Data analytics practices are plagued with inefficiencies, according to a new report from automated data integration provider Fivetran. Polling circa 500 data professionals, the firm uncovered "surprising" information surrounding how data analysts spend their working days and the challenges they face. According to Fivetran, most data analysts spend less than half of the day actually analysing data. Much of the rest of the day is wasted as a result of various bottlenecks. For example, more than 60 percent reported wasting time waiting for engineering resources, multiple times a month.

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.