Debating big data: A literature review on realizing value from big data

By Wendy Arianne Günther, Mohammad Hosein Rezazade Mehrizi, Marleen Huysman, Frans Feldberg. Journal of strategic information systems, 2017

820 citations recorded by the source corpus.

Abstract Big data has been considered to be a breakthrough technological development over recent years. Notwithstanding, we have as yet limited understanding of how organizations translate its potential into actual social and economic value. We conduct an in-depth systematic review of IS literature on the topic and identify six debates central to how organizations realize value from big data, at different levels of analysis. Based on this review, we identify two socio-technical features of big data that influence value realization: portability and interconnectivity. We argue that, in practice, organizations need to continuously realign work practices, organizational models, and stakeholder interests in order to reap the benefits from big data. We synthesize the findings by means of an integrated model.

Subfields: Management Information Systems, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/bc0fd9a19026326a07295525599503a01b02f4a4

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Citations in the corpus

Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities (2020, background): “Günther et al. [13] survey literature in the field and identify six areas surrounding big data and competitive performance.”
Unpacking the Difference Between Digital Transformation and IT-Enabled Organizational Transformation (2021, background): “Moreover, organizations continuously experience internal transformations that change how they create value (Dremel et al., 2017; Günther et al., 2017; Huang et al., 2017; Porter & Heppelmann, 2015; Wulf, Mettler, & Brenner, 2017) and structure their processes (Morakanyane, Grace, & O’Reilly, 2017;…”
Big data analytics in smart grids: a review (2018, methodology): “The processing and analysis method of these large volume data is a new challenge but opportunity at the beginning of this century with the concept of “ big data ” (Lv et al., 2017a; Günther et al., 2017).”
Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture (2019, neutral): “…al., 2017; Gunasekaran et al., 2017; Fosso Wamba et al., 2017; Srinivas and Swink, 2018; Wang et al., 2018; Aydiner et al., 2019), research on the influence of BDPA on manufacturing performance under the influence of external conditions is limited (Gunasekaran et al., 2017; Gunther et al., 2017).”
Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment (2019, neutral): “To be able to react on such insight and modify or renew the way the firm operates requires maturity in additional complementary aspects (Günther, Mehrizi, Huysman, & Feldberg, 2017).”