Being with Data - Nathaniel Tkacz

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dashboard; worked with researchers in a London-based think tank while they ‘dashboarded’ a televised election debate; discussed dashboards with a team of ‘smart city’ public servants in Melbourne, Australia, as they pondered what kind of dashboard was best for their city’s needs. I studied civil servants in the UK’s Government Digital Service team as they rolled out almost 900 performance dashboards as part of a larger digital transformation strategy. On an individual level, I have burned through six wearable fitness devices while researching and writing this book, each with its own dashboard configuration, and I have tested other non-fitness-related apps that feature dashboards (personal finance, mood, app usage analytics etc.), while also undergoing training in the use of business analytics dashboards. I have kept a keen eye on how dashboards have spread, and I have paid close attention to new and interesting developments, such as the Dublin Dashboard, led by Rob Kitchin and his team of researchers. Eventually, I settled on a case involving the use of commercial dashboard software in the context of hospitals and another based in a situation room which monitors for natural hazards and disasters. I realized along the way that there was no ideal setting for studying dashboards, no case that could hold the burden of acting as an ideal. Although there were obvious similarities that enabled me to recognize the dashboard format as such,55 each empirical instance had its divergences and idiosyncrasies. In the wild, dashboards are put to all kinds of uses. My attempts to speak of the format in general, or to outline distinctive characteristics, is made with the recognition that there are always counterexamples and counter-tendencies. I settled on two cases partly because I had the opportunity to observe them in the most sustained way, but also because I consider each exemplary for particular aspects of the format that I wanted to cover in detail.

      Chapter 2, ‘Formatting Cognition’, unfolds through a consideration of dashboards in hospitals. The chapter is partly narrated through an interview with a hospital manager, but it also tells the story of Qlik, the business analytics provider I introduced earlier. I explore Qlik’s dashboards in greater detail, drawing on interviews with a number of Qlik employees, documents and observations gathered during visits to the company’s UK headquarters, and web materials, and weave this back into the context of hospital life where Qlik’s dashboards are used. I use this material, however, to explore the cognitive elements of dashboards (recalling that dashboards involve a ‘cognitive function’). Dashboards force us to think about data explicitly in relation to cognition and to consider data as cognitive actors.

      Chapter 3, ‘Formatting Data’, is set in an environmental hazard and disaster situation room in Brazil, where teams of specialists monitor weather-related data and make decisions about issuing warning reports. The chapter explores the composition of the room and what lies beyond it, from displays, infrastructures and specialists, to the types of cognition found within it, and it does so in order to advance an argument about the formatting of data. While a situation room is a very specific site, it is well suited to explore how data are used for monitoring in an ongoing way in the context of making decisions. I use this setting to draw out what is specific about ‘dashboarded’ data, and place this in dialogue with recent debates about the transformative role of data in the production of knowledge. Dashboarded data require a rethinking of how data relate to knowledge, facts and truth. The chapter makes three general claims about data formatted through dashboards. First, they participate in fundamentally ‘uncertain’ ways of knowing. While any data element may increase or decrease certainty, understood in terms of the capacity to make decisions, dashboard data are always in relation to other data and they are always in motion, part of a format in motion. Such motion and relationality, I suggest, produce a constitutive uncertainty that forms the basis of any possible decision-making to follow. Second, dashboarded data prioritize what I call ‘time-value’. It is not that data’s truth-value is abandoned; rather, these data are prioritized in terms of their temporality. Third, and following on, these data are selected, arranged, compared or disregarded according to their capacity to contribute to making decisions. Since time is often crucial to making decisions, time-value is important here as well, but a more general ‘decision-value’ is also observable. The chapter concludes with a discussion of situations and ‘situationness’, which I define as the general dynamics or time-spaces produced by data formatted through dashboards. I use this discussion to further explore the specificity of dashboarded data in contrast to other sites and ways of knowing.

      1 1. For overviews of data and capital(ism), see Jathan Sadowski, ‘When Data Is Capital: Datafication, Accumulation, and Extraction’, Big Data & Society 6, no. 1 (1 January 2019); María Soledad Segura and Silvio Waisbord, ‘Between Data Capitalism and Data Citizenship’, Television & New Media 20, no. 4 (13 March 2019); Nick Srnicek, Platform Capitalism (Cambridge: Polity, 2016); Shoshana Zuboff, The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power (London: Profile Books, 2019).

      2 2. See ‘The Enunciative Function’ in Michel Foucault, The Archaeology of Knowledge (New York, NY: Routledge, 2002/1969), 99–119.

      3 3. Karin Van Es and Mirko Tobias Schafer, eds., The Datafied Society: Studying Culture Through Data (Amsterdam: Amsterdam University Press, 2017); José van Dijck, ‘Datafication, Dataism and Dataveillance: Big Data between Scientific Paradigm and Ideology’, Surveillance & Society 12, no. 2 (9 May 2014); Viktor Mayer-Schonberger and Kenneth Cukier, Big Data: A Revolution That Will Transform How We Live, Work and Think (London: John Murray, 2013), 73–97.

      4 4. See Frank Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information (Cambridge, MA: Harvard University Press, 2015); Nick Seaver, ‘Algorithms as Culture: Some Tactics for the Ethnography of Algorithmic Systems’, Big Data & Society 4, no. 2 (1 December 2017); Taina Bucher, If … Then: Algorithmic Power and Politics (New York, NY: Oxford University Press, 2018).

      5 5. See ‘Datafication’ in Mayer-Schonberger and Cukier, Big Data, 73–97.

      6 6. It does not receive attention in any of the major historical studies of facts, numbers, statistics, political arithmetic or indeed, data. See Mary Poovey, A History of the Modern Fact: Problems of Knowledge in the Sciences of Wealth and Society, 2nd edition (Chicago, IL: University of Chicago Press, 1998); Alain Desrosières, The Politics of Large Numbers: A History of Statistical Reasoning (Cambridge, MA: Harvard University Press, 1998); Ian Hacking, The Emergence of Probability: A Philosophical Study of Early Ideas About Probability Induction and Statistical Inference, 2nd edition (Cambridge: Cambridge University Press, 2006); Theodore M. Porter, The Rise of Statistical Thinking, 1820–1900 (Princeton, NJ: Princeton University Press, 1986); Theodore M. Porter, Trust in Numbers: The Pursuit of Objectivity in Science and Public Life, new edition (Princeton, NJ: Princeton University Press, 1996); Daniel Rosenberg, ‘Data before the Fact’, in ‘Raw Data’ Is an Oxymoron, ed. Lisa Gitelman (Cambridge, MA: MIT Press, 2013).

      7 7. Mark Prigg, ‘The iPM: David Cameron Testing “Number 10 Dashboard” iPad App to Help Him Run the Country’, Mail Online, 8 November 2012, https://www.dailymail.co.uk/sciencetech/article-2229910/David-Cameron-testing-Number-10-Dashboard-iPad-app-help-run-country.html.

      8 8.

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