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.
The chapter begins with an overview of distributed theories of cognition, focusing on anthropological, philosophical and mediatheoretical contributions. I find these theories of cognition compelling in general, but they are especially well suited as a conceptual backdrop from which to consider the cognitive elements of dashboards, since dashboards are more-or-less conscious attempts to extend cognition. I come to focus on the work of N. Katherine Hayles and what she has called the rise of the technological ‘cognitive nonconscious’ (briefly: things that do cognitive work that is not quite ‘thinking’).56 Hayles has done important work formally differentiating between different types of cognition with regard to recent technological developments. I aim to supplement and extend her work by stressing the importance of specific distributions and, especially, types of cognition – that is, all thought is not the same. I offer an empirically and historically informed discussion of cognition in hospital life, formatted through dashboards.
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.
Notes
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. Prigg, ‘iPM’.
9 9. Charles Arthur, ‘David Cameron Tests Real-Time Economic Data App on iPad’, The Guardian, 8 November 2012, https://www.theguardian.com/technology/2012/nov/08/david-cameron-tests-data-app.
10 10. Brookings Institution, ‘Janet Yellen’s Dashboard’, Brookings (blog), 10 June 2014, https://www.brookings.edu/interactives/janet-yellens-dashboard.
11 11. Alper Sarikaya et al., ‘What Do We Talk About When We Talk About Dashboards?’, IEEE Transactions on Visualization and Computer Graphics 25, no. 1 (January 2019).
12 12. Stephen Few, Information Dashboard Design: The Effective Visual Communication of Data. Cambridge, MA: O’Reilly Media, 2006, xi.
13 13. Steve Wexler, Jeffrey Shaffer and Andy Cotgreave, The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios (Hoboken, NJ: Wiley, 2017), xiv.
14 14. Of course, I do not presume that the dashboard is solely responsible for this idea.
15 15. Rob Kitchin, The Data Revolution: Big Data,
