In December 2022, JPMorgan Chase sued Charlie Javice, the CEO of Frank (a student financial aid start-up), alleging that Javice falsified data to justify the $175 million acquisition price that Chase paid for the company. Throughout the due diligence process, Javice portrayed that Frank served millions of users and maintained a database of related user data. When Chase sought access to the this data during due diligence, Javice allegedly created millions of fake accounts using false data to make the actual 300,000 accounts appear to be 4.25 million accounts. Arguing that they never would have purchased Frank if they had known only 300,000 accounts existed, Chase now seeks damages for fraud and unjust enrichment, among other claims. Javice, meanwhile, faces criminal charges in New York Federal Court. In an acquisition like this, where the scope and size of the data held by the target company were key drivers for the deal, the importance of data’s intrinsic value to the company’s valuation is obvious.
Other data-driven mergers, however, are not so easily identifiable. For example, several months ago, Capital One announced its intention to acquire Discover Financial Services, combining two of the largest credit card companies in America. Capital One is currently the fourth largest credit card issuer, with approximately 100 million customers. Discover operates as a credit card network, with 300 million users. While the combination has the potential to help Capital One overtake Chase as the largest issuer of credit cards in America, there are other incentives for the deal—consumer data. We argue that in such a transaction, where data plays a low profile but key role in driving the merger, understanding the valuation that Capital One ascribes to Discover Financial Services requires properly and formally including the value of data into the valuation of the target company in the same way JPMorgan Chase did in their valuation of Frank.
In our article, Uncovering Elon’s Data Empire, we use Elon Musk’s acquisition of Twitter as a case study of the less obvious variation of data-driven mergers—where the value of data in the transaction only becomes apparent by considering the transaction in the broader context of Musk’s other business ventures. We argue that properly understanding Musk’s acquisition of Twitter requires viewing it as part of a larger business strategy focused on data collection and monetization. Indeed, we argue that by doing so, larger gaps in the corporate governance framework that should operate to protect shareholder investment in such transactions become apparent.
Recently, increasing discussion about the significance of data in enhancing corporate value has emerged, focusing on the concept of “data-driven mergers.” This involves large conglomerates acquiring companies rich in data primarily to gain access to their data, raising concerns regarding its potential to circumvent antitrust regulations. Acquisition of smaller, data-rich companies by large tech firms is just a small fraction of data mergers, and other impacts of such mergers often go unnoticed and unresolved.
For example, although the obvious impact of the Capital One acquisition of Discover relates to growing Capital One’s credit card market share, observers are already speculating as to other factors that might be driving the transaction—many of which are dependent on the acquisition of Discover’s customer data. For instance, some commentators point to the improvement of reward programs for both Capital One and Discover customers. Rewards programs rely heavily on customer data to account for consumer preferences and increase customer retention in the primary service offering—in this case, the credit card. Capital One does not just issue credit cards. They have customers with checking and savings accounts, issue automotive loans, and provide brokerage accounts. They rely on Visa and Mastercard networks to issue credit cards, but with Discover, they can become full service. With increased consumer data, Capital One can better personalize their offerings across products to both existing and newly acquired Discover customers.
If Capital One gets more than an issuer from the Discover merger, how can Discover shareholders ensure they receive adequate value? In our article, we suggest that understanding the full impact of data-driven mergers requires applying a data lens to transactions using a three-step approach. First, data harvesting should be recognized as central to the company’s valuation. Second, the data aspects and purposes of high-tech companies’ mergers should be analyzed and whether these mergers are vertical or horizontal within a corporate family. Third, how these companies plan to use and monetize the collected data should be examined. This analysis may uncover insights missed by traditional antitrust and corporate governance analyses.
Musk’s acquisition of Twitter emphasizes data’s crucial role in valuation. Most observers thought the valuation of Twitter depended upon the number of Twitter’s active users—much like the importance of Chase placed on Frank having 4.2 million users instead of 300,000. However, the transaction contract deferred to Twitter’s measurement of users using the daily active user, or mDAU, metric, and Musk attempted to reduce the price over a dispute about the presence of bots on the platform. Applying a data lens to the transaction widens the understanding of Twitter’s data value beyond simple metrics like mDAU to encompass other broader uses of data related to Twitter’s users. To be valuable for use in other data-intensive ventures, the data needs to be high quality and representative of the activity of real people. Understanding the Twitter deal and related controversy in this light highlights a pivotal shift towards valuing companies based on their potential to contribute to and leverage vast data ecosystems for innovation, underscoring data’s growing importance in corporate governance.
Similarly, Capital One’s interest in Discover may go beyond the obvious to an interest in a larger data set that can fuel improved products and services across its offerings. Only by applying a data lens to the transaction will Discover shareholders and the Discover board know whether Capital One’s offering price is adequate in light of Captial One’s broader business goals. To properly value the Capital One-Discover merger and consider the regulatory and antitrust implications, full consideration must be given to the value of data included in the acquisition. The value of Discover may extend far beyond the dollars it collects from credit card customers—it may also include the value of information about those customers.
Carliss N. Chatman is an Associate Professor of Law at the SMU Dedman School of Law and a Faculty Affiliate of the Samuel DuBois Cook Center at Duke University.
Carla L. Reyes is a Robert G. Storey Distinguished Faculty Fellow and Associate Professor of Law at the SMU Dedman School of Law.
This post was adapted from their paper, “Uncovering Elon’s Data Emprie,” 53 Stetson Law Review 405 (2024), which is available in print and on SSRN.
