Big Data Mergers: Bridging the Gap for an Effective Merger Control Framework

Abstract

The emergence of multi-sided platforms, connected devices and Internet of Things (IoT) has turned us into a valuable information asset, whereby data about our tastes and preferences as a consumer can be ‘commoditised’. Even though ‘data’ is the key to competition, and thereby ensures competitiveness across markets – as diverse from retail to healthcare, from taxi rides to air travel, thanks to the uberisation of the economy – this valuable reservoir of information is controlled by a handful of Information Technology (IT) firms. Remarkably noteworthy is the fact that a significant proportion of the growth of these IT companies is not organic; instead, most of their valuable innovations have been acquired inorganically through acquisitions! Against this dynamic backdrop, this paper addresses the following research questions. First, what are the potential suitable tests for the notification of a transaction, and what factors must be taken into consideration for the selection of a particular test over others? Second, how can competition authorities innovate as regards the ‘theory of harm’? In other words, what should be the design and construct of a theory that can effectively capture the novel concerns in big data mergers? Here, the discussion is not just limited to ‘privacy’ as a dimension of competition, but also other areas of concern – such as non-horizontal effects in big data mergers. Finally, the paper very briefly discusses key factors to be taken into consideration for designing effective remedies.