Acting on Analytics: Accuracy, Precision, Interpretation, and Performativity

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Case Study—We report on a two-year project focused on the design and development of data analytics to support the cloud services division of a global IT company. While the business press proclaims the potential for enterprise analytics to transform organizations and make them ‘smarter’ and more efficient, little has been written about the actual practices involved in turning data into ‘actionable’ insights. We describe our experiences doing data analytics within a large global enterprise and reflect on the practices of acquiring and cleansing data, developing analytic tools and choosing appropriate algorithms, aligning analytics with the demands of the work and constraints on organizational actors, and embedding new analytic tools within the enterprise. The project we report on was initiated by three researchers; a mathematician, an operations researcher, and an anthropologist well-versed in practice-based technology design, in collaboration with a cloud services go-to-market strategy team and a global cloud sales organization. The analytics were designed to aid sellers in identifying client accounts that were at risk of defecting or that offered opportunities for up-sale. Three-years of sales revenue data were used to both train and test the predictive models. A suite of analytic tools was developed, drawing upon widely available algorithms, some of which were modified for our purposes, as well as home-grown algorithms. Over the course of this project important lessons were learned, including that the confidence to act upon the results of data modeling rests on the ability to reason about the outcomes of the analytics and not solely on the accuracy or precision of the models, and that the ability to identify at-risk clients or those with up-sell opportunities by itself does not direct sellers on how to respond as information outside the models is critical to deciding on effective actions. We explore the challenges of acting on analytics in the enterprise context, with a focus on the practices of ‘real world’ data science.

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