This piece analyzes data-driven mergers in the context of American anti-trust law.
Competition authorities across the globe are actively discussing the regulation of big data and its role in merger control. The EU- aggressively pursuing big-tech regulation through Commissioner of Competition Magrethe Vestager- opened consultations for regulating digital platforms in June 2020. Across the Atlantic, earlier this year, the Federal Trade Commission (FTC) issued a probe of all acquisitions undertaken by Google, Amazon, Facebook, Apple (GAFA) and Microsoft between 1 January 2010 and 31 December 2019. The inquiry focused on a key question: “how acquired assets were integrated and how acquired data has been treated.” The FTC also held held hearings on issues of competition and consumer protection, including the role of big data in competition. The seminal US Senate hearing and testimony of GAFA also considered the question of ‘big tech’ mergers. 
This article focuses on big data as a relevant consideration in merger control analysis in the US. It also discusses the policy takeaways from the US experience of data driven mergers.
What is big data?
While there is no consensus on a definition of big data, there is some common understanding regarding its key attributes viz. volume, velocity, variety (value and veracity being the additional attributes). Data is also characterized by economies of scale (i.e. cost of additional use of data / algorithm is very low), economies of scope (i.e. it is beneficial to analyze aggregated datasets), and non-rivalry (i.e. data can be used simultaneously by several users without diminishing its utility, and sometimes enhancing it). Big data finds many applications including in product development (by using predictive analytics, analyzing consumer data), predictive maintenance (extrapolating potential issues in hardware and services), enhancing customer experience (making data informed customized offers, proactive handling of customer complaints, etc.), fraud and compliance (identification of patterns and sifting through large data sets), machine learning, enhancing operational efficiencies, driving innovation, etc. Often, large data sets are analysed through complex algorithms and machine learning tools to eke patterns and insights from the data. Further, there may be several ways to collect certain data given the multiplicity of its points of generations. Let us now turn to treatment of big data in merger control in the US.
Big data & merger control in the US
It appears that the US has approached big data and merger control issues on a case by case basis. In several instances, the FTC has ordered divestitures of datasets and sharing of data with third parties in order to assuage competition concerns. In others it permitted the merger after appraising the possible theories of harms arising in each transaction. In these cases, the FTC found that the merger actually increased competition in the market. For instance, while deliberating over Microsoft’s purchase of search engine Yahoo!, the FTC concluded that the acquisition of datasets by Microsoft would lead to rapid innovation in search- related products, changes in search results presentation, user interface, and search algorithms. This would not hurt competition, but promote it.
Takeaways from the American experience
Policy takeaways for other jurisdictions in relation to data related or data driven mergers are:
Privacy concerns: It has been argued that mergers involving data need to factor in privacy concerns and deferring to data privacy laws is flawed. However, privacy law and data protection issues do not lie within the remit of antitrust law. Overall, a separation of jurisdiction and powers between both privacy and anti-trust frameworks would lead to better governance. If consumers deem privacy terms or data protection measures as a key feature of the product or service been offered, then there may be reason for competition authorities to analyse them while discerning the relevant product market. The US Horizontal Merger Guidelines (as well the European Union Merger Guidelines), and the decisional practice, are broad enough to encompass non-price effects in the analysis (such as privacy concerns) if the need arises.
Case by case approach: Several theories of harm have been proposed in relation to data mergers such as consumer harm due to horizontal effects, consumer harm due to vertical integration, and foreclosure of essential input, among others. However, a case by case approach that assesses the likelihood of harm while considering potential pro-competitive effects is best suited for dynamic data driven mergers. In contrast, a prescriptive set of rules devised to address data mergers may not be needed, even as special methods or expertise to analyze relevant markets may be required.
Lowering merger notification threshold: Several suggestions have been made in the EUand the US  to re-look at monetary/asset thresholds for merger notifications. This is because the acquisition of smaller firms by bigger companies may escape merger scrutiny in data driven mergers, on account of low turnover / asset base of the target company. Germany’s Federal Cartel Office and the Italian Competition Authority have already amended rules to this effect. The FTC also takes cognizance of mergers that can harm competition despite not meeting merger notification thresholds. Whether a competition authority has the power to review a consummated merger or examine mergers that fall below the notification threshold will vary from jurisdiction to jurisdiction. Where the legislative framework is wide enough, the competition authorities may be able to address the so called ‘killer acquisitions’ without changing notification thresholds or undertaking legislative crafting. These cases, especially post-merger reviews, should be handled carefully having due regards to incentives of entrepreneurs to innovate, pro-competitive effects of the merger and the need for a predictable merger control regime for businesses to thrive.
This piece has been authored by Gargi Yadav, external consultant, with inputs from Nehaa Chaudhari, Partner, Ikigai Law.
For more on the topic, please feel free to reach out to us at [email protected]
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“..it is important to remember that acquisitions that improve competitiveness are not anticompetitive. When a firm acquires an asset that enables it to provide better service and by doing so, becomes more attractive to customers—rendering its rivals’ jobs that much more difficult—that’s not normally an antitrust problem…” , D. Bruce Hoffman, Competition Policy and the Tech Industry – What’s at Stake?, Federal Trade Commission, 12th April 2018, available at https://www.ftc.gov/system/files/documents/public_statements/1375444/ccia_speech_final_april30.pdf, last accessed on 5th November 2020.
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 “..the dismissal of privacy considerations on grounds based largely on the classical divide between competition and data protection is unhelpful in instrumenting the case against behavioural price discrimination, which is something that privacy cannot deal with..the current assessment of mergers has to activate the public policy clause and to consider the economic implications of privacy following a merger. No merger should be unconditionally cleared if it involves a large amount of users’ data”, Anca D Chirita, Data-Driven Mergers under EU Competition Law, Durham University Law School 1, page 31, (2018).
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