Faculty of Management Research Workshop; Christopher Steele, Alberta School of Business
REWORKING THE ‘TRUTH MACHINE’: HOW DATA ANALYSTS, AND OTHERS, (RE)CONSTRCT THE EPISTEMIC PRACTICES OF ORGANIZATIONS
CHRISTOPHER STEELE, ALBERTA SCHOOL OF BUSINESS
Organizations live and die on the basis of their epistemic practices: the practices through which members determine what is to be accepted as true about the organization and its world, establishing the foundations of decision-making and action. But how does it come to be that an organization determines the truths of its world through one set of epistemic practices, and not another? Here is a space of contestation, experimentation and political skill that, although hugely consequential, seems as yet underappreciated and understudied. In this presentation, I aim to cast light on this topic – articulating some of the techniques and activities through which epistemic practices are crafted and legitimated, and through which the ‘minds’ of organizations are accordingly structured and transformed. To develop my argument, I draw from ongoing empirical work on data analytics: an array of epistemic practices that is currently surging into prominence, based in part on the rise of machine learning and artificial intelligence. I will show how data analysts seek to craft and enact analytic practices in such a way that these practices gain organizational legitimacy and influence; transforming the grounds of organizational decision-making and action, and perhaps even the nature of organizational life. My intention is to cast light on the nature of data analysts' work and impact, on the dynamics of organizational truth-making more broadly, and on the foundations of organizational cognition.
Christopher Steele received his PhD from the Kellogg School of Management, Northwestern University, and is an Assistant Professor at the Alberta School of Business. His research interests revolve around three overarching topics: the production and consumption of knowledge, the emergence and maintenance of collective identity, and the production of order and meaning in everyday life. In his work on data analytics, he is interested in exploring, first, how certain things come to be accepted as true, self-evident, practical or impossible, and, second, when and how different methods of knowing are prioritized in organizations, and in societies. His research is primarily qualitative, and draws on resources from institutional, organizational, and practice theories, the sociology of knowledge, and social theory more broadly.
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