Domain experts as owners of data: towards sustainable data science

Abstract

Research on data science has largely viewed data as an abstract input in service of algorithms developed by data scientists. In this view, data science activities are made sustainable by the efficient flow of data to improve the algorithms. Recent studies in CSCW and HCI, however, point to how the effectiveness of algorithms critically depends on sustainably collecting reliable, complete data situated in domain experts’ practices and settings. Drawing on ethnographic fieldwork and a pilot machine learning project at a craft brewery, we describe three types of situations where brewers’ data practices led to unreliable, incomplete data, and how such data practices limited the effectiveness of data science activities. We analyze sources of misalignment between their data practices and data science activities, which we use to offer design implications for sustainability. Extending research on end-user software development that views sustainability as driven by domain experts as ‘owners of problems,’ our study proposes data science research driven by domain experts as ‘owners of data.’