Preprint: Rieder, G. (2018). Tracing Big Data Imaginaries through Public Policy: The Case of the European Commission. In: Sætnan, A. R., Schneider, I., Green, N., eds., The Politics and Policies of Big Data: Big Data, Big Brother? New York; London: Routledge, pp. 89-109. 1 Tracing Big Data Imaginaries through Public Policy: The Case of the European Commission Gernot Rieder Abstract Across the globe, the notion of Big Data has received much attention, not only in technology and business circles but also among political authorities. Public officials in Europe, the U.S., and beyond have formulated Big Data strategies that will steer I(C)T development towards certain goals and aspirations. Drawing on official European Commission documents and using the notion of sociotechnical imaginaries as a sensitising concept, this chapter investigates the values, beliefs, and interests that guide European policymakers’ Big Data rhetoric, making the argument that while the Commission’s embrace of a strong free-market position can be partly explained in terms of vexing economic, institutional, and epistemic challenges, its push for Big Data solutions threatens to undermine democratic rights and principles as well as efforts towards responsible research and innovation. The article concludes with recommendations for further research, emphasising the need for cross-disciplinary dialogue and scholarship. I. Introduction In recent years, the term Big Data has emerged as a major buzzword, widely used by both public and private actors. A precise definition, however, remains elusive, as various stakeholders have offered different views – pointing, for instance, to the volume, velocity, and variety of data produced (see Laney 2012), new and improved ways to collect, store, process, and analyse those data (see Ward and Barker 2013), or profound changes in how people think, work, and live (see Mayer-Schönberger and Cukier 2013). Others have been more reluctant to buy into the hype, arguing that the current excitement is driven by inflated expectations rather than actual shifts in operational reality. 1 But while claims that the “Big Data bubble” is bound to burst sooner rather than later have been around for years (see Franks 2012), reports indicate that investments in Big Data solutions have only been increasing, with decision makers considering the ability to extract value from data as critical to future success (see Columbus 2015). Media and public interest, too, remains high, with the New York Times publishing 118 articles mentioning Big Data over the course of 2016 2 and a Google Trends analysis attesting to the continued popularity of the term in global search queries 3 . The reasons for the persistence of what has repeatedly been described as a “fad” destined to fade (see Woody 2012) are arguably twofold: On the one hand, the ongoing computerisation of ever more areas of human life – from social interaction and commerce to health care, law enforcement, and education – has provided ample opportunity for Big Data small talk. The notion has therefore become a convenient umbrella term, broad enough to be applicable to almost anything technology-related, while imparting a sense of urgency and importance. Big Data’s conceptual vagueness is thus very much part of the term’s appeal, 1 This is perhaps best visualised in Gartner’s (2013) Hype Cycle for Emerging Technologies, which shows Big Data right at the “Peak of Inflated Expectations”, gradually making its way toward the “Trough of Disillusionment”. 2 Articles have been identified and counted using the New York Times’ on-site search service. 3 Google’s Trends graph measures interest over time by showing the number of searches for a particular term relative to the total number of searches performed on Google.