on the behavior of similar users in its network. While Google no doubt got a leg-up into its dominant position through innovation in its core algorithm, even its own leaders have admitted that it has consolidated its control based on overwhelming control of data. As its Chief Scientist Peter Norvig has observed, "We don't have better algorithms than everyone else; we just have more data."[vi]
The triumph of raw data analysis means “more trumps less. And sometimes more trumps smarter,” argue Cukier and Mayer-Schönberger in their Big Data: A Revolution.[vii] Building a better mousetrap loses out in the market to companies with the most data.
Mid-size companies don’t benefit much from big data analysis since they usually lack the critical mass of data and can’t invest in the computing power to gain the insights driving market power by the bigger players. Fighting credit card fraud is one example of this; big data analysis tracking likely fraud based on the purchasing patterns of thieves gave larger financial institutions a big advantage in catching such fraud. The result was smaller banks avoided issuing their own credit cards, passing the business onto firms like Capitol One and Bank of America which metasized dominance of the field. This in turn further strengthened the big bank’s data about consumer finances and their overall market power and dominance in the financial sector.[viii]
Control of Unique Data Strengthens Control by Big Data Platforms
As companies collect user data, they gain competitive advantage against any potential challenger who will lack access to that unique user data in setting up any rival service. Such data can be redeployed by dominant players not just to strengthen their position in existing services but used in related new services to expand their economic reach. In this way, you see Google, Amazon, Apple and Facebook expanding rapidly into a multiplicity of emerging data-related fields, making it extremely hard for upstart companies to get a toehold except in very specific niches.[ix]
There has recently been a flurry of political interest in abusive practices by data brokers who buy and sell personal data, with major reports released by both the U.S. Senate[x] and the Federal Trade Commission.[xi] While the consumer harm detailed in those reports are important, it is worth noting that the companies involved are relative minnows in the big data ecosystem compared to the major big data platforms —and are likely to be even more marginal over time. Experian is one of the largest at $4.8 billion in sales per year[xii] while Acxiom, a data broker often cited as having one of the largest datasets on consumers, has only about $1 billion per year in revenue.[xiii] Even collectively, these data brokers are dwarfed by a company like Google with over $60 billion in annual revenue.
This is largely due to the fact that most data brokers do not control unique information about individual consumers but instead are merely middlemen. While smart entrepreneurs running such firms were able to position themselves to benefit as corporate advertisers began engaging in targeted advertising, power in the data-driven economy is going to inevitably move to data platforms like Google, Facebook and other companies that continually generate new unique data on consumers. As Kenneth Cukier and Viktor Mayer-Schönberger argued in their recent book, The Rise of Big Data: A Revolution That Will Transform How We Live, Work, and Think, the initial skills of data brokers is inevitably losing out to companies “holding large pools of data and being able to capture ever more of it with ease…large data holders will flourish as they gather and store more of the raw material of their business, which they can reuse to create additional value.”[xiv]
The key importance of and market power by data platforms controlling unique data is reflected in recent acquisitions. Facebook’s buyout of the photo-sharing company Instagram and of the global texting company WhatsApp were based on gaining control of the massive user base and unique data generated by those users.[xv] Google has largely focused on growing its own data sources but it’s recent large percentage investment in the taxi service Uber reflects its interest in having a stake in the transit and logistics data being generated by users of that company.[xvi] And the potential scope of Google’s data ambition is reflected in its acquisition of Skybox Imaging, which puts into orbit low-flying satellites which photograph the whole planet twice a day with the accuracy to count cars in stores’ parking lots and predict those company’s next quarter sales figures, or to survey the health of cropland across earth and estimate the likely price of grain months in advance.[xvii] In each case, the big data platforms gain access to unique data that leaves potential competitors with even less openings to challenge their market dominance.
[i] Google Inc., FTC File No. 111-0163, at 1 n.1 (Jan. 3, 2012) (Rosch, Comm’r, concurring and dissenting), available at http://www.ftc.gov/sites/default/files/documents/public_statements/concurring-and-dissenting-statement-commissioner-j.thomas-rosch-regarding-googles-search-practices/130103googlesearchstmt.pdf
[ii] Address at Privacy Platform Event: Competition and Personal Data Protection (Nov. 26, 2012), available at europa.eu/rapid/press-release_SPEECH-12-860_en.doc
[iii] Juliette Garside, “From Google to Amazon: EU goes to war against power of US digital giants,” The Observer, July 5, 2014, http://www.theguardian.com/technology/2014/jul/06/google-amazon-europe-g...
[iv] Cukier and Mayer-Schönberger, p. 36.
[v] Ibid., p. 52.
[vi] Matt Asay, “Tim O'Reilly: 'Whole Web' is the OS of the future,” CNET (Mar. 18, 2010), http://cnet.co/ckl9ov. Similarly, former Google CEO Eric Schmidt has said, "Scale is the key. We just have so much scale in terms of the data we can bring to bear." “How Google Plans to Stay Ahead in Search,” Bloomberg Businessweek (Oct. 02, 2009), http://buswk.co/1arA6c.
[vii] Cukier and Mayer-Schönberger, p. 36.
[viii] Cukier and Mayer-Schönberger, p. 127
[ix] For more, see Nathan Newman, “Search, Antitrust and the Economics of the Control of User Data” to be published in the Yale Law School Journal on Regulation (2014); http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2309547
[x] U.S. Senate Committee on Commerce, Science and Transportation, A Review of the Data Broker Industry: Collection, Use, and Sale of Consumer Data for Marketing Purposes, December 13, 2013; http://op.bna.com/der.nsf/id/sbay-9ehtxt/$File/Rockefeller%20report%20on%20data%20brokers.pdf
[xi] Data Brokers: A Call For Transparency and Accountability: A Report of the Federal Trade Commission , Federal Trade Commission (May 2014); http://www.ftc.gov/reports/data-brokers-call-transparency-accountability...
[xiv] Kenneth Cukier and Viktor Mayer-Schönberger. Big Data: A Revolution That Will Transform How We Live, Work, and Think (2013), Chapter 7, subsection “A question of utility.”
[xv] “Facebook Buys WhatsApp for $19 Billion,” Feb. 19, 2014, BusinessWeek,
[xvi] “What Uber Will Do With All That Money From Google,” Wired, Jan. 14, 2014,
[xvii] “Amid Stratospheric Valuations, Google Unearths a Deal With Skybox,” WSJ, June 15, 2014,, http://online.wsj.com/articles/amid-stratospheric-valuations-google-unearths-a-deal-with-skybox-1402864823
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