Data Vision: Learning to See Through Algorithmic Abstraction
This addresses the problem of understanding human work in data analytics for researchers and practitioners, but it is incremental as it builds on existing CSCW and social science research.
The paper examines how data analytics involves both mechanical rule application and situated decision-making, showing that effective 'data vision' requires balancing formal abstraction with empirical contingency.
Learning to see through data is central to contemporary forms of algorithmic knowledge production. While often represented as a mechanical application of rules, making algorithms work with data requires a great deal of situated work. This paper examines how the often-divergent demands of mechanization and discretion manifest in data analytic learning environments. Drawing on research in CSCW and the social sciences, and ethnographic fieldwork in two data learning environments, we show how an algorithm's application is seen sometimes as a mechanical sequence of rules and at other times as an array of situated decisions. Casting data analytics as a rule-based (rather than rule-bound) practice, we show that effective data vision requires would-be analysts to straddle the competing demands of formal abstraction and empirical contingency. We conclude by discussing how the notion of data vision can help better leverage the role of human work in data analytic learning, research, and practice.