Paper short abstract:

This paper explores values of management in data science. Focusing on new modeling practices, I explore how data science generates "insightful" knowledge, paying attention to how data science enables novel notions of evidence, expertise, and interdisciplinary practice.

Paper long abstract:

Data scientists frequently describe their work as providing not just knowledge, but "insight," according well with data science's promise that by experimentally combining data from different life worlds the profession can capture or disclose new patterns, behaviors, or objects that would not otherwise be knowable. Insight is part of how data scientists distinguish themselves from their parent disciplines, such as statistics, information science, and the human sciences, enabling the self-image of the data scientist as a new kind of super-consultant who provides science-as-a-service.

The history of data science suggests that values from management and the promise of "insight" have played a crucial role in crystallizing a distinctive approach to modeling within the young profession. As practitioners sought to loosen their historical bonds to medicine, science, and policy to enter the lucrative worlds of consulting and engineering, they turned away from exploring why things happened, adopting approaches attuned to prediction and speculation. Recent developments indicate similar trends at work, as the emerging field of "computational social science" seeks to scale-up and unify the social and behavioral sciences through data science modeling (Gonçalves & Perra, 2015; Lazer et al., 2009; Raghavan, 2014).

Drawing on the literature on modeling cultures (Morgan & Morrison, 1999), this paper will probe how practitioners in the rapidly expanding field of data science generate distinctively "insightful" knowledge, paying particular attention to how data science enables novel notions of evidence, new forms of scientific self-presentation, and new forms of interdisciplinary practice.