Addressing the knowledge gap between business managers and data scientists: the case of data analytics implementation in a sales organization

While learning algorithms are assumed to support organizational decision making, their introduction requires the incorporation of new epistemic practices that are often orthogonal to the incumbent ways of knowing in the organization. In this study, we examine the organizational challenges that organizations face when they introduce learning algorithms to shift to data-driven decision making. We report on a qualitative study performed in the sales department of a telecommunications organization. The introduction of data analytics triggered a clash between account managers and data scientists. It brought to the surface deep-seated views about what kind of information mattered and how that informed judgements and actions. Those fundamentally different views impeded the collaboration between the two groups, who were unable to integrate their different epistemic practices. We analyze how the clash unfolded and discuss implications for theory and practice.