In the debate about the significance of big data for business, the phenomenon is often presented as a technology-based avenue to competitive advantage: a new frontier of IT-enabled experimentation, innovation, and customer centricity (Chen, Chiang, & Storey, 2012; Davenport, 2013; McAfee & Brynjolfsson, 2012; Parmar, Mackenzie, Cohn, & Gann, 2014). Much of this debate was initially driven by simplistic and optimistic notions often stemming from various evangelists, consulting firms, and other practitioners (e.g., Anderson, 2008;Chui, Manyika, & Bughin, 2011; Mayer-Schönberger & Cukier, 2013). Harnessing big data can allegedly produce outcomes recurrently described as ranging from better overall financial performance and optimized business prioritization to increased customer insight that can favorably affect innovation (Davenport, 2014; McAfee & Brynjolfsson, 2012; Pigni, Piccoli, & Watson, 2016; The Economist, 2012; Westerman, Bonnet, & McAfee, 2014). While the possibility for such outcomes to manifest themselves cannot be questioned, they have hitherto mostly been of a hypothetical nature, and to the extent that they reflect reality, it has mostly concerned a few actors that are repeatedly referred to in the debate as successful examples (cf. Goes, 2014). Among other things, these actors tend to have highly digitized operations, to be data-driven companies, and to have been frequently, but not exclusively, born digital, i.e. having embraced digital technologies since their inception. Examples of such firms are Amazon, Dell, eBay, Facebook, Google, LinkedIn, Netflix, Procter & Gamble, Target, Tesco, UPS, Walmart, and Zara (Davenport & Harris, 2007; Manyika et al., 2011; Smith & Telang, 2016; The Economist, 2010; Westerman et al., 2014). However, the vast majority of organizations, particularly incumbent organizations, which make up the largest part of the economy, are not yet conversant with big data (Goes, 2014; Sanders, 2016). While many of these organizations understand that they operate in data-rich environments, they do not understand how to exploit that data (Ross, Beath, & Quaadgras, 2013). Vendors who promote various sets of technologies (e.g., Frizzo-Barker, Chow-White, Mozafari, & Ha, 2016; Wang, Xu, Fujita, & Liu, 2016) that, they argue, can enable clients to manage big data through, for instance, big data analytics (BDA) operations, often do so by adding to the choir of simplistic and optimistic chants (e.g., IBM, 2011; IBM, 2012; Manyika et al., 2011). Frequently the various sets of technologies are offered as generic solutions to problems that are not easily identified as such by the incumbents. The level of confusion only increases in organizations that are considering buying a set of technologies as these sets form a rather fragmented landscape of technologies (Goes, 2014). Judging from the aforementioned exemplary companies, positive big data outcomes are related to systematic and coordinated efforts “as part of an overarching strategy championed by top leadership and pushed down to decision makers at every level” (Sanders, 2016:27). Despite the attention and significance attributed to big data by scholars lately (e.g., Abbasi, Sarker, & Chiang, 2016; Agarwal & Dhar, 2014; Chen et al., 2012; George, Haas, & Pentland, 2014; Goes, 2014; Kallinikos, 2013), very little is known about how incumbents explore and, if possible, implement big data technologies and what challenges are associated with such endeavors. Yet, it is incumbents that stand to gain the most when such technologies, in various forms, are applied in their organizations (e.g., Gandomi & Haider, 2015; Varian, 2013).