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Big Data as Governmentality – Digital Traces, Algorithms, and the Reconfiguration of Data in International Development

December 06, 2016 by Mikkel Flyverbomreas Rasche

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This paper conceptualizes how large-scale data and algorithms condition and reshape knowledge production when addressing international development challenges. The concept of governmentality and four dimensions of an analytics of government are proposed as a theoretical framework to examine how big data is constituted as an aspiration to improve the data and knowledge underpinning development efforts. Based on this framework, we argue that big data’s impact on how relevant problems are governed is enabled by (1) new techniques of visualizing development issues, (2) linking aspects of international development agendas to algorithms that synthesize large-scale data, (3) novel ways of rationalizing knowledge claims that underlie development efforts, and (4) shifts in professional and organizational identities of those concerned with producing and processing data for development. Our discussion shows that big data problematizes selected aspects of traditional ways to collect and analyze data for development (e.g. via household surveys). We also demonstrate that using big data analyses to address development challenges raises a number of questions that can deteriorate its impact.