Big Data and Society 7 (1) (2020)

Abstract
Data science is characterized by engaging heterogeneous data to tackle real world questions and problems. But data science has no data of its own and must seek it within real world domains. We call this search for data “prospecting” and argue that the dynamics of prospecting are pervasive in, even characteristic of, data science. Prospecting aims to render the data, knowledge, expertise, and practices of worldly domains available and tractable to data science method and epistemology. Prospecting precedes data synthesis, analysis, or visualization, and is constituted by the upstream work of discovering disordered or inaccessible data resources, thereafter to be ordered and rendered available for computation. Through this work, data science positions itself in the middle of all things—capable of engaging this, that, or any domain—and thus prospecting is a key driver of data science’s ongoing formation as a universal science.
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DOI 10.1177/2053951720906849
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References found in this work BETA

The Taming of Chance.Ian Hacking - 1990 - Cambridge University Press.
Michel Serres.Steven D. Brown - 2002 - Theory, Culture and Society 19 (3):1-27.
Rules and Implements: Investment in Forms.Laurent Thévenot - 1984 - Social Science Information 23 (1):1-45.

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Data objects for knowing.Fred Fonseca - forthcoming - AI and Society:1-10.

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