Big data, organizational learning, and sensemaking: Theorizing interpretive challenges under conditions of dynamic complexity. Issue 1 (February 2016)

• Record Type: Journal Article
• Authors: Calvard, Thomas Stephen
• Is Part Of: Management learning. Volume 47:Issue 1(2016:Feb.)
• Page Start: 65
• Page End: 82
• Publication Date: 2016-02
• Subjects: Big data -- complexity -- learning -- organizing -- reflexivity -- sensemaking; Industrial management -- Study and teaching -- Periodicals; Executives -- Training of -- Periodicals; Organizational learning -- Periodicals; Knowledge management -- Periodicals; 302.35
• DOI: 10.1177/1350507615592113
• Languages: English
• ISSNs: 1461-7307

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. This 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 21st century.