From the Digital Economy “Communities and Culture” Network: “The term ‘Big Data’ carries a great deal of currency in business and academic spheres. Data and their subsequent analysis are obviously not new. ‘Bigness’ in this context often refers to three characteristics that differentiate it from so-called ‘small’ data: volume, variety, and velocity. These three attributes of ‘bigness’, promising to open novel, macro-level perspectives on complex issues (Boyd and Crawford 2011), led enthusiasts like Chris Anderson to claim that ‘with enough data, the numbers speak for themselves”. But is this actually the case? Critical voices like Manovich (2011) argue that data never exist in ‘raw’ forms but are rather influenced by humans who—whether intentionally or not—select and construct them in certain ways.
These debates about data are relevant to wider discussions about digital change in society because they point to a more general concern about the potential of all sizes of data to selectively reveal dimensions of social phenomena on which decisions or policies are based. Crucially, if data generation and analysis is not entirely neutral but rather carries assumptions about what is ‘worthwhile’ or ‘acceptable’ to measure in the first place, then it raises critical questions of whether preferences for certain types of research—particularly work conducted under the auspices of a Big Data ‘brand’—reflect coherent sets of values and worldviews. What assumptions underpin preferences for ‘evidence-based’ research based on data? What qualities does such a phrase signify or confer to research? Which ‘sizes’ of data qualify as ‘evidence’ in the first place, or, to play on Anderson’s words, what kinds of data are allowed to speak for themselves in the realms of policy, media, and advocacy?
Hosted at the ESRC Centre on Migration, Policy, and Society (COMPAS) and The Migration Observatory at the University of Oxford, this project critically interrogates the values that inform demands by civil society organisations for research that is ‘data-driven’ or ‘evidence-based’. Specifically, it aims to document the extent to which perceived advantages of data ‘bigness’ (volume, variety, and velocity) influence these demands.
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