“Big data” has become one of the hottest subjects for antitrust enforcers around the globe. There is concern that large tech companies are amassing vast amounts of data and will use that data to entrench their dominant positions. With this emerging view, more data may be viewed as anticompetitive.
Yet, recent transactions such as CVS-Aetna (in which Dechert represented CVS) and AT&T-Time Warner show how the use of data can be procompetitive, reducing costs and benefitting consumers in the process. Under the right circumstances, big data can drive substantial merger efficiencies, a key showing in obtaining merger clearance.
What Is Big Data?
As a threshold matter, what is big data? There is no consensus definition, but big data is often described as having four key characteristics (the “four Vs”): Volume (i.e. size and scale), Velocity (i.e. rate at which it is generated), Variety (i.e. different types and forms), and Value.1 Big data may be comprised of information that is provided by consumers or generated from such consumer information. It may be used to identify consumer preferences or to predict consumer behavior.
Big data, when combined with evolving data analytics, such as artificial intelligence and machine learning, can lead to substantial and wide-ranging benefits for consumers. Yet, certain uses of big data have raised concerns implicating consumer privacy, state surveillance, and other potentially undesirable economic and societal outcomes.
Antitrust and Big Data
In the antitrust sphere, enforcers and policymakers are grappling with whether big data raises new competitive issues and whether existing antitrust laws are able to address these potential issues. For example, big data is sometimes described as a barrier to entry for firms lacking comparable datasets. Another competitive concern is that a dominant firm with access to more and better data about customers or suppliers than its rivals will use that data to maintain or extend its market power by excluding potential competitors.
The head of the Department of Justice Antitrust Division (DOJ) recently commented on the role of data in the agency’s ongoing investigation of large tech companies: “We are examining the role of data. There’s no question that is part of our review. . . . In and of itself it’s not anticompetitive, but just like any other asset, data can give you market power and its abuse would be a violation of antitrust law.”2
The antitrust agencies have investigated and even challenged several mergers involving large datasets. In some cases, the data is the market implicated by the merger itself, such as the real property data at issue in the 2014 CoreLogic-DataQuick merger, which the Federal Trade Commission cleared with a database divestiture.3 In other cases, data is a key input for delivery of a digital product, such as the national audience measurement data implicated in the 2013 Nielsen-Arbitron merger, which involved an FTC-imposed data divestiture.4 In still other cases, data is viewed as an entry barrier, as in Google’s 2011 acquisition of ITA, which the DOJ cleared with a prohibition against Google denying competitors access to ITA’s online flight pricing data.5
Still, the antitrust agencies have found that access to greater amounts of data can actually increase competition. In Microsoft-Yahoo!, the DOJ investigated and did not challenge the companies’ agreement to combine their back-end search and paid search advertising technology. The agency concluded that the transaction would “enhance Microsoft’s competitive performance because it will have access to a larger set of [search] queries, which should accelerate the automated learning of Microsoft’s search and paid search algorithms.”6 The transaction would thereby create a more viable competitive alternative to Google.
Data-related efficiencies also played a role in the DOJ’s clearance of eBay’s acquisition of PayPal. The merging companies both provided person-to-person payment systems used to complete transactions in connection with eBay auctions. The DOJ concluded that the integration of the two companies “would make transactions more convenient for eBay buyers and also improve the detection of fraud by combining the information that had been separately amassed by the two companies.”7
Recent Transactions: Big Data as a Merger Efficiency
AT&T-Time Warner
More recently, AT&T and Time Warner succeeded in rebuffing the DOJ’s challenge to their merger in part by pointing to the benefits of combining the companies’ complementary data and other assets. A key industry trend prompting the merger was the growing popularity of streaming video and other online content providers. As a traditional programmer, Time Warner lacked direct relationships with its viewers (i.e., customers of video distributors such as cable companies) and therefore information about the content preferences of those viewers. That put Time Warner at a competitive disadvantage relative to streaming video services such as Netflix and Hulu and online platforms such as Google and Facebook in catering its content and advertising to viewers.
The district court hearing the AT&T-Time Warner merger challenge noted the benefits from the vertical integration of the companies’ complementary data, including: “Time Warner’s popular content and significant advertising inventory, and AT&T’s consumer relationships, customer data, and large wireless business.”8 Through the merger, the court found, the combined company can use information from AT&T’s mobile and video customers to better tailor Time Warner’s content and advertising to better compete with online platforms.9
CVS Health-Aetna
The CVS Health-Aetna merger offered substantial efficiencies, including efficiencies driven by data integration. One of the major problems plaguing our healthcare system is that information is siloed. For example, physicians and hospitals may lack access to pharmacy claims data. Pharmacies may lack access to medical records. These inefficiencies can harm patients and lead to higher-cost, lower-quality care.10
The CVS Health-Aetna merger combined large quantities of medical and pharmacy data from the companies’ respective patients and customers. To obtain government approvals, Dechert turned big data from a potential problem to a solution. We were able to show that the combination of CVS Health and Aetna would lead to better-integrated medical and pharmacy data. As a result of that integration, providers will be able to intervene earlier and more often to help patients before complications develop, improve patient care, and reduce healthcare costs.
As shown in the public records,11 the merger positioned the combined firm to improve patient care and lower costs, including in the following ways:
- Using better information and through earlier interventions, the combined firm will be able to improve medication adherence for patients with chronic diseases such as diabetes.12
- Similarly, a pharmacist will know about a patient’s recent hospitalization and be able to counsel the patient to avoid any drug complications that could lead to readmission.13
- With better access to medical records, a pharmacist will know that a patient has not received a recommended immunization and can encourage the patient to get it at either a MinuteClinic or from the patient’s physician.14
- Finally, using analytical tools and medical claims data, providers can identify members at high risk of avoidable ER visits and—through pharmacist and other interactions—educate them about the availability of retail clinics, urgent care centers, and other less-expensive sites of care as appropriate.15
Importantly, we were able to demonstrate that these data-driven efficiencies are merger-specific—that is, they can only be achieved through the merger. In particular, the efficiencies were unlikely to be achieved through a mere contractual relationship between independently-owned companies, where allocating the costs and risks associated with the joint investment needed to reduce medical costs is particularly challenging.
As the DOJ recognized in concluding its investigation of the CVS Health-Aetna merger, the resulting “integrated pharmacy and health benefits company . . . has the potential to generate benefits by improving the quality and lowering the costs of the healthcare services that American consumers can obtain.”16
Conclusion
Offensive use of big data has helped clear the path to government approvals of the CVS Health-Aetna and other mergers. By understanding current, inefficient data limitations and ways to overcome them, companies can show that mergers will facilitate the use of data to improve products and lower costs, ultimately to the benefit of consumers.
Footnotes
1) See American Bar Association Section of Antitrust Law, Artificial Intelligence & Machine Learning: Emerging Legal and Self-Regulatory Considerations (Part One), at 2 (Sept. 30, 2019), https://www.americanbar.org/content/dam/aba/administrative/antitrust_law
/comments/october-2019/clean-antitrust-ai-report-pt1-093019.pdf.
2) Richard Vandeford, Tech Players Extensively Meeting with DOJ as Big Tech Probe Continues, MLex, Nov. 6, 2019.
3) See Decision & Order at 5-8, In re CoreLogic, Inc., FTC Docket No. C-4458 (May 21, 2014), https://www.ftc.gov/system/files/documents/cases/140521corelogicdo.pdf.
4) See Decision & Order at 5-7, In re Nielsen Holdings N.V., FTC Docket No. C-4439 (Feb. 28, 2014), https://www.ftc.gov/system/files/documents/cases/140228nielsenholdingsdo.pdf.
5) See Final Judgment at 13-21, United States v. Google Inc., Case No. 1:11-cv-00688 (D.D.C. Oct. 5, 2011), https://www.justice.gov/atr/case-document/file/497636/download.
6) Press Release, U.S. Department of Justice, Antitrust Division, Statement of the Department of Justice Antitrust Division on Its Decision to Close Its Investigation of the Internet Search and Paid Search Advertising Agreement Between Microsoft Corporation and Yahoo! Inc. (Feb. 18, 2010), https://www.justice.gov/opa/pr/statement-department-justice-antitrust-division-its-decision-close-its-investigation-internet.
7) Federal Trade Commission & U.S. Department of Justice, Commentary on the Horizontal Merger Guidelines 55 (2006), https://www.ftc.gov/sites/default/files/attachments/merger-review/commentaryonthehorizontalmergerguidelinesmarch2006.pdf.
8) United States v. AT&T Inc., 310 F. Supp. 2d 161, 182 (D.D.C. 2018).
9) See id. at 182-83.
10) See, e.g., Thomas M. Moriarty, Executive Vice President, Chief Policy and External Affairs Officer, and General Counsel, CVS Health Corp., Testimony before the Subcommittee on Regulatory Reform, Commercial and Antitrust Law of the U.S. House of Representatives Committee on the Judiciary, “Competition in the Pharmaceutical Supply Chain: the Proposed Merger of CVS Health and Aetna” (Feb. 27, 2018), https://docs.house.gov/meetings/JU/JU05/20180227/106898/HHRG-115-JU05-Wstate-MoriartyT-20180227.pdf (discussing shortcomings with current healthcare system and ways in which the CVS-Aetna transaction is uniquely positioned to address them).
11) See CVS Health Corporation’s Memorandum in Response to the Court’s December 3, 2018 Order to Show Cause at 11-13, United States v. CVS Health Corp., Civ. Action No. 1:18-cv-02340-RJL (D.D.C. Dec. 14, 2018); Moriarty, supra note 10, at 3-7; CVS Health Corp., Transforming the Consumer Health Care Experience: How our Integrated Model Addresses Key Patient Challenges (Jan. 9, 2019), https://payorsolutions.cvshealth.com/sites/default/files/cvs-health-payor-solutions-transforming-the-consumer-health-care-model-briefing-jan-2019.pdf.
12) See CVS Health Memorandum, supra note 11, at 12.
13) See id.
14) See id. at 13.
15) See id.
16) See Press Release, U.S. Department of Justice, Antitrust Division, Justice Department Requires CVS and Aetna to Divest Aetna’s Medicare Individual Part D Prescription Drug Plan Business to Proceed with Merger (Oct. 10, 2018), https://www.justice.gov/opa/pr/justice-department-requires-cvs-and-aetna-divest-aetna-s-medicare-individual-part-d.
