'Big data' is usually conceived as a way to generate knowledge by analyzing ever larger and 'messier' quantities of data. The rationality behind big data is often associated with centralized control and surveillance: Grab as much (if possible: all) data there is about a phenomenon and analyze it to discover patterns and predict future behavior. Not something one would easily associate with democratic values or citizen empowerment.
However, from a historical perspective it seems that big data is the latest expression of what can be described as the "two-faced nature of quantifying society". Porter illustrates this two-faced nature when he points out that the notion of "objectivity" is
evidently required for basic justice, honest government, and true knowledge. But an excess of it crushes individual subjects, demeans minority cultures, devalues artistic creativity, and discredits genuine democratic political participation. (Porter 1995, 3)
Fears over excessive objectivity seem to echo our modern-day critique on big data. We could re-articulate such fears with relation to data by asking: When data is used rhetorically as "that which is given prior to argument" (Rosenberg 2013, 36) -- as the 'factual' and indisputable basis for debate -- where is room for argument and debate when data is everywhere? On the other hand, Porter's observation also points out that "quantification was important for democratization", as Bernhard Rieder mentioned after his excellent presentation (thanks to him for pointing out Porter's book to me!). Since increased quantification can have negative and positive effects, we should not only criticize big data but also think about the conditions under which 'datafication' -- the ubiquitous quantification of social life underpinning big data (van Dijck 2014) -- can actually be good for democracy. Of course, this does not mean that critique is not important! My point is that we have to accept the fact that these technologies are here to stay. Thus, thinking about how to overcome the dangers of big data's modern-day practices and rationalities is valuable and important.
Looking at alternative data rationalities
I think a good starting point is to look at alternative approaches or rationalities around data that do not follow the categories and logics of big data. Therefore, I want to point out some presentations from the Social Media and the Transformation of Public Space conference that addressed alternative approaches to datafication:
- In my own presentation about the Open Data movement (you can get the slides here) I argued that datafication may not only lead to "big data rationalities", but also to a spread of values and practices from the Open Source culture. This idea is based on the observation that Open Data activists take key values and practices from Open Source and apply them to new domains outside the development of software (see also Kelty 2008). For example, 'raw data' is conceived as 'source code' that should be shared openly. For activists, this implies a slightly different role of journalism and a form of political participation that to some degree resembles the 'Bazaar model' of Open Source. Such a spread of Open Source culture could lead to a re-articulation of concepts like journalism, participation and democracy -- in ways that may not have seemed possible before.
- Helen Kennedy's presentation Making Analytics Public: really useful analytics and public engagement (you can find both hers and mine abstract here) asked whether and under which conditions (data) analytics can contribute to the public good. She argued that analytics need to become more public itself in three ways. First, both the data and the analytical tools should be available to the public to use. Second, instead of being proprietary and black-boxed analytics need to be open to public supervision in order to be scrutinized and debated. I think this point connects to the question whether public social media are a good idea. Moreover, Nick Couldry and Joseph Turow made a similar argument in a recently published article, warning that "the emerging culture of big data" may "erode democracy unless their hidden workings are made public and contested broadly" (2014, 1711). Thirdly, Helen argues that analytics should be rethought as a more participatory process, which means that they should not only be instruments in the hands of experts but means that offer new forms of representation "by which publics can come reflexively to know and constitute themselves in new ways". In other words, datafication and analytics can be thought of as means that offer publics new ways of constituting themselves, something that could empower citizens and serve a public good.
- Lonneke van der Velden's presentation Forensic devices for activism: on how activists use mobile device tracking for the production of public proof (abstract) explored how activists use the ubiquitous tracking of their activities for their own ends. She described InformaCam, a mobile phone application that can be used to store images or videos in two versions: one in which identifying meta-data (time, location etc.) is removed, and one in which it is preserved and in which one can even add information manually. This way, the application gives activists the means to produce public evidence without giving up their anonymity. On the notion of activism, I would also like to add Nafus' and Sherman's (2014) study about the Quantified Self Movement. They describe this movement as an alternative big data practice because activists appropriate the techniques and conceptions of big data while at the same time resist its rationality by emphasizing their status as individuals who do not fit into common categories. In Nafus' and Sherman's own words, they "appropriate big data’s attention to granular patterns, but resist the categories that are built into devices and into the market for data" (2014, 1791). Resembling Helen's arguments, the Quantified Self Movement asks "what it means to think of data 'as a mirror' and what kinds of reflection, learning, and personal insights might emerge" (Nafus and Sherman 2014, 1787).
We need more research
I think more research like this is necessary to explore what types of alternative rationalities around datafication are emerging -- outside the 'big data business'. Nick Couldry has called this type of research social analytics. That is,
the study of how social actors are themselves using analytics -- data measures of all kinds, including those they have developed or customized -- to meet their own ends, for example, by interpreting the world and their actions in new ways. (Couldry 2014, 892)
Whether datafication serves businesses and intelligence agencies more than democratic values and citizen empowerment depends on how data and analytics are utilized and distributed. Research on social analytics will help us to find out under which conditions it might be good for democracy.
Couldry, Nick. 2014. ‘Inaugural: A Necessary Disenchantment: Myth, Agency and Injustice in a Digital World’. The Sociological Review 62 (4): 880–97. https://doi.org/10.1111/1467-954X.12158.
Couldry, Nick, and Joseph Turow. 2014. ‘Advertising, Big Data and the Clearance of the Public Realm: Marketers’ New Approaches to the Content Subsidy’. International Journal of Communication 8: 1710–26. https://ijoc.org/index.php/ijoc/article/view/2166
van Dijck, José. 2014. ‘Datafication, Dataism and Dataveillance: Big Data between Scientific Paradigm and Ideology’. Surveillance & Society 12 (2): 197–208. https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/datafication
Kelty, Christopher M. 2008. Two Bits: The Cultural Significance of Free Software. Experimental Futures. Durham: Duke University Press. https://twobits.net/download/index.html.
Nafus, Dawn, and Jamie Sherman. 2014. ‘This One Does Not Go Up To 11: The Quantified Self Movement as an Alternative Big Data Practice’. International Journal of Communication 8: 1784–94. https://ijoc.org/index.php/ijoc/article/view/2170
Porter, Theodore M. 1995. Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton, N.J: Princeton University Press.
Rosenberg, Daniel. 2013. ‘Data before the Fact’. In ‘Raw Data’ Is an Oxymoron, edited by Lisa Gitelman, 15–40. Infrastructures Series. Cambridge, MA: The MIT Press.