This chapter aims to review data-driven mergers including, but not limited to, major conglomerates involving large scale of individual user data, known as ‘big data’, by Facebook (WhatsApp), Microsoft (Yahoo!, Skype and LinkedIn), Google (Double Click), TomTom (Tele Atlas), Publicis/Omnicon, Telefonica/Vodafone UK, and so on. These mergers have been unconditionally cleared based on the traditional law and economic analysis of mergers, known as a ‘significant impediment to effective competition’ legal test. The test disregards public policy concerns, including the economics of privacy, i.e., data analytics; data sharing with third parties, e.g., publishers or retailers; and data selling. The chapter draws on previous research on the rise of big data and the loss of privacy, which sheds light inter alia on the ineffectiveness of the data, consumer and competition rules and on the intrusive privacy policies of the various digital platforms. This chapter argues that 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. The chapter arrives at the conclusion that the new data protection framework is insufficiently robust. The contract theory of informed consent associated with the potential of sharing anonymised and/or aggregated data means that digital platforms are able to exploit data protection loopholes and abuse users’ trust in digital platforms. In addition, the chapter looks at the treatment of innovative digital platforms from the perspective of Schumpeterian economics and therefore identifies the fallacy of too great a reliance on ephemeral market shares. It discusses more critically the expectation of a robust and coherent theory of harm to consumers in the context of digital markets.