Dear COR Community,
Please join us for our next paper development workshop on Friday, May 24, featuring work of Professors Katie Pine and Melissa Mazmanian, “Organizing for Data: Crafting Data Elements in the ‘Data-Driven’ Organization.”
Discussants: Kim Fortun (Anthropology) and Gerardo Okhuysen (Merage)
May 24, 2019
Please RSVP to email@example.com by May 15 to receive a paper to read ahead of the workshop. Lunch will be provided.
“Organizing for Data: Crafting Data Elements in the ‘Data-Driven’ Organization”
Organizations in a variety of sectors are adopting an array of techniques known broadly as “data driven” or “algorithmic management.” Digital workflow systems increase the availability of high-quality information available in real time. The promise of such systems lies in the representation of activities and work process that provide new forms of knowledge and enhanced decision making capacity. However, in order to achieve these goals, workflow system require stable, accurate, and uniform data. Creating and crafting such data rarely happen in everyday organizational processes. While recent literature has examined how the effects of such workflow systems on organizational functioning, little scholarly attention has been paid to how organizations must organize in order to become “data driven.” In other words, what is the work involved in “organizing for data” that organizations engage in? Emerging from a multi-sited ethnographic study of multiple hospitals and a meso-level healthcare policy organization, we find that organizations re-tooled existing work practices and implemented new forms of data work in order to create, manage, and deploy data for management, research, and accountability. Such practices are a precursor to becoming “data driven” and suggest that we cannot begin to understand scope of what it takes to become “data driven” without examining the practices that organizations must engage in prior to implementation of complex workflow systems. Further, we find that organizing for data is never complete. Even when it appears that a data lifecycle has reached an idealized state and has been made amenable to a comprehensive system, subjectivities remain. Data crafting requires situated judgement and decision making. However, these subjectivities are masked in service of creating ‘clean’ and ‘objective’ measures that are legible to digital workflow systems.