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The history, evolution, and future of big data and analytics 25 2.1 Introduction Big data and analytics continue to spark interest among scholars and practitioners. Organizations are increasingly aware that they may transform and process their large data volumes to capture value for their businesses and employees (George, Haas, & Pentland, 2014). With the advent of more computational power, machine learning – particularly deep learning through neural networks – has become more broadly deployable in organizations. These techniques may realize the predictive value of big data, unleashing its strategic potential to transform business processes and providing the organizational capabilities to tackle key business challenges (Fosso Wamba et al., 2015). When searched for the term “ big data ”, the Web of Science Core Collection yields 3,347 hits in 2015, and over 4,000 publications in both 2016 and 2017. Some of these studies indeed demonstrate how big data and analytics (BDA) impact organizational performance, demonstrating that firms with data-driven strategies tend to be more productive and profitable than their competitors (Brynjiolfsson, Hill, & Kim, 2011; LaValle et al., 2011). Yet, very few attempts have been made to culminate this plethora of BDA research. Although some scholars have reviewed how organizational value can be derived from BDA, this relationship is often approached from a rather narrow information systems or information technology perspective (for some exceptions see Fosso Wamba et al., 2015; Grover & Kar, 2017; Günther, Rezazade Mehrizi, Huysman, & Feldberg, 2017). Calls to explore the impact of BDA from other organizational and management perspectives (e.g., human resources; Angrave et al., 2016) remain largely unanswered to this date. A more comprehensive review of the implications of BDA for the management of performance in and of organizations seems warranted. Synthesizing past research findings is one of the most important tasks for advancing a field of research, particularly one characterized by an extensive growth of publications (Garfield, 2004; Zupic & Čater, 2015), such as BDA reserach. An overview of the BDA-performance debate may (a) delineate the subfields that constitute the intellectual foundation of the debate and how these subfields relate to one another, (b) unveil and explore the evolution and roots of the debate, and (c) provide insight into the future development of the debate. Moreover, by revealing discrepancies in the applications and perspectives within the different functional management domains and their research streams, a review may allow for cross-fertilization of best practices, research designs, and theoretical frameworks. Particularly a bibliometric review, using science mapping would provide several advantages over classical qualitative and meta-analytical methods. First, a bibliometric approach is more macro-oriented because it allows the analysis of a comprehensive field of research. Researchers do not need to choose the exact relationship they wish to explore which offers increased objectivity in reviewing literature (Garfield, 1979). Second, science mapping consists of a classification and visualization of previous research (Small, 1999). This produces a spatial representation analogous to a geographic map that can demonstrate how knowledge domains and individual papers relate to one another. This seems particularly useful for BDA research because it spans between research domains (Günther et al., 2017) and may provide the “bigger” picture of the state of the art of these