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

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)65-82
JournalManagement Learning
Volume47
Issue number1
Early online date10 Dec 2015
DOIs
Publication statusPublished - 14 Jan 2016

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