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Big data, organizational learning, and sensemaking: Theorizing interpretive challenges under conditions of dynamic complexity

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    Rights statement: Final published paper available via http://dx.doi.org/10.1177/1350507615592113

    Accepted author manuscript, 535 KB, PDF document

Original languageEnglish
Pages (from-to)65-82
JournalManagement Learning
Volume47
Issue number1
DOIs
Publication statusPublished - 14 Jan 2016

Abstract

In this conceptual article, the relations between sensemaking, learning, and big data in organizations are explored. The availability and usage of big data by organizations is an issue of emerging importance, raising new and old themes for diverse commentators and researchers to investigate. Drawing on sensemaking, learning, and complexity perspectives, this article highlights four key challenges to be addressed if organizations are to engage the phenomenon of big data effectively and reflexively: responding to the dynamic complexity of big data in terms of ‘simplexity’; analyzing big data using interdisciplinary processes; responsible reflection on ideologies of learning and knowledge production when handling big data; and mutually aligning sensemaking with big data topics to map domains of application. The article concludes with additional implications arising from considering sensemaking in conjunction with big data analytics as a critical way of understanding unique aspects of learning and technology in the twenty-first century.

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