Big data, organizational learning, and sensemaking: Theorizing interpretive challenges under conditions of dynamic complexity
从意义建构、学习和复杂性视角，提出组织有效利用大数据需应对的四个关键挑战，包括动态复杂性、跨学科分析、意识形态反思和意义对齐，对管理者和研究者理解大数据与组织学习的关系有参考价值。
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.