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Top1. Introduction
Much of the foundational research on technology-based initiatives - from the implementation of integrated enterprise systems, to the digital transformation of processes and products - examined their ability to sustain competitive advantages and create new competitive opportunities (Bradley et al, 2013; Chen et al, 2012; Mims, 2012; Piccoli & Ives, 2005; Sallam et al, 2013).
Today, organizations invest considerable resources in IT based/digital initiatives to search for value creation opportunities (Chen, Chiang & Storey, 2012, Braganza et al. 2017), drive their digital business strategies (Bharadwaj, El Sawy, Pavlou & Venkataraman, 2013) and make better informed business decisions (Eastburn & Boland, 2015; Brynjolfsson & McElheran, 2016).
All these IT-based initiatives rely on data and this data needs to be digital. Therefore, it has become more and more critical to know how data is generated and how digital data should be processed. Indeed, every day, both organizations and people generate digital data through tweets, clicks, videos and a plethora of sensors (Kietzmann & Canhoto, 2013). Furthermore, instruments and machines such as smart meters, manufacturing sensors, equipment logs and vehicle tracking systems automatically and continuously generate digital data.
The ability to generate and manage digital data creates the opportunity to reshape entire industries. In the entertainment sector, for instance, Netflix creates value in the form of personalization, through data streams. Netflix uses self-generated and external digital data, and social networks, to recommend personalized suggested titles based on a household’s preferences. It gains mass visibility through real-time sensing of users and makes recommendations based on this visibility (Piccoli and Pigni 2013). Netflix’s ability to collect and manage huge amounts of digital data allows the firm to design its products in a way that is not possible for the traditional movie studios. In the case of Netflix, as well as in many other cases such as Amazon, Uber, AirB&B etc. we observe that the digital nature of data constitutes a fundamental characteristic of the data itself. Digital data has unique properties that we do not find in physical infrastructures (Kallinikos, Aaltonen & Marton, 2013). It is easily shared, replicated, and combined thus presenting tremendous reuse opportunities (Lynch, 2008). At the same time, however, digital data is at risk of various forms of obsolescence (Lynch, 2008), so organizations need to leverage this data promptly.
The unique characteristics of digital data have contributed to its exponential growth (Kallinikos et al., 2013) and such growth requires new organizational approaches and specific research streams. “Businesses appear to be on the cusp of a data-driven revolution in management. Firms capture enormous amounts of fine-grained data on social media activity, RFID tags, web browsing patterns, consumer sentiment and mobile phone usage, and the analysis of these data promises to produce insights that will revolutionize managerial decision-making” (Tambe, 2014:1452). Such fine-grained data play an additional economic function in generating wishful content and unwitting meta-data that surround main content (Kallinikos et al., 2013; Orlikowski, 2015).