This is part of a track on “New Collective Practices of Measurement, Monitoring and Evidence” organised by Evelyn Ruppert, Daniel Neyland and Jennifer Gabrys at Goldsmiths, University of London.
If you’re interested in hearing more about our work or collaborating in this area, please do get in touch.
Data Infrastructure Literacy: Reshaping Practices of Measurement, Monitoring and Evidence
Jonathan Gray (University of Amsterdam); Carolin Gerlitz (University of Amsterdam); Liliana Bounegru (University of Groningen / University of Ghent).
A recent report from the UN makes the case for “global data literacy” in order to realise the opportunities afforded by the “data revolution.” In this context data literacy is characterised as a combination of information literacy, statistical literacy and technical skills, and reflects conceptions proposed by both practitioners and researchers working around this topic. We argue that such conceptions risk obscuring the methodological and analytical inscriptions or bias that data come with, and the particular forms of valuation which they might favour (e.g. auditorial and entrepreneurial). In response to these dominant conceptions of data literacy we advance an alternative conception of data infrastructure literacy. The conception of data infrastructure literacy that we propose draws attention to the need to account for the wider data infrastructures which create the socio-technical conditions for the creation, extraction and analysis of data. In shifting focus from datasets as raw materials to data as infrastructures we call for rethinking the action repertoires of data publics and their potential to challenge, reshape and reconfigure the composition of data infrastructures and of the techniques of measurement and monitoring inscribed in them. In order to advance this agenda, we propose a provisional framework for thinking about data infrastructure literacies and discuss a number of new collective practices and examples from activism, journalism and social research that have sought to challenge the constitution of existing data infrastructures and to reshape the techniques of measurement and monitoring central to the formation of evidence.