Anticipation by Data? Socio-Material Entanglements in Datafied Efforts to See, Know and Govern Futures

Data analytics and automated forms of pattern recognition are presented as the new frontier of prediction and forecasting. Data enthusiasts and big tech companies are busy rolling out data-driven approaches to security and police work, human resource management and other forms of governance in organizational settings. The goal is more accurate, more proactive, and more objective methods for the anticipation of developments on the horizon – the opportunity to deal effectively with the future in the now. This paper sets out to challenge such assumptions about digital data and algorithms as a direct path to anticipation through conceptual and empirical exploration. First, it unpacks conceptually the complex work and technological and social forces that go into such datafied forms of knowledge production. On this backdrop, the paper examines the make-up and operations of a data-driven, algorithmic tool developed by Google with the aim of anticipating and preempting radicalization in digital spaces, and shows the complex entanglements between resources and forms of knowledge production involved. The paper articulates how attempts to make social problems visible and governable through data and algorithmic sorting involve complex forms of labor and socio-material entanglements that deserve more attention. To understand how futures are produced and steered through data and algorithms, we need to return to fundamental questions about how social worlds are made seeable, knowable and governable through situated processes of knowledge production. The paper contributes to emergent research on how digital transformations and processes of datafication condition and relate to contemporary attempts to frame and govern societal challenges and opportunities.