Acting with Inherently Uncertain Data: Practices of Data-Centric Knowing
Data-driven data science challenges our conceptualization of “data.” Significantly beyond capturing a given phenomenon, data, increasingly, are the phenomenon. Data may be iteratively manipulated algorithmically, undermining the “faithfulness” of data to any originating phenomenon. Crucially, data that are not “faithful” are inherently uncertain as data risk becoming meaningless symbols. We empirically study how a community of commercially based geoscientists grapple with the phenomenon of offshore oil and gas reservoirs residing kilometers below the seabed. The data available about these reservoirs are algorithmically manipulated sensor-based Internet of Things data. Our main contribution is the articulation of three patterns of work practices detailing how inherently uncertain data are woven into consequential work practices: (i) accumulating, the cumulative process of supporting and triangulating one set of data with supplementary ones; accumulating captures the conservative approach of backing up existing interpretations of the data, (ii) reframing, the process where existing interpretations are contested by new data or models; reframing captures how there are limits to how far data may be pulled by their hair and (iii) prospecting, the cultivation of competing, incompatible data interpretations; with the former two patterns essentially attempting to regulate uncertainty, prospecting is about embracing it. Our concept of data-centric knowing is constituted by these three interleaved, ongoing practices.