The politics of new data and the challenge of archiving

1. The politics of new data and the challenge of archiving www.cresc.ac.uk Mike Savage CRESC & Sociology University of Manchester

2. Issues • The coming crisis of empirical sociology… • Implications for the social science archive • Commodification • Visualisations and networks • Mapping • Temporality • Conclusions: the scope of archiving

3. The ‘coming crisis of empirical sociology’ The two main social sciences research repertoires - the national sample survey and the in-depth interview - gained (sudden) precedence in the 1950s, but are now rather old (and tired?). The intervening years have seen huge innovation in the generation of new data and methods of digital analysis, yet sociologists have not been centrally involved in these. In the 1950s, a special effort had to be made to collect ‘social’ data – which could then be archived - now such data is routinely produced as part of normal transactional – and non-archived - processes, making the role of specially commissioned social research less clear. Social scientists have not adequately responded to the challenge of these new data sources, preferring to stick to ‘tried and trusted’ data sources and methods…..

4. Implications for the social science archive? • Recent years have seen increasing interests in the archiving of social science data involving the deposit of originally collected survey and interview data • ESDS; Qualidata; Timescapes; . • Over the past two decades these archival resources have been increasingly mobilised by social scientists • The Surrey School’ of secondary data analysis from the early 1980s • Digitalisation of survey sources and their increasingly routine deployment by researchers. • Increasing – though still contested - interest in using archived qualitative data, partly as a result of the ‘re-use’ debate (Moore, CRESC) • Yet the ‘coming crisis’ poses the issue of how we archive new data sources, in a situation when informationalisation challenges the distinction between data and analysis.

5. Commodification • Traditional market research relies heavily on surveys (e.g TGI), but is now challenged by new digital data • E.g. Tesco ‘loyalty club’ data, analysed by Dunhumby which is • Molecular (we all have our own consumer DNA) • (Nearly) instantaneous • Incorporates the consumer into the research process through reward processes • Privately owned, not publicly archived, and rapidly outdated

6. Archiving visual networks • The usual archiving of social science data focuses on depositing data on individuals, with ethical concerns about ‘informed consent’. • Transactional data, however, focuses on the links between transactions. The attributes of the individual ‘transactor’ are not a central research issue. • The data becomes less significant than the procedures used to analyse the data. It is these which become socially significant, and which should be archived so that they can be evaluated by others (for instance, the role of neighbourhood classifications). • Consider the following example, developed by computer scientists and physicists.

9. Mapping…. • Transactional data works through surfaces, and deploys primarily spatial and visual operators. • Digitised maps are highly manipulable, and offer very specific details. Should these procedures also be archived….. • Following example is from Culture, Class, Distinction where we (Tony Bennett, Elizabeth Silva, Alan Warde, Modesto Gayo-Cal and Dave Wright) have used mapping methods (in the form of geometric data analysis) to lay out the ‘patterning of culture’ derived from a national survey

11. Axis 2 - 3.86 % 0.8 Hilda Joe 0.4 Seren Rachel Vasudev Caroline Robert Maria Majid Stafford James Axis 1 - 5.33 % 0 Jim Poppy Molly Margaret Jenny Rita Cherie Ruth Janet -0.4 Cecilia -0.8 Sally-Ann -0.8 -0.4 0 0.4 0.8 Cloud of Individuals – axes 1 and 2

12. Temporality and Sequencing • Archiving has historically been assumed to involve depositing data collected at specific moments in time • How does archiving deal with the routine ‘real time’ collection of data in which there is no central time point which justifies depositing at any one specific moment? • Are there lessons from computer science? How do we retain information permanently? Do we need to?

13. Conclusions • New forms of data challenge assumed notions of archiving which depend on differentiate the collecting from the deposition of data. • Informationalism might be said to conflate these processes and replace them with a politics of ‘data traces’. In this case, archiving loses its specificity • Archiving has traditionally not covered procedures of data analysis, yet it is these which are increasingly central to data processes. • Do we need to radically extend the scope of archiving as a means of allowing public contestation of modes of social research