Prospecting (in) the data sciences

• Published in: Big data & society Vol. 7; no. 1; p. 205395172090684
• Main Authors: Slota, Stephen C, Hoffman, Andrew S, Ribes, David, Bowker, Geoffrey C
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-01-2020; Sage Publications Ltd; SAGE Publishing
• Subjects: Big Data; Data science; Data search; Domains; Epistemology; Exploration; Science; World problems; logic of domains; Infrastructure; science studies; science policy; data science; Big Data hubs and spokes
• ISSN: 2053-9517; 2053-9517
• DOI: 10.1177/2053951720906849

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(izing) science.