Sociologists encounter digitized data in all aspects of social life, albeit data of indefinite quality. We ask, how might scholars use the process of inspecting and cleaning such data as an occasion to advance social theory? We draw on Art Stinchcombe’s theory of formality to demonstrate how the craft of data wrangling can be used to assess and improve the quality of our concepts. Drawing on examples from contemporary computational social science, we show how the cognitive adequacy, communicability, and improvability of conceptual abstractions can be developed and refined in the data wrangling process. Using Stinchcombe to highlight the link between theorizing and the organizational and institutional systems in which such theorizing is embedded, we argue that the systems that impact the quality of our data simultaneously impact the quality of our concepts. Inspired by AI oversight research, we provide a roadmap for evaluating the systems involved in computational abstractions.
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