Data, big data, open data and datalogical systems (Clough et al. 2015) are already, as David Beer has noted, ‘an established presence in our everyday cultural lives’ (2015:2) and this means that the material and embodied configurations of data are already normative and quotidian and novel and innovative. Much of my research over the last 4 years, supported by a range of ESRC [i], EPSRC [ii] and British Academy grants, has engaged with normative and everyday configurations of data – whether that is in terms of routine and mundane mediations, lived subjective experiences framed by datalogical systems and their obscure decision making processes, the relationship between the promises of data for infrastructural change and the realisation of this, or human interrogations of machines. While the scope and breadth of my research into data and datalogical systems is broad and diverse, what connects all of my research is a continued concern with how data and datalogical systems are not just reconceptualising epistemology and ontology more widely (see also Burrows and Savage 2014), but how they implicate us as researchers and reveal to us that our long-term methods of research are equally and always already subject to, and framed by, the very issues we purport, in the digital era, to be critiquing.
To rehash a familiar argument: if we conceive of technology in relation to social media, big data and data flow, the subsequent methods that epistemologically frame this are defined by that initial conception: web analytics, scraping and mining tools, mapping – tools that seek to make visible the power relations of the digital infrastructures but that actually generate those power relations in the act of making them visible (boyd and Crawford 2012). For the ESRC project where we have investigated risks and opportunities of social media for the UK Ministry of Defence (MoD), web analytic methods show us very clearly mundane and dull, repetitive mass media management of content. News headlines are retweeted effectively and broadly with limited discussion that is capturable by the scraping tools we use.
‘Social media ninjas’ are employed – as they are across the sectors – to manage reputational content and intervene into potentially controversial posts by the public, suggesting that the major concern is around reputation and branding. Reputational bots, on the other hand, are not currently used by the UK military partly because they contravene the public service remit of the military in the UK (and the digital by default & ‘transparency’ agendas). Despite this, they are nevertheless viewed with envy as an efficient means of disappearing controversy into the ‘noise’. Whether social media is thought of as data, ninjas or bots, the point is that all of these contribute to a conception of social media that is defined by the parameters of paid or free access in the form of analytics – Twitter, Facebook, Google – and the rhetoric of ‘waste’ data is reconfigured in a popular and academic imaginary of what social media is.
The digital tools have limited scope to tell us about perception and imagining of social media: they offer a blunt instrument that captures the flow of conversations, that maps emerging stories and details key agents in a system. Yet they produce aesthetically powerful data visualisations, that are affective and speak the ‘truth’ (Kennedy 2015). It is these data visualisations that circulate powerfully to our stakeholders regardless of the cautionary notes we offer around their limitations, malleability or partial representation. They feed into, but do not challenge, one of the central findings of our project to date: that social media is fundamentally mis-conceptualised as content and that controlling content is understood as the effective management of social media.
This conception is, of course, long-running, and follows a long trajectory of media scholarship and for the MoD, it enables a familiar approach to media, which is about managing stories and people, but also leads to familiar and cyclical rhetoric of plethora, speed, and ubiquity. It also, of course, feeds into a self-perpetuating cycle where expertise, truth and value are located away from the human (individual), and within the self-legitimating, dynamic and smart datalogical system. This (re)organises embodied practices and ontology in relation to the values of that datalogical system ultimately valuing them (only) in terms of their capability of generating data, and therefore positioning labour, embodied practices or processes as surplus to, or at least less important than, the system to which it speaks.
At another end of the spectrum, and in another research project, we have been engaging with data as materiality. Here we have disrupted and played with hackathons methods, that also have roots in community art practice, craft and art work, labour and making cultures (if we take a longer and more local view than Silicon Valley does). Here data is the central signifier of creativity and innovation within these events, but data is also disappeared as a political or even operational force for creativity and innovation. Indeed, the digital is constructed as offering particular affordances for ‘empowerment’, ‘creativity’ and ‘innovation’– the events are only possible because of digital technologies and their underpinning datasets, and this is written into the discursive signifiers of the events themselves [iii].
This means that data is both a discursive trope with particular affordances, and a material ‘condition’ of the events (Suchman 2007). At the same time, data centrally frames the material and embodied conditions of the events themselves but is simultaneously obscured through the infrastructure of the hackathon events where the emphasis is on creativity and innovation, embodied disruption and processes of labour. Here, then, there is an opportunity for data to be put ‘in its place’ through a range of process and practices.
And for us?
Participants of research projects are carefully invited along key demographic lines. They are chosen because of their particular value – they are women; they represent a certain set of disciplines; they have particular expertise; or they are the ultimate users of the new system that was sought. If we are to critique the politics of data, we also need to recognize how we are implicated here as digital researchers into the politics we purport to critique: we promote certain identity signifiers that aggregate and afford particular value in the formation of research. This means that the processes critiqued in relation to big data (see boyd and Crawford 2012) and data analytics (Kitchin 2014) where the ‘variables that have the most utility’ (Kitchin 2014, 101) are those most valued, are precisely those we are also employing in our own epistemological and ontological pursuits. Similarly just as big data effaces the power relations behind these processes of aggregation, so we write out these politics in our narration of the events (see also Bassett 2016, Sterne 2015).
In recognizing data in these ways (in thinking about the power relations of data) we can open up a different space where new demands of the digital can be made. But in relation to this, we also have to ask about our own complicity. Indeed, Caroline Bassett has recently critiqued our ‘post-digital desire to accept the presence of…technology as a given, but also to put it aside’ in the explanation of power (2016:23, see also Sterne 2015, Clough et al. 2015). Her comment locates data as already figured within discursive and material power relations (and vice versa) but routinely set aside in explanations of them. In the end, then, the politics of data that are revealed here as having much wider resonance beyond a datalogical system demand that we consider and question our own roles in the long-term normative constitution of data. This is necessary if we are to move beyond a simple acknowledgment of sociality-as-datalogical with its corresponding disempowerment of our own (human) value, and instead seek interventions that move us beyond our own complicity in being primarily and ultimately reconfigured through the values of that datalogical system.
Bassett, C. (2016, forthcoming) What Perec Was Looking For: Notes on Automation, the Everyday, and Ethical Writing. Unpublished paper: 1-27
Beer, D. (2015) Productive Measures: Culture and Measurement in the Context of Everyday Neoliberalism in Big Data and Society 2(1) DOI: 10.1177/2053951715578951 bds.sagepub.com
boyd, D & Crawford, K (2012) ‘Critical Questions for Big Data: Provocations for a cultural, technological and scholarly phenomenon in Information, Communication and Society 15 (5): 662-679
Burrows, R & Savage, M (2014) ‘After the Crisis? Big Data and the methodological challenges of empirical sociology’ Big Data and Society 1-6: DOI: 10.1177/2053951714540280 bds.sagepub.com
Clough, P; Gregory, K; Haber, B; Scannel, R. J (2015) The Datalogical Turn. Unpublished Article. 1-26 accessed at: https://www.academia.edu/5986819/The_Datalogical_Turn (accessed 2nd December 2015)
Kennedy, H (2015) Numbers as Standard: Keynote address for ECREA Digital Culture and Communication Workshop. Salzberg
Kitchin, R. (2014) The Data Revolution: Big Data, Open Data, Data Infrastructures and their Consequences. London. Sage
Sterne, J (2015) Inaugural Lecture for the Sussex Humanities Lab: Keynote Address. RSA London.
Suchman, L. (2007). Agencies in technology design: Feminist reconfigurations. Unpublished manuscript. Available in: http://goo.gl/tsTTyl
[iii] For example ‘Wreckshops’ with the evocation of objects or systems to be unpacked; ‘digital labs’ with the explicit reference to the digital; ‘hackathons’ with the reference to hacker culture and durability (see also Irani 2015, Leckart 2012, Coleman 2012, Marlow 2013 for histories of the hackathons).