Reflections on the rise of transactional data in social research Mike Savage Sociology & CRESC University of Manchester

Intellectual context These reflections arise out of Editorship of The Sociological Review, one of the three core UK sociology journals since Directorship of CRESC, where I have developed an interests in how cultural institutions (DCMS, ACE, etc) actually do their research. My research on the history of popular identies in the UK , (funded by a Leverhulme Major Research Fellowship). CRESC interests in the politics of method, especially of (relatively neglected) social science methods Initial arguments in Mike Savage and Roger Burrows, The coming crisis of empirical sociology to be published in Sociology Sept 2007

Historical observations The two main research repertoires of social scientists, namely the national sample survey and the in-depth interview, gained precedence in the post-war years, and are now rather old. The intervening years have seen huge innovations in the generation of data and methods, yet academic social scientists dont seem to be centrally involved in these. In the 1950s, a special effort had to be made to collect social data, now such data is routinely produced as part of normal transactional processes, making the role of specially commissioned social research less clear. Nigel Thrifts conception of knowing capitalism allows us to recognise how transactional data is both routinely produced by, and also constructs, circuits of production, distribution, exchange and consumption. The role of the academic social scientist is thus thrown into question

Depth models in the social sciences In defining their identities and activities, academic social scientists often invoke depth models, implicit in positivist, realist, and hermeneutic methods Both the interview and the sample survey are championed as a means of delving into, and revealing, hidden social processes. Both allow inference, abstraction and the search for regularities, a causal social science in which particularities are subsumed to underlying forces Transactional data –works through surfaces using data on whole (sub-)populations. –is concerned not with revealing the hidden, but with arraying surface data in visible and accessible form. –It concerned with particularising, as much as generalising. –Is implicated in an audit and commercial neo-liberal climate. –Can be seen as part of descriptive turn.

The Descriptive turn Recent methodologists/ theorists seek to re-instate the discredited role of the descriptive in social research. Historian of science John Pickstone identifies four distinct ways of knowing (i) classificatory, (ii) analytical (iii) experimental and (iv) hermeneutic, and argues that (ii) should not be over-emphasised. US sociologist Andrew Abbott attacks conventional multi-variate analysis with its problematic assumptions of general linear reality in favour of descriptive methods. Social theorist Bruno Latour criticises the delineation of the deep social. Deleuze and Guattari on the immanence of the social, with links to chaos theory, etc (cf Delanda).

Networks The rise of the sample survey in the mid 20 th century depended on discrediting the field analysis approach, in which it was deemed essential to study whole populations (e.g. Tavistock Institute). Despite the early prominence of British network research in the 1950s (Barnes, Bott, Mitchell) this tradition faded, as it is not easily amenable to study using either sample surveys or in-depth interviews. SNA now increasingly championed by physicists (Barbarasi, Watts, etc). Transactional data allows the revival of network methods, where understanding the links between transactions, and not the attributes of the individual transactor becomes a central research issue. E.g. Amazon; Tesco loyalty cards; marketing research, etc

Visualisations Social science depth methods have historically involved abstracting from the visual either through prioritising numbers or narratives. Some theorists (Martin Jay) talk about the denigration of vision in the academic endeavour The reporting of transactional data routinely deploys hybrid mixes of text, number, and the visual in ways which mutually inter-relate. The visual, textual and numerical play off each other, and rely on a hermeneutic of accessibility and engagement Examples include network sociograms, web pages, maps, etc

Mapping…. Transactional data works through surfaces, and deploys primarily spatial and visual operators. Speaks to the older concerns of social scientists to use graphical and mapping methods. Pierre Bourdieus Distinction, which uses multiple correspondence methods, and draws on field theory, offer an example of how to interpret through a mapping exercise. Such methods can also be used to plot individuals uniquely. Following example is from Cultural Capital and Social Exclusion (Tony Bennett, myself, Elizabeth Silva and Alan Warde).

Table. MCA cloud of contributing modalities, axis 1 and 2.

Table. MCA cloud of individuals: preferences for classical music lit up, axis 1 and 2.

Whole populations? Transactional data collects data on whole populations within a system (Amazon customers, Tesco users), rather than a random sample. This (partly) limits the applicability of this data. Increasing capacity of brokers of to merge and link transactional data sets to allow comprehensive maps of whole populations to be conducted. The applicability of data capture methods The neighbourhood becomes the main site around which such assemblage takes place, feeding into a new politics of classification and belonging. We need to note the limits of the sample survey with its assumptions about bounded national units.

Temporality Longitudinal and panel data is increasingly central to definitions of social science methods (c.f. Abbott). How far does the spatial focus of transactional data means that temporality is difficult to incorporate into its mapping methods? The key arenas for the deployment of longitudinal surveys are in epidemiology and educational research. It is interesting how far this maps onto a public sector vs private sector divide. How far can transactional data be used in such research? Issues of private sector vs public sector domains clearly relevant here…..

Expertise… Bauman argues for the shift from legislative to interpretative intellectuals in post-modern times. Social scientists were historically central to the generation of specially commissioned social knowledge but they are now not the central innovators. Transactors (both consumers and producers) routinely create their own data, by passing the role of the social science expert (though not necessarily the finance or IT expert). Research on transactional data challenges established canons for social science expertise through By-passing the peer reviewed journal. Burying the procedures. Co-producing the researched Defying causality

Conclusions Academic social scientists get very caught up in their own internal disputes (between quant and qual, between disciplines, etc) and have not been attentive to the rise of new methods that offer a radically different form of social research Social scientists should not dismiss this new work as un scientific: it is highly scientific (for instance in its affiliation with the natural sciences), and speaks to important theoretical currents. Social scientists need to critically engage with transactional research,,e.g. by questioning its classifications, assumptions, procedures, etc We need to reflect on the politics of method in which academic social scientists do not enjoy a legislative position but are – at best – intermediaries between numerous agents. A focus on description could be a way of staging a debate between academic social scientists and work using transactional data.