Categories: Big Data
Last year The LSE Impact of Social Sciences blog saw sociologist, consulant and SAGE author Mark Carrigan interview a number of methods luminaries on the nature of ‘big data’ and the opportunities and challenges presented for scholarship with its growing influence in a series they called the Philosophy of Data Science Series. In this second interview with Deborah Lupton, they discuss what social theory brings to discussions of social data.
Ruppert is senior lecturer at Goldsmiths and the founding editor and editor of Big Data & Society with interests in the sociology of governance specifically in relation to how different kinds of data are constituted and mobilized to enact and manage populations. She has undertaken research on how different socio-technical methods and forms of data (censuses, administrative databases, surveys, transactions) organize and make possible particular ways of constituting and governing populations and how digital devices and data are reassembling social science methods. She has co-lead the Social Life of Methods theme and a series of projects on digital data and devices at the Centre for Research on Socio-cultural Change. She currently leads a collaboration, The Social Lives of Digital Data-Objects and a related ESRC funded project, Socialising Big Data.
You’re founding editor of the journal Big Data & Society which was launched by SAGE. What was the motivation for establishing the journal? What is its focus?
The initial motivation was the problematic way that ‘big data’ is being taken up and discussed in business, industry, government and the academy. From claims about the end of theory and raw data to assumptions about behaviour, we want to create a forum for critical and reflexive engagements with data and methods that address their social, cultural, economic and political ramifications. This is urgently needed in the face of expanding research and papers that reduce big data to questions of analytics, storage and computation. Instead of over generalisations about the promise or perils of big data we also want to create a space for studies of and experiments with Big Data in relation to specific situated contexts. Additionally, given the different skills and knowledge required to work with new data sources and analytics we especially see the need for a multidisciplinary space and one that can invite and involve non-academics – especially those in industry, government and civil society – into the debate.
We thoroughly debated whether or not to use the term big data, which I should note was the most scrutinised aspect of our journal proposal when it went out to peer-review. Some alternatives were proposed such as Data & Society, but after careful consideration we felt that this and other alternatives do not capture the major changes we see happening to our various relations to data whether as subjects, citizens or researchers. Additionally, instead of introducing an alternative, we decided to adopt the term exactly because it is popular and hyped and thus in need of critical interventions. We think that it is by engaging with the term that the journal can connect with and challenge how it is being defined and understood and to influence how its meaning is being constituted.
What is the significance of the ‘society’ in the journal’s title? Has this social dimension tended to be obscured in existing debates about big data?
While the term ‘societies’ also holds different meanings, we have adopted it because the stakes of Big Data are not just about new epistemologies and ontologies but about the consequences for how individuals, groups and social worlds are being done, sorted, known, and governed. You are thus right to note that one of the impetuses is that social as well as cultural, economic and political consequences are not being attended to as the focus is primarily on analytic and storage issues. We want to change that by providing a platform that doesn’t reduce big data to analytics, but extends our understanding it to all practices such as how data is generated and configured as a part of everyday digital practices to how it is curated, categorised, cleaned, accessed, analysed and acted upon. This is a much richer engagement, what you could call a data social science that analyses these practices and at the same time also engages empirically through experimenting with and innovating methods while reflecting on the consequences for how societies are represented (epistemologies), realised (ontologies) and governed (politics).
What, if anything, can social theory bring to these debates? Is there a need for a philosophy of data science? One of the things I’m keen to explore in this series is the potential practical implications of such a project – I worry that otherwise social theory risks becoming stuck in a critical mode, correctly critiquing the excesses of a renewed positivism but doing so in a way which has little to no purchase outside of its existing sphere of influence.
The ambition is that the journal will be a space for such debates and to be open to different approaches. For my part I come at this question from a poststructuralist understanding that theories and concepts do not precede but come out of our engagements with material, technological, and other relations. Mostly inspired by approaches in science and technology studies, for me this means that theory neither comes before or after but through specific inquiries and engagements with phenomena such as Big Data. This is not an externalist critique about Big Data that draws on or develops totalising concepts or theories of how it is transformative. Rather it is an approach that attends to the specificities of what we refer to as Big Data practices, their materialities and productivities and how they are reworking and mediating not only social and other relations, but also the very assumptions of social science theories and methods and how and what we know about those relations.
It is through analysing different practices and/or inventing and experimenting with methods that big theoretical claims about Big Data can be challenged and new concepts and ways of thinking opened up. Of course there will be other approaches taken up in the journal that will challenge or disagree with this. But the point is to not close down those differences but instead create a space for them to co-exist in tension but also possibly in conversation. The commentary section is an attempt to facilitate this through shorter and sometimes more polemical contributions submitted by researchers from different academic disciplines but also from industry and government.
I find the multidisciplinary focus of the journal intellectually exciting but wonder about the practical difficulties posed by the rather divergent intellectual traditions which could potentially find themselves represented in it. Is there a risk of people talking past each other? How can this be overcome?
This indeed will be a challenge and while we encourage interdisciplinarity we also recognize that in many cases we will have content that speaks to specific disciplinary approaches that, as you say, talk past each other. Be that as it may, one aspect of the journal that may facilitate such trans-disciplinary conversations and understanding is that its focus is a particular material thing – Big Data – which is a matter of concern across disciplines and about which there is no clear settlement or definition. We are at a moment of discovery, uncertainty and experimentation and it is at such times that I think there is greater openness to different possibilities and ways of thinking. The extent to which we can demonstrate and facilitate this is to be seen. At the same time I think it is important to be modest about what a journal can achieve!
In this series
Rob Kitchin |Big data should complement, Not Replace, small data, not replace them’
Deborah Lupton | Drawing on Liquid Metaphors for Big Data
Susan Halford | “Semantic web innovations are likely to have implications for us all”
Noortje Marres | Technology and culture are becoming more and more entangled.
Sabina Leonelli | “What constitutes trustworthy data changes across time and space”
Emma Uprichard | Most big data is social data – the analytics need serious interrogation