Category: Contribution

Introduction: Data Politics Beyond Liberation and Protection?

“Data” has become an important keyword in contemporary life. It features prominently in many different visions of the future, as well as in relation to a wide range of practical tasks. Companies see data as a lucrative new asset class and as a resource for streamlining their operations and for providing new offerings. Politicians see data as an instrument of reform by enabling transparency, accountability, participation and innovation. Journalists see data as a means to source stories and enrich their reportage. Activists see data as both an issue in itself and as a resource for intervention concerning everything from corporate and governmental surveillance to climate change and migration. Data is envisaged to make money, strengthen democracies, aid investigations and enable justice. At the same time it has been subjected to numerous critiques. Data is also held to disrupt livelihoods, violate privacy, undermine democracies, deepen inequalities, distract from issues, and displace other forms of reasoning, sense-making and experience.

What are we to make of what appears concurrently as an almost magical object of attention and concern, as well an integral part of the mundane organisation of daily affairs? Data has become an object of study in numerous fields, and has even given rise to new fields and sub-fields such as “data studies” and “critical data studies” (see, e.g. Kitchin and Lauriault 2014; Iliadis and Russo 2016; Dalton, Taylor and Thatcher 2016). This article introduces the notion of “data worlds” and explores its relevance for studying, theorising and doing things with data. It draws on previous research on worlds, worlding and world-making in order to examine three aspects of data worlds as: (i) horizons of intelligibility, (ii) collective accomplishments, and (iii) transnational coordination. These three aspects are illustrated with examples from ongoing research on the politics of public data. The article concludes by reflecting on how the notion of data worlds might inform not only social and cultural research, but also inspire interventions and experimentation around the politics of data.

While using the concept of data worlds in my own research, I’ve been reviewing how others use it. The term has been mentioned in relation to topics such as “big and small data worlds” (Blok and Pedersen 2014), “data art” (Singer 2016), “test bed urbanism” (Halpern, LeCavalier, Calvillo and Pietsch 2013) and “thing ethnography” (Giaccardi, Cila, Speed and Caldwell 2016). Many of these brief references do not dwell on what is meant by the term. This article addresses the gap in this literature by unpacking the notion of data worlds and suggesting three closely related ways in which it can be understood. Before looking at these in more depth I will briefly say a bit more about why and how the concept of data worlds may be useful in relation to studying the politics of data, and why I focus on the particular set of ideas about worlds, worlding and world-making that informs the discussion below.

The notion of data worlds is used in my work in order to look beyond data as a “representational resource”, to consider the various forms of epistemic, social and political work that it does and which is done to produce it. The representational conception is evident in both implicit metaphors and explicit models for talking about and doing things with data. While data does indeed designate aspects of situations, it could also do other things, such as shape the way we see and think about things, serve as a common point of connection across situations, and help to conventionalise ways of organising the world.

The notion of data worlds is thus partly a response to contemporary “socio-technical imaginaries” (Jasanoff and Kim 2015) about data. Just as industrial technologies of the past were accompanied by new social, cultural and political imaginaries, so we can trace the ascent of “data imaginaries” and “data speak”: visions and rhetoric concerning the role of data in society. As Gillespie notes in relation to platforms, these imaginaries do “discursive work” (2010). For example, data is framed as “the new oil”, “the new gold” or “the new soil”, in order to emphasise its value as a social or economic resource. We also see the idea of “infrastructures” of data being used in order to emphasise different configurations of public-private and state-citizen collaboration, as well as to establish information infrastructures as a basic good in society alongside infrastructures for water, gas, electricity and so on. The platform, the portal, the app, the lab and the hackday give rise to new imaginaries and discursive regimes as well as material practices suggesting the role of data in public life.

Many of these imaginaries focus on the value to be extracted from data, through various mechanisms and arrangements to make data public. The issue is often framed as one of access, formats and conventions for encouraging the re-use of public data in innovative applications and services. I have found the notion of data worlds to be useful in examining what open data initiatives do and do not do, and how they might be done differently (Gray 2018). For example, open data projects may focus on redistributing access to data about public finances without substantively engaging with the epistemic, social and political work of data infrastructures in selecting, translating, arranging and articulating certain aspects of fiscal policy (such as detailed spending estimates of local councils), but not others (such as the economic activities and tax payments of multinational corporations).

The notion of data worlds is intended to gesture beyond two prominent forms of data politics which can be broadly characterised as “data liberation” and “data protection”. Both emphasise dynamics of power related to access. Data liberation is widely associated with hacker culture and other forms of information activism: setting information free from institutions and corporations as a means to address information asymmetries, and to provide a resource for activism and social change. This may be considered in terms of a “Promethean” mythology of broadening access to a powerful resource or instrument (just as Prometheus stole fire from the gods to give to humankind) – an outlook which is shared across the spectrum from the “mega-leaks” of Wikileaks and the Panama Papers and the more curated, selective leaks of Edward Snowden, through to “Freedom of Information” (FOI) and access to information movements in the 1990s, as well as official and grassroots open data initiatives which emphasise making data legally and technically amenable to re-use (Gray 2016).

On the flipside, we have “data protection” as a narrative of information politics which emphasises the protection of personal information from state, corporate and other actors – as is exemplified in the work of civil society groups such as Privacy International and the Electronic Frontier Foundation. These narratives place an emphasis on the individual ownership and control of personal data, as well as on preventing, obstructing, managing, regulating and raising awareness of the collection of personally identifying information – from artistic projects to make visible the personal information different actors have collected, to law and policy (such as EU Data Protection rules or US Fair Information Principles).

Whilst these two genres of information policy and information politics are indeed vital, data infrastructures do much more than making data public and making data private. Raymond Geuss has critiqued what he considers the disproportionate attention accorded to the “public/private distinction” which both reflects and reinforces the absence of “any effective general framework for thinking about politics apart from liberalism” (Geuss 2003). Dominic Boyer has suggested the phrase “digital liberalism” as an invitation to attend to how “techno-institutional processes such as computerization and digital information and politico-institutional discourses of late liberalism have coevolved, at times reinforcing and naturalizing each other, promoting novel bundles of epistemics and ethics” (Boyer 2013).

The notion of data worlds is intended to make space for thinking about data as more than simply a representational resource, and the politics of data as more than a matter of liberation and protection. It is intended to encourage exploration of the performative capacities of data infrastructures: what they do and could do differently, and how they are done and could be done differently. This includes consideration of, as Geoffrey Bowker puts it, “the ways in which our social, cultural and political values are braided into the wires, coded into the applications and built into the databases which are so much a part of our daily lives” (2014). In doing so we may draw on performative analyses of numbers (Espeland and Stevens 2008; Verran 2015), models (Mackenzie 2008) and methods (Law, Ruppert and Savage 2011) to consider how data infrastructures may be involved in not just the representation but also the articulation of collective life, while at the same time being the products of social and institutional work themselves.

Many accounts of performativity allude to the work of J. L. Austin, who suggests “the issuing of an utterance is the performing of an action” (1975: 6). Austin is associated with a “linguistic turn” in Anglophone analytic philosophy said to begin with Wittgenstein, whose later work reflects on what language does beyond referring to things. In the following discussion of the performative and world-making capacities of data infrastructures, I draw on an earlier linguistic turn that occurred in German philosophy in the eighteenth century and which has recently begun to receive more attention in English-language scholarship (Lifschitz 2012; Bowie 2013; Taylor 2016). Thinkers associated with this earlier turn also sought to look beyond representational accounts of language towards its other capacities as a situated set of social practices. Ian Hacking argues this tradition can be viewed as an alternative to Wittgenstein’s “depoliticized” philosophy of language (2002). I do not argue for the special relevance of this period and these ideas. Rather I suggest that it contains conceptual and theoretical resources which may be useful when considering different aspects of worlds, worlding and world-making in relation to data.

The three aspects of data worlds which I examine below are not intended to be comprehensive, but illustrative of what is involved in data infrastructures, what they do, and how they are put to work. As I shall return to in the conclusion, this outline is intended to open up space for not only thinking about data differently, but also doing things with data differently. The test of these three aspects is therefore not only their analytical purchase, but also their practical utility.

1. Data Worlds as Horizons of Intelligibility

The first aspect of data worlds draws on philosophical ideas about worlds, worlding and world-making to look at how things are sayable, knowable, intelligible and experiencable through data. In European philosophy this begins with Kant’s “Copernican revolution” which recognises the active and creative role that human beings played in composing the worlds that they experience – including through schemes, categories and structures such as space, time, causality and quantity which give form to experience. This is an explicit departure from views which saw experience as “given” and immediate, and also heralds a broader philosophical shift towards looking at how experience is articulated and mediated through language, culture and social arrangements.

Subsequent thinkers in this tradition –Hamann and Herder in the eighteenth century to thinkers as diverse as Heidegger, Gadamer, Benjamin and Wittgenstein in the twentieth century – stripped Kant’s project of its aspiration to clarify universal structures, and highlighted the role of socially and historically situated linguistic and cultural infrastructures, or what the contemporary philosopher Charles Taylor calls “meaningful media” (Taylor 1985), in shaping our apprehension of the worlds we inhabit.

Many of these earlier thinkers mainly focused on the role of language as a horizon of intelligibility, providing the “conditions of possibility” for our experience. As Taylor notes, this also corresponded with an explicit move away from a dominant focus on the designative, representational and “information encoding” capacities of language and other meaningful media – and a focus on their role in composing and co-producing our worlds of experience (2016). As Hacking puts it, in this tradition we find the notion that: “language is creative; to it we owe the existences and structures that populate our world-versions” (2002, 139). And yet, while there is a focus on language as an important and paradigmatic case of how our experience is formed, language is very often construed in a broad sense – including not only written and verbal language, but also music, painting, sculpture, and other social and cultural conventions for making meaning.

Benjamin draws on Hamann’s “metacritical” challenge towards narrower conceptions of experience as “naked, primitive, self-evident” (Benjamin 1996), exploring in his work the world-making capacities of architecture, urban planning, fashion, advertising and technologies, perhaps most famously in his Arcades Project (Benjamin 1999). Later in the twentieth century, these kinds of appropriations of Kantian ideas about schematism and world-making (minus the transcendental idealist baggage), have broadened out from what Apel calls the “linguistic a priori” of thinkers like Hamann, Herder and Heidegger (Apel 1973, 39), to the “historical a priori” of Michel Foucault (Foucault 1972) and what has been called the “technological a priori” of German media theorists shifting the focus to Kulturtechniken or “cultural techniques” (Tuschling 2016; Winthrop-Young, Iurascu and Parikka 2013).

What might this sense of world-making bring to an understanding of the politics of data? Taking a cue from this theoretical constellation, we might envisage data worlding in terms of a contingent, historically and socially situated, technologically mediated “data a priori” which not only designates but also provides the conditions of possibility for seeing and engaging with different aspects of collective life – making possible particular styles of reasoning and particular forms of knowledge and experience.

Data practices might be understood not just in terms of more sophisticated ways of picking things out, but as contributing to new ways of making things up, as Hacking puts it (1985). Here critical data scholars can benefit from decades of research on social practices of quantification (Porter 1996; Espeland and Stevens 2008; Rottenburg, Merry, Park and Mugler 2015; Bruno, Jany-Catrice and Touchelay 2016); statistics (Porter 1986; Desrosières 2002); standards (Lampland and Star 2008); probability (Hacking 1990); visual reasoning (Halpern 2015); and other studies of cultures and practices of knowledge which focus not just on what is said, but on the background against which things become sayable. In looking at how data worlds provide horizons of intelligibility we can both draw on genealogies of the modes of experience and styles of reasoning which are rendered possible through data over previous decades and centuries, as well as looking at what is distinctive about new and emerging digital technologies. As Nelson Goodman puts it in his classic Ways of Worldmaking: “worldmaking as we know it always starts from worlds already on hand; the making is a remaking” (Goodman 1978, 6).

Figure 1: Screenshot of transportation maps from Mapnificent project (mapnificent.net), showing which places are accessible in a given amount of time from a given point.

Thus in relation to digital data worlds we may examine how composites of conventions, norms, technologies, practices, methods, pieces of software, graphical user interfaces, data standards, data formats and aesthetic approaches are implicated in making things up and making things intelligible with data. This might include looking at how horizons of intelligibility change from pre-digital to digital data worlds. For example, we might look at differences in how the world is conceptually organised or “carved up” into categories. In contrast to the classificatory practices of statisticians taking measure of economies or populations, “born digital” and big data, generated as a result of interactions with online platforms, can give rise to novel practices of semi-automated classification, as well as emerging forms of inequality and discrimination.

Figure 2: Screenshot of interactive “animated bubble charts” of Gapminder project (gapminder.org), exploring relations between average life expectancy and income per capita over time for countries around the world.

There are many historical studies looking at how social categories are articulated through statistical practices (Desrosières 2002). In digital data worlds computational techniques such as machine learning may be used to facilitate “class discovery”. Clusters and orderings of hashtags, links, likes, images and other media can be viewed as co-produced by the logic of platforms, algorithms, and the “device cultures” of users. For example, Sam Lavigne’s “Taxonomy of Humans According to Twitter” at The New Inquiry explores and visually represents the “bizarre rubrics Twitter uses to render its users legible” (Lavigne 2017). This project aims to make visible the way in which people are classified according to a combination of user activity and information from data-brokerage companies, leading to categories such as “people who live with three other people”, “buyers of frozen ethnic foods”, and “households whose behavior indicates they are spa mavens”. These algorithmically-mediated data practices around online platforms can be understood, as Annemarie Mol puts it, as “new ways of doing reality” (Mol 1999).

We might also look at the forms of experience, styles of reasoning, and genres of sociality that arise with novel kinds of cultural objects associated with digital data worlds. This includes the world-making capacities of things such as apps, platforms, software packages, code libraries, and data analysis and visualisation tools through which people make sense with data, and integrate data into different kinds of social processes, practices and institutions. We might consider how space, time, relations and categories are articulated and organised through lists, tables, charts, timelines, maps and coordinate systems – and inscribed into dashboards, interactive data visualisations, word clouds, network graphs, mapping technologies, and computational techniques for filtering, reconciling and analysing data.

Just as Scott talks of “seeing like a state” by reducing “an infinite array of detail to a set of categories that will facilitate summary descriptions, comparisons, and aggregation” (1999, 77), and Law talks of “seeing like a survey” by using statistical methods to enact “a very particular version of the collective” (2009), so we may consider how the performative and world-making capacities of data projects are conventionalised into familiar forms such as seeing like an app, a network graph, a data portal, an API, an interactive map, a Google Spreadsheet and so on.

Figure 3: Detail of dashboard previews from London Datastore (data.london.gov.uk) showing trends in relation to performance indicators for the city.

“Time travel” maps articulate novel and interactive relationships between space and time by estimating the zones that can be reached from a given point in a given time interval (Figure 1). Global indicators are no longer simply represented through tables, charts or line graphs, but through interactive animated graphics dramatizing the passage of centuries through the movements of multicoloured bubbles articulating different dimensions of collective life (Figure 2). Interactive dashboards are envisaged as the preferred mode to increase transparency and public accountability in the city by tracking trends in relation to key performance indicators (Figure 3). While these kinds of projects draw on ideals and practices that have much longer histories – such as the aspiration for what Theodore Porter characterises as “thin descriptions” and an aesthetic of distance – digital technologies are also facilitating reconfigurations and redistributions of these data world-making capacities, leading to emerging genres of making sense with data. As we shall see in the following section, these meaning-making practices should be understood as social conventions.

2. Data Worlds as Collective Accomplishments

The second aspect of data worlds draws on a sociological tradition of studying “social worlds”. Adele Clarke and Susan Leigh Star trace this from the Chicago School of Sociology to Science and Technology Studies (Clarke and Star 2008). This approach encompasses and informs a range of research on social worlds – including for example Anselm Strauss, who suggests in the 1970s that we should look at the “social worlds” of genetics, high energy physics, computerisation and banking (Strauss 1978), to Howard Becker’s renowned work on “art worlds” (Becker 1984), as well as the “worlds of classification” and “information worlds” explored in the work of Bowker, Star and other scholars of information infrastructures (Bowker and Star 2000; Star, Bowker and Neumann 1997).

This view of social world-making is also commensurate with both critics and radical interpreters of Kant who suggest that language and meaning-making practices should be regarded in fundamentally social and historical terms – a move which led Ian Hacking to mark this as a key moment when language “goes public” (2002). This tendency to look at language and meaning-making practices in terms of contingent and evolving social institutions is also present in Wittgenstein’s work, which is a formative influence on subsequent social research agendas from ethnomethodology, to the “Strong Programme”, to Science and Technology Studies (see, e.g. Bloor 1983, 2002; Hacking 1984; Lynch 1992).

Taking a cue from this tradition, we might look at how the information products, styles of reasoning, and meaning-making capacities associated with data infrastructures can be considered as “relational achievements” or “distributed accomplishments” – and how the collectives associated with data infrastructures are changing in composition. Data worlds as horizons of intelligibility must thus be understood as social and collective. Changes in these collectives can carry significant political and political-economic consequences. For example, in the case of the redistribution of “data work” from official institutions to actors outside the public sector – as in the case of open data initiatives (Gray 2018), to civil society and citizen generated data (Gray, Lämmerhirt and Bounegru, 2016), through a shift of emphasis from statistical data to “big data” generated by major technology companies (Flyverbom, Madsen and Rasche 2017).

In Howard Becker’s terms, we can examine the “conventions” and practices which hold these social “data worlds” together – which he characterises as “ways of seeing and hearing that were known by everyone involved and thus formed the basis for their collective action” (Becker 1984, xv). In the case of open data, this might include, for example, such things as open licensing practices, legal arrangements, and technical practices which aim to “unlock the potential” of data as a resource, and “reduce the barriers” to its re-use by non-state actors – whether in new technology products such as Google Maps, the stories of data journalists, or the campaigns of NGOs or civil society groups. This concern with legal and technical conventions suggests that the open data community might be understood as what Chris Kelty calls a “recursive public”, or “a public that is vitally concerned with the material and practical maintenance and modification of the technical, legal, practical, and conceptual means of its own existence as a public” (Kelty 2005, 3). There are also emerging conventions for making sense with data such as those discussed in the previous section.

Looking at data worlds as collective accomplishments includes recognising the role of actors whose contributions may otherwise be under-recognised. In his work on the sociology of “art worlds” Becker suggests a shift of emphasis from the formal quality of art works to “complex networks through which art happens” (1984, 1). In his work he describes a broad range of materials, formats, spaces, instruments, distribution networks and art workers which are involved in the production and distribution of art works, and the assembly of their publics. Hence we might survey not just the formal properties of data projects or practices of prominent data workers (such as data scientists or data journalists), but a much broader cast of characters who are involved in the production, circulation and reception of data work.

Similar moves will be familiar from approaches inspired by Science and Technology Studies which view data infrastructures as relations of people, machines, software, standards, processes, practices, and cultures of knowledge production (e.g. Bowker and Star 1998, 2000; Star 1999; Star and Bowker 2002; Star and Ruhleder 1996; Jackson, Edwards, Bowker and Knobel 2007). Susan Leigh Star and Geoffrey Bowker suggest the notion of “infrastructural inversion” to bring neglected actors and processes into the foreground, including the role of non-human actors. In other recent work this has been framed in terms of “data assemblages” (see, e.g., Kitchin and Lauriault 2014).

One notable feature of many aspects of contemporary data politics is the emphasis on redistributing different forms of data work through digital technologies and networks. This redistribution comes in many different flavours. The tendency to redistribute “data work” from the public sector to the private sector is reflected in what Joseph Stiglitz calls the “default position” in information policy in the US, which is that states should not attempt what can be more effectively delivered by markets. This sentiment is also echoed in an influential paper called “Government Data and the Invisible Hand”, suggesting that states cannot “keep pace” with the internet. This paper is picked up by Tim O’Reilly with his idea of “government as a platform” (which he opposed to “vending machine government”), an idea which was institutionalised as part of government policy in the UK (Gray 2014). Since the turn of the millennium, public information policy has seen an influx of different ideas concerning how and why to redistribute public data work – from enabling new kinds of innovation and businesses, reducing public sector costs, to crowdsourcing, distributed collaboration or peer production around data (modelled on open source software development), to grassroots, bottom up and participatory data cultures.

The redistribution of data worlds can be facilitated through a variety of devices and conventions, such as open licenses (like Creative Commons licenses); data formats such as Google Transit Feed Specification (later renamed General Transit Feed Specification); online platforms such as GitHub; data portals (such as data.gov); as well as hackathons, fellowships, and other public engagement activities. We may consider these not only as “transparency devices” (Barry 2010), but also as “infrastructuring devices” (Star and Bowker 2002; Pipek and Wulf 2009; Björgvinsson, Ehn and Hillgren 2010; Karasti 2014; Le Dantec and DiSalvo 2013), assembling different publics around data, whether it is to clean it up, crowdsource quality control of bus stop locations, monitor potholes, or make new apps and services. How these different forms of publicity, participation and contribution are materially organised is an important question which can be read in relation to recent research on the politics of openness and participation (Tkacz 2014) and of platforms, platformisation and platform capitalism (Helmond 2015; Srnicek 2016).

3. Data Worlds as Transnational Coordination

A third aspect of data worlds is world-making as transnational coordination, which includes projects of shaping, governing and articulating transnational relations, from empires and international institution building, to the networks, circuits and tendencies which are often studied by sociologists of globalisation (Sassen 2006).

Through this lens we can look at the world-making ambitions of legal and technical norms, standardisation, harmonisation and interoperability processes undertaken by a wide variety of different actors in the service of different projects for making things global. For example, UN bodies and EU statistical agencies have undertaken extensive programmes of work to align national forms of quantification – to support transnational policy coordination and comparison. Intergovernmental actors and international organisations such as the IMF, the World Bank and the UN, have long supported the creation and alignment of systems and standards for the management of public finances in order to support objectives such as “fiscal discipline”, the allocation and coordination of development funds, and the comparability of public spending across borders.

It is not only public institutions which share these kinds of world-shaping ambitions by means of data. They are accompanied by a host of researchers, companies, statisticians, consultants, analysts, accountants, scientists, activists, technologists, managers, journalists, ecologists, librarians, and others who seek to establish transnational information systems, practices, norms and standards. This may range from professional standards bodies (such as the International Accounting Standards Board), to multinational consultancies (such as Deloitte and other “big four” accounting firms), to private technology companies or startups (big tech companies such as Google to smaller projects like OpenCorporates), to non-profit and civil society initiatives (such as the Open Contracting Partnership’s work on procurement data or Data2X’s work on gender data).

Such initiatives often aim to shape the world through the coordination of data. Data worlds can make things amenable to measurement, monitoring, evaluation, analysis, and visualisation across space and time in support of diverse political, geo-political, eco-political or political economic programmes – from neoliberal fiscal policy to market creation, gender equality to tax justice, increasing biodiversity to strengthening democracy. Civil society interventions to create and shape global data can be read in terms of other recent work around the history and sociology of quantification, as well as in terms of what some researchers have called “statactivism”, and, more recently, “data activism” (Bruno, Didier and Vitale, 2014; Milan and van der Velden 2016). Longstanding information infrastructure projects, such as Amnesty International’s “Urgent Actions” database, can be viewed as a kind of transnational “issue work”, in order to render what might otherwise be disconnected incidents amenable to classification, measurement, comparison and virtual witnessing across borders.

Figure 4. “Urgent Actions Visualised” from Amnesty International’s Decoders project. http://decoders.amnesty.org/projects/decode-urgent-actions/results

There are of course many ways in which a given issue or matter of concern may be articulated, defined, parameterised, quantified, and given life through data. The transnational coordination aspect of data world-making is multivalent and may be considered in relation to a wide variety of utopian and dystopian, progressive and regressive political and ecological projects, as well as in terms of different histories and conceptions of land, territory, empire and earth. Here we may benefit from previous research on the colonial aspects of worlding in literary and cultural studies (e.g. Spivak 1985; Karagiannis and Wagner 2007; Clark, Finlay, and Kelly 2017); post-colonial computing (e.g. Irani, Vertesi, Dourish, Philip and Grinter, 2010); “planetary-scale computation” and emerging “technogeographies” (Bratton 2016; Gabrys 2016); as well as the surge of interest around “global intellectual history” and the making of worlds (e.g. Bell 2013).

We may consider data worlds to facilitate the demarcation and shaping of spaces, territories, environments, categories, identities and boundaries, separating interior from exterior, and sorting things, people and places out. They may also direct attention to different kinds of transnational issues, dynamics, concerns or collectives. For example, we may look at the role of data worlds in relation to notions of the Anthropocene and the Capitalocene, in order to look at the role of human activity on a geological scale, as well as in the service of anthropologies of modernity, and projects to, as Bruno Latour puts it, “recompose a common world” (Kunkel 2017; Haraway 2016; Latour 2013). As well as deploying data worlds in order to better understand how human activity shapes the earth, information infrastructures may also be used to attempt to take various ecological signals into account in collectively redirecting its trajectory. As Goodman puts it: “if there is one world, it embraces a multiplicity of contrasting aspects; if there are many worlds, the collection of them all is one” (1978, 2). Data infrastructures can be used to establish the material character and limits of our one earth which contains such a plurality of social worlds and world-versions.

Data worlds can thus be understood as a means to institutionalise different forms of transnational coordination by providing the background against which things become seeable, sayable and doable with data across borders. Following recent research on neoliberal programmes (Roberts 2011; Davies 2014), data worlds may be considered as part of projects for reconfiguring relations between states, markets, citizens and civil society by foregrounding rankings, ratings and regimes of valuation in order to reinforce ideas of performance, competition and innovation, at the same time as moving tenets of economic and fiscal policy outside the realm of public and political deliberation. We can also read the redistributions of various forms of data work in terms of these contemporary imaginaries of democracy, markets and information – including those of competition, accountability, transparency, innovation, self-optimising systems and specific configurations of centralised management and decision-making coupled with decentralised delivery and contribution.

While there are indeed data worlds which may be configured to accelerate marketisation, bureaucratisation and what Habermas characterises as the “colonisation of the lifeworld”; other projects seek to address inequality and injustice, or to hold powerful elites accountable (as emphasised by the “statactivism” tradition), and all else in between. Data worlds can be malleable and may have unexpected consequences – such as in the cases of reports for investors being used by journalists and activists, or data from international development organisations being used by credit agencies.

Conclusion: Other Data Worlds Are Possible?

The aspects of data worlds described above are intended to gesture beyond two prominent narratives of data politics: of Promethean conceptions of liberating data as a resource on the one hand, and Orwellian visions of data surveillance, privacy and data protection on the other. These are vital parts of contemporary information politics, but there are other important aspects of what data is and what data does that should not be overlooked.

This article explores how theoretical traditions and literatures about worlds, worlding and world-making may be brought to bear to suggest different ways of thinking about data politics, highlighting three closely related aspects of data worlds. These three aspects are intended to be illustrative not exhaustive, and are intended as overlapping rather than distinct lenses through which to consider data infrastructures. They give rise to three different but closely related sets of questions for researching, theorising and reflecting on different aspects of data worlds.

1. Data worlds as horizons of intelligibility: What are the epistemic world-making capacities of data infrastructures? How might data infrastructures be involved in “making things up”? Can they provide conditions of possibility for different ways of seeing, saying and knowing collective life, and if so, how?
2. Data worlds as collective accomplishments: Who and what is involved with making, and making sense with, data? How are data worlds being redistributed through digital technologies? Who is (and who isn’t) able to shape data worlds? What kinds of practices of participation and public involvement are emerging around data worlds?
3. Data worlds as transnational coordination: How might data infrastructures be implicated in different attempts to “make things global”? What kinds of transnational alliances and circuits are being formed and to what end? Who advocates which kinds of data worlds, and according to which kinds of visions and fields of transnational coordination (from international relations to earth science)?

It is worth noting that it remains an empirical question as to how and to what extent data infrastructures are involved in world-making in these three senses. Data infrastructures can be deployed with certain epistemic, social and political aspirations and imaginaries in mind which they do not live up to. Data projects can fail to become data worlds in these three senses.

The notion of data worlds is not just intended to advance research on data politics. Following recent debates about the performativity of critique (e.g. Latour 2004), and calls to integrate critical, theoretical and humanistic reflection into technical practice (e.g. Agre 1997; Rieder and Röhle 2012; Berry 2014), I am particularly interested in how the notion of data worlds might suggest different kinds of data politics. Of course, theory and critique can contribute to doing things differently, as critical data studies researchers have pointed out. Dalton, Taylor and Thatcher, for example, propose to “develop alternative knowledges that reflect and build on our criticisms” (2016).

To this end, I’d like to propose the notion of “critical data practice” as a site for pedagogical experimentation, research and intervention around the politics of data. This follows Agre’s notion of “critical technical practice” which he uses to characterise his attempts to integrate historical and theoretical reflection around artificial intelligence into his work as an AI researcher (Agre 1997). The crucial question here is what difference critical studies can make in doing things with data. As well as contributing to critical genealogies and sociologies of the politics of “data worlds” and “data world-making” projects, researchers and universities might contribute to “making space” for such experimentation and intervention around public data infrastructures and the role they play in collective life.

The three aspects of data worlds I have examined are intended to assist with the task of rethinking the politics of public data, by considering how and for whom it is made public. Thus we may examine the organisation of what Evelyn Ruppert calls “data publics” (Ruppert 2015) beyond a focus on accessing, liberating and using data, and taking a broader look at how different actors engage with, mobilise around, shape and are shaped by, public data infrastructures. This includes distributed collaboration around different kinds of “data work” – from projects inspired by free software, free culture and open access movements such as Open Street Map or Wikidata, to data journalism and data activism projects for counting police killings or migrant deaths, to other kinds of civil society interventions for changing the socio-technical arrangements by which public institutions account for issues by means of data. As well as attending to these arrangements, researchers may also consider “experiments in participation” (Lezaun, Marres and Tironi 2016; Marres 2012) around data worlds, which are also cognisant of patterning and politics of these participatory processes. Such experimentation would not just aim to interpret data worlds, but also to question them, to re-imagine them, and to change them.

Acknowledgements

I’d like to thank Frank Pasquale, Will Davies, Liz McFall, Daniel Wilson and other participants at a workshop on “Outnumbered! Statistics, Data and the Public Interest” at the Centre for Research in the Arts, Social Sciences and Humanities (CRASSH), University of Cambridge in June 2017 for their useful feedback on an earlier version of this paper. It has also benefitted from feedback and input from colleagues in talks and workshops at King’s College London, the University of Amsterdam, the Politecnico di Milano, the University of Westminster, the University of Siegen and the Université Paris Nanterre. I’m also most grateful for time, suggestions and encouragement from Claudia Aradau, Liliana Bounegru, Carolin Gerlitz, Noorje Marres, John Durham Peters and two anonymous reviewers. Parts of the research for this article were made possible by a Starting Grant of the European Research Council (639379-DATACTIVE, https://data-activism.net) at the University of Amsterdam, where I was a postdoctoral researcher (2015-2016) and continue as a Research Associate; as well as through a Prize Fellowship at the Institute for Policy Research, University of Bath (2016-2017).

Introduction

It is now widely accepted that data are oiling the twenty-first century (Toonders 2014). Data gathering and tracking are practically universal, and datafication (the quantification of aspects of life previously experienced in qualitative, non-numeric form, such as communication, relationships, health and fitness, transport and mobility, democratic participation, leisure and consumption) is a transformation disrupting the social world in all its forms (Couldry 2016). Statistics confirm the assertion that the datafication of almost everything is growing relentlessly: in 2012 it was claimed that 90% of the world’s data had been created in the previous two years (IBM 2012), and a future 40% annual rise in data generation has been estimated (Manyika et al. 2011).

Less commonly noted is the place of everyday experience in the machine of datafication. The Berliner Gazette (nd) has claimed that 75% of these newly available data are by-products of people’s everyday activities, and Michael and Lupton also note the centrality of the everyday in the current Big Data moment:

Human actors contribute to big datasets when they engage in activities such as making calls and using apps on mobile phones, using online search engines such as Google, purchasing goods or services online or taking part in customer loyalty programmes, uploading contributions to social media platforms, using wearable self-tracking devices or moving around in spaces that are equipped with digital sensing or recording devices (Michael and Lupton 2015, 104).

Despite the significance of such everyday practices in the production of large-scale data, little attention has been paid to people’s thoughts and feelings about these data-producing processes. These issues have not, on the whole, been the focus of the emerging field of data studies, which seeks to understand the new roles played by data in times of datafication. This is a problem for a number of reasons. First, if we do not understand whether data condition everyday experiences as it is claimed, and our thinking about these matters is not informed by the perspectives of the people upon whose data datafication is built, scholarship about data-in-society will be incomplete. Second, and importantly for this special issue, in the absence of such knowledge, data activism, which seeks to challenge existing data power relations and to mobilise data in order to enhance social justice, will rely upon the judgments of elite technical actors and activists about what would constitute more just data practices. In contrast, I argue that to build a picture of what just data arrangements (that is, the practices of organisations that handle and produce data, the policies that govern these practices, and provisions for the development of skills that people need in order to engage with data) might look like, it is important to take account of what non-expert citizens themselves say would enable them to live better with data, based on their everyday experiences of datafication. Greater understanding of everyday living with data can contribute significantly to the knowledge base on which data activism is built. A third problem, then, is that by not focusing on these issues, the field of data studies is not currently as well aligned to the aims of data activism as it might be. This paper explores how we might address this gap.

The paper proceeds to elaborate the argument that data studies and data activism could be better aligned through a focus on everyday experiences of datafication. The next section provides a brief sketch of the field of data studies, identifying the everyday as a critical absence. Here I discuss how more empirical research into what it means to live with datafication could enhance both data studies and data activism. In the subsequent section, I outline what the project of researching living with data might look like. I explore two possible approaches to this endeavour, the first of which I describe as “a phenomenology of datafied agency.” The second focuses on data-related capabilities and their emotional dimensions. Both of these approaches, I argue, suggest the need for a vocabulary of emotions in researching everyday living with data.

Inserting the Everyday into Data Studies and Data Activism

Within the emerging field of data studies, datafication is said to have all kinds of effects, many of them troubling, and to result in an array of new harms. These include: increased surveillance; threats to privacy; new forms of algorithmic control; and the expansion of new and old inequalities and forms of discrimination. Surveillance is said to be much more ubiquitous, opaque and speculative in datafied times, as social media and other kinds of data mining make it possible to surveil aspects of life once private and intimate (Andrejevic and Gates 2014, Dencik and Cable 2017, Lehtiniemi 2017) and thus deny people their basic right to privacy (Cohen 2013). Privacy itself is a contested issue, with industry figures like Mark Zuckerberg claiming that it is no longer a social norm (Johnson 2010), and critical researchers pushing back against this view with all kinds of empirically grounded (boyd 2014) and philosophical (Nissenbaum 2009) assertions that privacy does, in fact, still matter. Elsewhere, significant attention has been paid to the function of algorithms in emergent forms of datafied governance and control. In times of datafication, algorithms have power, it is claimed (for example by Gillespie 2014 and Striphas 2015). They make and shape data in particular ways, certifying knowledge and so shape public, social and cultural life.

Another troubling consequence of datafication is that it reproduces old inequalities and creates new ones. One of danah boyd and Kate Crawford’s much-cited “six provocations for big data” is that “limited access to big data creates new digital divides” (boyd and Crawford 2012, 673). In data mining, who is deemed to have expertise determines who controls the process and the “knowledge” about the social world that results, knowledge which in turn reproduces the social world, as scholars writing about the power of algorithms also claim. Relatedly, and emerging from these debates, the discriminatory consequences of the rise of big data have also been noted. Data mining, analysis and subsequent discrimination result in certain groups having better access to all kinds of resources (Andrejevic 2013, Taylor and Richter 2017). Datafication affects citizens differentially, and data-driven discrimination can mean that already-disadvantaged populations have their access to fundamental human rights further limited (see for example Gangadharan 2012 and 2015).

To date, studies of the rise of datafication have primarily sought to expose these harms, and the field of data studies has therefore been dominated by critical political economy and neo-Foucauldian analyses of the problems that accompany widespread datafication and its intricate relationship with neoliberal forms of governance. Data are seen as powerful and troubling actors in the control of contemporary life, playing a role in shaping how we live, what we know and how we know, and contributing in new ways to old problems like discrimination and inequality. Without doubt, this literature has played a vital role in making visible the serious issues that datafication raise in relation to rights, freedoms and justice, and in questioning the celebratory rhetoric that has accompanied the spread of big data.

But there are some absences here. Because data studies has primarily focused on the operations of data power and their harmful consequences, it has been characterised in large part by a focus on powerful actors. Studies which focus instead on attempts to democratise data, such as open data initiatives (Baack 2015), hackathons (Gregg 2015, Irani 2015), the Quantified Self (QS) movement (in which participants use apps and mobile devices to collect data about various aspects of their bodies and lives, such as the work of Neff and Nafus 2016) or data activism, also often focus on elites, as the initiatives and practices which are the object of these studies tend to involve technological and data savvy experts. For example, as early researchers of the QS movement have acknowledged, QSers are atypical in their data expertise, commitment and enthusiasm, and so cannot be said to be representative of “ordinary people.” Indeed, my use of the term “ordinary people” in the cultural studies tradition in this paper highlights what is missing from the existing literature. Building on the politics of the ordinary, my argument here is that “lowering” academic sights (McCarthy 2008) to “activities in the daily round” (Silverstone 1994) is a much-needed political gesture in the field of data studies.

Thus data studies has not paid much attention to the everyday experiences of non-expert citizens of living with data and datafication. Michael and Lupton noted in 2015 that “there is still little research that has investigated what the public make of big data, aside from reports from privacy organizations and government bodies” (Michael and Lupton 2015, 110), and that characterisation is still largely true two years later. It is because of this absence that, alongside Michael and Lupton, commentators such as Couldry and Powell (2014), Pink et al. (2017) and Ruckenstein and Pantzar (2015), have called for more research into everyday engagements with data. I join them in this call in this paper, argue that such a turn is important to data activism as well as to data studies, and explore how we might enact it. Indeed, it is because of these limitations in dominant critical approaches that I propose that the term “data studies” is preferable to describe this emergent field, rather than the alternative “critical data studies” which is also sometimes used (for example by Iliadis and Russo 2016).

It should be noted that there are a small number of exceptions to my claim that data studies has not attended to the everyday. These include research into the datafication of health, such as Harris et al’s (2016) work on cybergenetics, and the work of Lupton (2018) and Ruckenstein (2016). Another example is Couldry et al’s (2016) Storycircle project, which explored how analytics are used by community groups for social ends. Research in the field of data-driven discrimination, such as Eubanks and Gangadharan’s Our Data Bodies project (http://www.odbproject.org/), is also an exception, as it grounds concerns about the discriminatory effects of datafication in empirical research with those most likely to be discriminated against. Two small-scale studies have explored how social media platform users feel about these technologies and their algorithms (Bucher 2017, Colbjornsen forthcoming). Barassi’s Child Data Citizen project (http://childdatacitizen.com/), which aims to look at how the lived experience of childhood is being transformed by datafication, is also an exception, as is research into the everyday self-monitoring practices that have emerged from the more elite QS movement mentioned above (especially the work of Pink, such as Pink and Forst 2017, Pink et al 2017 and Pink et al 2018; see also Sharon and Zandbergen 2016). These examples notwithstanding, there is limited research which seeks to develop understanding of how ordinary people experience and live with data as part of everyday life – these exceptions represent only a small handful of projects from across the globe, after all. In short, we need more empirical research into everyday living with data.

A further problem with existing data studies literature is that much of it conceives of life with data in limited ways, as harmful and oppressive. This is not helpful for data activism which, as noted above, seeks to identify and establish more just forms of datafication. Data activism is characterised by mobilisations against existing data uses and practices, and has been defined as a “series of sociotechnical practices that, emerging at the fringes of the contemporary activism ecology, critically interrogate datafication and its socio-political consequences” (Milan and Van der Velden 2016, np). As such, data activism is premised on the assumption that current “data arrangements” are harmful to non-powerful citizens, and that alternative arrangements are therefore needed to improve people’s experiences of datafication. Data activism thus requires the possibility of agency, yet there is little scope for agentic engagements with data in the visions of datafication provided in much data studies scholarship.

Bringing the sociology of the everyday into data studies can help to resolve this problem. In sociological terms, the everyday refers to the habits and practices in which we engage and which surround us, what Pink et al describe as the “routines, contingencies and accomplishments” of the mundane (Pink et al 2017). Given that datafication is now widely considered to be a defining feature of everyday life (Couldry 2016), this approach is clearly helpful for understanding everyday experiences of datafication. Because the everyday is conceived as contingent and situational within this sociological sub-field, this makes it possible to think of social phenomena like datafication as not simply constitutive of social life, as critical political economy and neo-Foucauldian analyses do, but rather as “made and unmade” (Neal and Murji 2015, 812) through everyday practices. (Of course, people’s everyday lives are not all the same, and it is important to take account of how social inequalities lead to different data experiences, as work on data and discrimination has begun to do (such as the Our Data Bodies project mentioned above)). Thus not only does researching everyday engagements with data fill an empirical gap for data activism, its acknowledgement of the agentic actions that constitute social and political life mean that it is well aligned with data activism’s interest in the possibilities for agency in datafied times.

There is a third contribution that a focus on the everyday can make to data activism, in addition to the two already noted (that is, filling an empirical gap and focusing on agency). In a talk about data activism which had as its subtitle “the conditions of possibility for democratic agency in the datafied society”, Stefania Milan (2017) identified three such conditions. The first is critical consciousness, or “conscientização”, a Portuguese term coined by Brazilian popular educator Paolo Freire to refer to achieving critical understanding of the world and taking action against injustices revealed through such consciousness (Freire 1968). The second, according to Milan, is grassroots data literacy, which is required in order to comprehend existing data practices and processes. The third is critical imagination, or the capacity to imagine alternative forms of living with data. Here Milan draws on Emirbaye and Mische’s (1998, 970) definition of political agency as constituted “through the interplay of habit, imagination and judgement” (my emphasis). But what precedes the achievement of these conditions outlined by Milan? What comes before them? We need to know the answers to these questions before we can understand what leads people (or future data activists) to be able to acquire them. Here again, understanding of non-expert citizens’ experiences of datafication is crucial, because we cannot arrive at the conditions identified by Milan without understanding first how people who do not (yet) consider themselves as data activists experience datafication, and second how to move from these experiences to engagement in data activism. In the next section, I explore two possible approaches for arriving at such understanding and some of the issues that might emerge along the way.

Approaches to Researching “Living with Data”: Two Possibilities, and an Emergent Vocabulary of Emotions

One of the main purposes of exploring how ordinary people experience datafication in their everyday lives is to develop understanding of their perspectives on how they might live better with data, understanding that is useful to the mission of data activism and its efforts to improve data arrangements so that they are less harmful and more just. Taking account of what people say about these issues is important, but so are the conceptual tools with which we develop this knowledge. In this section, I explore two possible approaches that put philosophical concepts into dialogue with examples from my own research. Taken together, these explorations suggest the need for a vocabulary of emotions in researching living with data.

A Phenomenology of Datafied Agency

The field of the philosophy of technology, concerned in large part with questions about the relationship between technology and well-being, seems a good place to start exploring how people live and how they might live better with data. A major question for this field is whether technological developments of diverse kinds are good or bad for society – as Brey (2012) notes, whether it is possible to lead good lives in a world so committed to technology is a pressing question. While some philosophy of technology literature focuses on subjective variations of well-being, either asking whether technology can make us happy (Spahn 2015), or conflating well-being with happiness and using these terms interchangeably (Søraker et al. 2015), subjective notions like happiness are not helpful to the cause of data activism and the data justice it seeks to promote. They are individualistic and do not contribute to thinking about the kinds of data arrangements that might benefit communities of people disadvantaged by current conditions.

We need therefore to turn elsewhere in the field, to philosophers of technology who ask whether technological ensembles of all kinds can be appropriated as tools of democratisation, enablement and activism, despite their origins within the belly of the beast. Data activism asks these same questions of datafication, a new kind of technological ensemble. This concern is captured nicely in the first words of Andrew Feenberg’s preface to Transforming Technology: “must human beings submit to the harsh logic of machinery, or can technology be fundamentally redesigned to better serve its creators?” (2002, v). Through this question about agency, important both to data activism and to researching datafied everyday life, Feenberg raises the issue of differential technological subject positions that he addresses explicitly elsewhere. In another book, Feenberg (1999) argues that the fundamental difference “between the dominant and subordinate subject positions with respect to technological systems” (1999, x) is significant, a distinction which echoes my argument that non-expert citizens’ experiences of datafication are differentially socially stratified. “Ordinary people encounter technology as a dimension of their lifeworld” (1999, x), he writes, and he continues:

For the most part they merely carry out the plans of others or inhabit technologically constructed spaces and environments. As subordinate actors, they strive to appropriate the technologies with which they are involved and adapt them to the meanings that illuminate their lives. Their relation to technology is thus far more complex than that of dominant actors (which they too may be on occasion) (1999, x).

Differential subject positions matter, Feenberg argues, because change comes “when we recognize the nature of our subordinate position in the technical systems that enrol us, and begin to intervene in the design process in the defence of the conditions of a meaningful life and a livable environment” (1999, xiv). Likewise, my argument here is that moving beyond critical thinking about technology, as Feenberg advocates, and exploring technology’s “ambivalence”, or “the availability of technology for alternative developments with different social consequences” (1999, 7), is an essential component of data activism and an important next step for data studies.

Feenberg’s concerns are concerns about agency, as are those of data activism. To address these issues of agency and possible change through empirical research into everyday experiences of datafication, a phenomenological approach might be helpful. Phenomenology focuses on the point of view of actors and their perceptions and experiences of the (datafied) world – in this sense it is distinct from ethnography which is more commonly dependent on the point of view of the observing researcher. This perspective and phenomenology’s excavation of the taken-for-granted layers of everyday action fit with the project that I am describing here, which also prioritises the conscious experiences of datafication of non-expert citizens. As such it enables attention to the differences in people’s experiences of datafication which, as noted above, are significant. Schutz and Luckmann’s (1973, 105) argument that phenomenology acknowledges that “not only the what but also the how of the individual situation in the lifeworld belongs to the fundamental elements of the stock of knowledge” (quoted in Couldry et al 2015, 125), further confirms its usefulness for producing a more detailed understanding of living with data “from the bottom up” (Couldry and Powell 2014). Also arguing for a phenomenology of datafication, Couldry et al (2015) state that we need research “that recognises people’s ongoing reflexivity about their conditions of entanglement with [the] digital infrastructures” of datafication (2015, 124) – indeed, Couldry et al and some of the other researchers turning their attention to living with data who were noted above (such as Ruckenstein 2014) describe their own research as phenomenological. For many writers, such reflexivity is intricately entangled with agency. Couldry, for example, defines agency as “the longer processes of action based on reflection, giving an account of what one has done, even more basically, making sense of the world so as to act within it” (Couldry 2014, 891). Phenomenology’s focus on both of these things, reflexivity and action, make it a useful approach for researching living with data.

My research into uses of social media data mining, in organisations which I describe as the pillars of everyday life (local councils, museums, training organisations, educational institutions and shops), illustrates what a phenomenology of datafied agency might offer. This research focused on the datafication of working life, not on non-experts’ experiences of data in the everyday, but nonetheless it is indicative of what might surface through such an approach. In this research, one thing that emerged across research sites was a desire for numbers, which, I have argued, engaging in data mining elicits (Kennedy 2016). I give some examples below, after which I reflect on what these tell us about reflexivity, agency, and the importance of researching living with data.

In research with city councils and museums in which we experimented with social media data mining techniques, the data generated through our experiments was met with much enthusiasm by participants, especially when presented in visually-appealing charts and graphs. A sense of amazement was expressed by participants who read reports we produced and who attended workshops. Emotional responses to data often elicited a desire for more data. One participant said:

I think I had a lot of confidence in the numbers. I think I was amazed by how deep a lot of these tools could go. […] I think they’re very clever. It was amazing how much you could drill into this.

Some participants said that they were required to report the results of analytics exercises “up” to managers and funders, but that there was no discernible action taken as a result. Quantitative data, produced through systems like Museum Analytics (a platform which carries out social media analytics for the museums sector, as the name suggests), were desired by managers and funders, with no apparent concrete consequence – the “data gathered” box was ticked, the desire for numbers was fulfilled, and data were filed away.

In interviews within social media insights companies, some social media analysts said that accuracy was not important to their clients. One said:

Whether that data is accurate or not is irrelevant. They just want some numbers to put into a PowerPoint that they can show to their boss. If anyone asks, “Are you keeping an eye on social media?” You can say, “Yes, we’re 36 this week.” And it is a very attractive solution.

Sometimes clients are drawn in by the allure of numbers and just want numbers, participants said – inaccuracy is acceptable, as long as the desire for numbers is fulfilled. Participants felt that this desire for numbers suppressed discussion between them and their clients about the limitations of the data that data mining produces, and they were frustrated at this. They were alert to the inadequacies of the numbers they produced and they would have liked to talk to clients about the challenge of obtaining good quality, accurate data, and about what the numbers that social media analytics produce do and do not represent.

In an interview in an educational organisation which uses the services of social media analytics companies, a digital marketer expressed his frustration with what he referred to as “the fetishism of the 1000”. He claimed that within his organisation there was a perception that the ability to cite numbers of people “reached” through a campaign was proof in itself of a campaign’s success. When a project has been completed, he said, if numbers can be produced, if it can be claimed “that we’ve reached 50,000 and we’ve had 1000 people respond back to us about it, then that fulfils some kind of sense of requirement.” He felt that measurement was rarely undertaken with a genuine desire to self-evaluate, but rather was motivated precisely by a desire to produce numbers, which were uncritically equated with success.

The emotions that came to the surface in this research seem troubling, as it seems as if there is no escape from the prevalent desire for numbers in the datafied workplace. However, a phenomenological approach, which excavates the layers of conscious experience and is attentive to the judgements, perceptions and emotions of key actors, reveals a more nuanced picture. As can be seen in the examples above, although a desire for numbers dominated, participants were also reflexive about the ways in which such desires limit critical engagement with data amongst their clients or colleagues. There was also some reflection about the ethics of data mining amongst participants, about whether data mining methods are acceptable, whether the mantra that social media data are public and therefore “fair game” to be mined and analysed holds up. All of these reflections, and related ethical decision-making and line-drawing, can be seen as nascent acts of agency and point towards possibilities for different data arrangements, as participants did not simply submit to the “harsh logic” (Feenberg 2002, v) of datafication and desiring numbers. A phenomenological approach to participants’ experiences also highlights the important role that emotions play in engagements with data in the workplace. This in turn suggests a need for a vocabulary of emotions in researching everyday experiences of datafication.

Data-related Capabilities and Their Emotional Dimensions

Another way in which we might bring together data activism’s interest in social justice with people’s perspectives on how they live and might live better with data is through the capabilities approach. Two of the original proponents of this approach, Amartya Sen and Martha Nussbaum, argue that to think about how people might live well, we need to focus on what people need to be capable of doing, should they choose to (Nussbaum 2006 and 2011, Sen 1973, 1992 and 2009). For Nussbaum, the capabilities approach directs attention to how certain social or institutional arrangements are more effective than others in enhancing social life and social justice. Both Nussbaum and Sen emphasise the role of the external environment in enabling capabilities; this makes possible a normative assessment of technological developments like datafication, argues Johnstone (2012). Nussbaum’s version of the capabilities approach also involves asking how we might live well together, especially in light of the growing value attached to competitive individualism and neo-liberalism. Despite the dominance of these latter ideas, within capabilities thinking, “the notion of the common good survives as a key ethical principle” argues Hesmondhalgh (2012, 84), a notion that is clearly relevant to data activism.

For Sen, capabilities are freedoms (freedoms to – for example receive an education, earn a living, express oneself, form relationships – and freedoms from – for example oppression, violence, censorship, arbitrary arrest) (Johnstone 2012). He defines capabilities as “the various combinations of functionings (beings and doings) that a person can achieve […] reflecting the person’s freedom to lead one type of life or another […] to choose from possible livings […]” (Sen 1992, 40). Nussbaum (2006) lists ten such capabilities, some of which are physical, such as not dying prematurely and having good health and shelter. Others relate more clearly to datafication: being able to imagine, think and reason; being able to form a conception of the good and to engage in critical reflection about planning one’s life; and having control over one’s (political or material) environment, can all be influenced in one way or another by people’s experiences of data and datafication.

A number of media theorists have applied the capabilities approach to explorations of the media practices that can enhance people’s efforts to live good lives (such as Coleman and Moss 2016, Hesmondhalgh 2013, Mansell 2002). For example Hesmondhalgh’s (2013) Why Music Matters draws on Nussbaum and Sen’s ideas to reflect on the communicative practices – in this case, in relation to music – that would create conditions under which humans might flourish. Whilst it might be easy to understand why cultural goods like music matter in relation to living well and flourishing, these questions are equally important in relation to data and datafication.

Exploring how ordinary people’s experiences of datafication might be enhanced through the lens of the capabilities approach could also be seen as a response to Andrew Sayer’s argument that ideas about living well and flourishing are vital for attempts to understand how greater social justice might be achieved (Sayer 2011). Sayer argues that social science should be more attentive to people’s first-person, evaluative relation to the world, to their evaluations of how they live and how they might live well, because “Social struggles are not merely struggles for goods and power but about how to live, about what is a just, virtuous or good life and a good society” (2011, 172). Social science often disregards people’s evaluative relation to the world and the force of these evaluations, he claims, but we need to attend to these things; values, feelings and emotions need to be taken more seriously in social sciences. Sayer writes that there is a “macho tendency to view the study of values, emotions and ethics as less scientific than the study of power, discourse and social structure”, whereas he argues that we need to develop our understanding of the former which, he argues, constitute “ethical being in everyday life” (Sayer 2011, 15).

 

Ideas about capabilities and flourishing might help us to understand what is important for people to live their lives well with data. In relation to datafication, capabilities might include being able to have control over one’s own data, to choose to opt out of – or, better still, in to – data gathering, and to make sense of data mining processes because they are made transparent to non-expert citizens, or accountable to expert others. The problem is that, whilst these issues are widely discussed amongst data activists, ordinary people’s perspectives on whether they might result in living better with data are missing from these debates. This is why we need to produce the kinds of first-person evaluations of “living well with data” that Sayer advocates. 

As with the phenomenological approach discussed earlier, a capabilities approach also highlights the importance of emotions in relation to living well and flourishing. Through this approach Hesmondhalgh looks at how music communicates emotions in particular ways and how emotions thus play a role in good lives. Likewise, Sayer highlights the significance of feelings in everyday evaluative relations with the world. In other research of mine which explored how people engage with visual representations of data which circulate in the everyday, my co-researchers and I found that emotions play important roles in such engagements (Kennedy and Hill 2017, Kennedy et al 2016). Because a major way that most people access data is through their visual representation, as Gitelman and Jackson (2013) argue, visual sensibilities are required in order to make sense of data, not just cognitive reason and statistical skills. This entanglement of the numeric and the visual, at the heart of most people’s engagements with data in their everyday lives, means that data stir up emotions. A broad range of emotions emerged in relation to engagements with visual representations of data in the research, including pleasure, anger, sadness, guilt, shame, relief, worry, love, empathy, excitement, offence. Participants reported emotional responses to visualisations in general, represented data, visual style, the subject matter of data visualisations, the source or original location of visualisations, and their own skill levels for making sense of visualisations (Kennedy and Hill 2017).

Thinking about the relationship between emotions and capabilities can contribute to understanding experiences of engaging with visual representations of data. In our research, participants expressed strong feelings about their own skills – or capabilities – for decoding visualisations. Some participants felt a lack of confidence in this regard. One said of a visualization: “It was all these circles and colours and I thought, that looks like a bit of hard work; don’t know if I understand”. Many of our participants expressed similar negative feelings about their lack of skills, and this lack of confidence had a profound impact on some participants’ engagement with visualisations. One reacted to all of the visualisations that we showed him in a focus group with confusion and dislike, as seen in the grid he produced, on which we asked all participants to place thumbnail images of visualisations in order to identify whether they liked, or had learnt from, the visualisations which we showed them. He placed most thumbnails in the “disliked + didn’t learn” quadrant, and one outside the grid altogether (Figure 1).

Figure 1: Seeing Data participant’s grid

This participant responded in this way because he doubted his own ability to make sense of visualisations. This lack of confidence was echoed by other participants, such as this person who wrote in her diary about visualisations in a newspaper article on tax:

I felt confused and a bit stupid for not being able to stay the course with this article. It’s too maths based for me. Too many numbers and pie charts, I get lost in it.

Feeling “stupid” was the result of a perceived lack of skills, such as knowing how to read particular chart types. When participants felt more skilled, unfamiliar chart types could evoke positive emotions, rather than negative ones: “I didn’t hate it because it made me want to try and put a little bit of effort into navigate those lines”, said another participant about a visualisation of freshwater consumption across the globe.

Educational background was an important factor which influenced whether participants felt stupid or felt capable, something which they themselves recognised. Some participants identified that higher education contributed to the development of relevant skills. One participant who had a Masters degree was more confident about how to understand and assess the data visualizations than the participant whose grid is shown in Figure 1, because he felt he had the training to do so. Alongside education, gender and class, which we might describe as social arrangements, to use Nussbaum’s term, appeared to influence emotions such as feeling confident or feeling stupid.

Although these examples are not concerned with reducing data-related inequalities and improving data justice as data activism seeks to, the emotional dimensions of living with data which they reveal are relevant to this mission. As Sayer notes, it is important to take people’s values, emotions and ethics seriously in our quest for social justice. Feelings play a role in non-experts’ experiences of data and datafication processes. Building on this finding, for example in initiatives which aim to enhance data literacy (Milan’s second condition for the possibility of datafied agency), might result in more people feeling confident about their skills for engaging with data, and greater understanding of datafication and its many consequences might result. Thus the approaches I explore in this paper, and the ways in which they enable foregrounding the emotional dimensions of living with data, are important for data activism as well as for data studies. To advance, both fields need better understanding of how data and emotions relate to each other in ordinary people’s everyday experiences of datafication.

In thinking with concepts like capabilities and living well with data, it is important to subject them to critical scrutiny, of course. For example, how might data be constrained from contributing to people’s flourishing, given their location in unequal relations of power? How to account for how injustice, inequality and oppression inform people’s access to capabilities, to living well and flourishing (Hesmondhalgh 2012)? How to avoid homogenising everyday experiences, and instead to recognise that human needs “may take very different forms in different societies” (Hesmondhalgh 2012, 18)? Who gets to decide what needs to be sacrificed for the common good? It is important to acknowledge that these notions are not without problems – they are abstract and complex and suffer from what Johnstone (2012) describes as “radical empirical underspecification.” Nonetheless, their application in data studies and in data activism could open up a vocabulary of the emotional, and such a vocabulary could make these issues explicit and open them up for debate. Thus these concepts, and the vocabularies they bring with them, are potentially useful for understanding datafication’s social consequences. They might help our mission to identify the types of data arrangements that can enhance people’s efforts to live good lives, reduce data-related inequalities and improve data justice.

Conclusion: Why This Matters for Data Activism

Datafication is a major social phenomenon which has all kinds of effects, and because of this, how people experience data in their everyday lives is extremely important. Yet, in data studies and in data activism, little attention has been paid to ordinary people’s thoughts and feelings about their own data production and the data practices of others. Understanding such everyday experiences is crucial, because without such understanding, data studies and data activism are not informed by the perspectives of the people with whose lives they are ostensibly concerned. In this paper I have argued that we need to look at datafication as it is lived, felt and experienced at the level of the everyday. We need to attend to ordinary people’s perspectives on how data arrangements can be improved, so that these perspectives can play a role in determining improved data arrangements. Through this, I argue, an empirical gap in data studies would be filled, knowledge that is useful for data activism would be produced, and data studies and data activism would be better aligned. Data activism seeks to challenge unjust data arrangements and to mobilise data in order to enhance social justice, and taking account of what non-expert citizens say would enable them to live better with data will help data activists to imagine more just arrangements, the third of Milan’s conditions for data activism.

I have also argued that inserting the everyday into data studies opens up a space in which to explore possible conditions for agency in datafied societies, to paraphrase the subtitle of Milan’s talk. The topic of agency is a shared concern of scholars of the everyday and of data activists, and in this way a focus on the everyday makes another contribution to data activism. What’s more, not only does researching everyday experiences of datafication fill an empirical gap and highlight issues of agency, it also enables us to attend to the question of what precedes data activism. How is datafication lived, felt and experienced by non-expert citizens before they start to develop the conditions or consider the possibility of activism in relation to data? Data activists cannot make the three conditions for datafied political agency identified by Milan possible without first understanding where we need to move from in order to get to these conditions.

In this paper, I explored two approaches to researching everyday engagements with datafication. The first, “a phenomenology of datafied agency”, mobilises a phenomenological excavation of data experiences to explore the possibility of agency in datafied conditions. The second is an approach which looks at data-related capabilities and their emotional dimensions, and which highlights the importance of identifying what people need to be capable of doing in order to live well in times of datafication. Arguably, the “conditions of possibility for political agency in the datafied society” identified by Milan are, in fact, capabilities. Thus there is a direct link between these approaches and data activism. Both of the approaches I discuss highlight the importance of emotions in everyday engagements with data. The ways in which these approaches value emotions in relation to acting agentically and living well can help us understand the important role that feelings play in everyday engagements with data. These approaches thus suggest the need for a vocabulary of emotions within data studies and data activism, something that has been largely absent from these fields to date.

At the time of writing, there are many examples of initiatives which aim to improve life with data. Attempts to “do good with data”, to paraphrase the strapline of US visualisation agency Periscopic, include enterprises like DataKind, which puts data experts, working pro bono, together with social groups to address social and humanitarian problems, for example relating to homelessness and child poverty. Data visualisation itself is seen by many practitioners as a way of “doing good with data” (this is the strapline of a datavis agency after all), as this practice is often motivated by a desire to make data transparent and accessible. Efforts to develop alternative, human-centric personal data management models, like the MyData movement (http://mydata2016.org/) and to enhance data literacy, such as School Of Data (https://schoolofdata.org/), can also be included here. What is missing from these endeavours is an understanding of non-expert citizens’ perspectives on whether they might result in better living with data. We need to listen to the voices of ordinary people speaking about the conditions that they say would enable them to live better with data and, in so doing, arm ourselves with knowledge which advances data studies, serves the interests of data activism, and brings both fields into closer alignment with each other.

 “Someone once said it is easier to imagine the end of the world, than it is to imagine the end of capitalism,” wrote Frederic Jameson, in a now famous reflection on the stifling parameters of cultural life in late capitalist societies.

Building on this, Mark Fisher advanced the concept of “capitalist realism” in his 2009 book of the same name as a way to articulate the peculiar persistence of a system that has proved itself so full of fallacies, so unjust and inegalitarian in its rewards (Fisher 2009). Writing in the wake of the financial crisis, Fisher described the contemporary condition of capitalist realism as a ‘‘pervasive atmosphere” that comes to regulate thought and action, limiting the possibilities of even imagining alternatives: “[it is] the widespread sense that not only is capitalism the only viable political and economic system, but also…it is now impossible even to imagine a coherent alternative to it.” (Fisher 2009: 6; italics in original). In describing capitalism in these terms, his ambition was to stress its contingency and destroy its appearance as an inevitable ‘‘natural” order.

The dominance and resilience of contemporary capitalism has been a prominent theme in debates on the politics of imagination, and linked to that, the field of social and political imaginaries (Adams et al. 2015). In looking at imagination and imaginaries, we are invited to consider how we make sense of society, instituting and instituted by social practices in their emergence, formation and reproduction. As such, a concern with the politics of imagination is as much a concern with the way in which social institutions and practices are legitimized and continued as it is a concern with the possibilities for the articulation and doing of alternative formations. That is, imagination can both open and close a path to critique (Bottici and Challand 2011).

Capitalism, as a political-economic imaginary in the context of modernity (Adams et al. 2015), is intimately linked to contemporary forms of surveillance. Today, capitalism is said to increasingly progress through an accumulation logic based on the ability to monitor and track different forms of social activity with the view to predict and modify human behavior as a means to gain revenue and market control. An information order Zuboff (2015) has described as ‘‘surveillance capitalism”, advanced in the form of ‘‘big data”, and underpinned by a digital economy based on mass data collection and analysis. Turow et al. (2015) have argued that this information order now constitutes what can be described as a ‘‘21^st century imaginary” in which we see the discursive and institutional normalization of surveillance infrastructures pervading more and more aspects of everyday life. Despite prominent concerns with how these infrastructures might be inherently unjust, Turow argues that ubiquitous and continuous data collection has become ‘‘common sense” – a set of practices that people have become widely resigned to.

In this article I engage with this interplay between data-driven surveillance and contemporary social imaginaries, using research based on the aftermath of the Snowden leaks, first published in June 2013, which revealed unprecedented details of contemporary surveillance programs. Drawing on Fisher’s use of the term ‘‘‘realism” in relation to capitalist realism, I advance the argument here that public debate and response to the Snowden leaks indicate a similar ‘‘pervasive atmosphere” that comes to regulate thought and action, in which the active normalization of surveillance infrastructures limits the possibilities of even imagining alternatives – a condition I describe as surveillance realism. I also use this to highlight some of the opportunities and challenges in articulating and doing resistance, such as the kind that has emerged in the form of ‘‘data activism” (Milan and van der Velden 2016). Whilst the research is focused on the UK and the Snowden leaks, I also draw here on wider debates and studies to illustrate key developments. By analyzing activities and public response in this way, the point is, in line with Fisher, to ‘‘reveal what is presented as necessary and inevitable to be a mere contingency” as a way of advancing an emancipatory politics that can also then ‘‘make what was previously deemed to be impossible seem attainable.” (Fisher 2009: 21).

I start by briefly outlining the role of imagination in sustaining and challenging social orders before going on to discuss contemporary imaginaries of surveillance and datafication, providing examples from post-Snowden developments. Based on this, I argue that surveillance realism is useful for understanding the politics of imagination in relation to ubiquitous data collection, and I end by considering how resistance might be enacted in such a context.

The Politics of Imagination

Early inceptions of imagination, such as those articulated by Aristotle and later within the Kantian tradition, predominantly viewed imagination in relation to individual capacity. It is with the emergence of social imaginaries as a field that we begin to emphasize the properly social aspect of the imagination, and to grasp it as authentically creative rather than as merely reproductive and imitative (Adams et al. 2015). In his seminal work on the imaginary institution of society, Castoriadis (1987) stresses the collective instituted and instituting forms of meaning and the societal dimension of the human condition. Imagination, for Castoriadis therefore, is linked more to a social imaginary that comes to be central for the very existence of society, in that the instituting social imaginary is always at the same time instituted. Coming before both the concept of ideology and utopia as presented in the related work of Ricoeur (Bottici and Challand 2011, Adams et al. 2015), Castoriadis presents a view of imagination as radical, in the double sense that without it there could be no reality as such, and that it can always potentially question its objects by disclosing possible alternatives.

In advancing a concept of a social imaginary, ‘‘reality” and ‘‘subject” become intertwined. As laid out by Taylor (2004, 2), the social imaginary is, therefore, not a set of ideas; rather, it is what enables, through making sense of, the practices of a society. For Taylor, this is central to understanding the nature of modernity and the conception of moral order of society that accompanies it. He describes the relationship as a form of ‘‘embeddedness” in that certain self-understandings are embedded in certain practices that are both promoted by the spread of these practices and which shape them and help get them established. It is, as he argues, ‘‘both a matter of identity – the contextual limits to the imagination of the self – and of the social imaginary: the ways we are able to think or imagine the whole of society.” (2004, 63). The focus is on the way ordinary people ‘‘imagine” their social surroundings, shared by large groups of people, if not the whole of society, leading to a public understanding that makes possible common practices and a widely shared sense of legitimacy.

In that sense, a focus on imagination in this collective form helps to understand how systems come to be legitimized and what comes to be perceived as possible. Whilst the concepts of imagination and imaginary have been used much more differentially than this (including a prominent discussion on socio-technical imaginaries in the field of Science and Technology Studies), my interest in them here is in terms of how people come to make sense of social institutions and practices. I therefore draw on the field of social imaginaries as outlined by Adams et al. (2015, 19) as elucidating ‘‘the ways in which cultural configurations of meaning creatively configure the human encounter with – and formation (as articulation and doing) of – the world, on the one hand, and, articulate their centrality for the emergence, formation and reproduction of social institutions and practices, that is, of social change and social continuity, on the other.” In particular, I draw on the notion of radical imagination, in Castoriadis’ sense, as both the study of what limits our imagination and the study of what expands it. Similarly, Ricoeur’s outline of the reproductive imagination as the core of ideology and utopia situates the ideological imagination as that which reproduces an image that society has of itself, whilst the utopian imagination produces alternative images of society that puts ideological images into question (Ricoeur 1986). This can come to serve an emancipatory politics by approaching the legitimacy of established practices and the accompanying cultural articulations as socially constructed. The way in which we imagine our social surroundings is not necessary or inevitable. Emancipatory politics serves to reveal the power relations that underpin any perceived natural order as a means to simultaneously nurture alternative imaginations of what can be possible.

Although Fisher, in his description of capitalist realism, does not refer explicitly to the field of social imaginaries, he is concerned with what he describes as a ‘‘pervasive atmosphere”, articulated in the circulation of cultural products and evident in work and education, in which capitalism has ‘‘colonized dreams”, lowering our expectations into accepting that, in the words of Margaret Thatcher (during the miners’ strike) ‘‘there is no alternative’. As Fisher states, ‘‘The “realism” here is analogous to the deflationary perspective of a depressive who believes that any positive state, any hope, is a dangerous illusion.” (2009, 9). This is closely aligned to more recent debates on the resilience of contemporary neo-liberal capitalism and the relative lack of political imagination as in the articulation of alternatives. There has been a closure of the economic imaginary in the form of depoliticization, meaning the active denial of the need for a political, public discussion of the means and ends of the market economy (Adams et al. 2015, Straume and Humphrey 2010). This also speaks to Foucauldian notions of normalization as the way in which norms of conduct are established and enforced through discursive practices backed up by institutional sanctions (Foucault 1977). However, Fisher’s take on ‘‘realism” pays more explicit attention to the active marginalization of alternatives, and the dictation of terms of any resistance. That is, the focus is on the construction of the realm of what is considered to be possible.

In thinking about how people make sense of society, and understanding Fisher’s use of the term ‘‘realism” to articulate a relative closure of cultural articulations of the world as part of an active denial of political debate, I now turn to look at data-driven surveillance through this lens.

Surveillance Culture and Dataism

In analyzing the changing consumer environment of the twenty-first century, Turow et al. (2015) combine Taylor’s notion of social imaginary with Gramsci’s concept of hegemony to articulate the entrenchment of data-driven surveillance within retail. For Gramsci, hegemony is a way to understand how power is exercised without, or in addition to, coercion. It relies on a ‘‘compromised equilibrium”, achieved from concessions that subordinate groups gain from the bourgeois state, which is then maintained through the concrete coordination of interests by civil society. Civil society – the public sphere where ideas and beliefs are shaped – in turn comes to reproduce hegemony through the ‘‘organic intellectuals” in the media, universities and religious institutions that then enable ‘‘common sense” societal values and legitimacy (Gramsci 1971, Wood 2015). Whilst hegemony therefore emphasizes how common sense becomes institutionalized as part of certain social mechanisms and power relations, Taylor’s social imaginary captures the nuances of everyday life and practices. Combining these approaches, Turow et al. (2015) argue that, over time, consumers have become institutionalized into accepting a retail environment that has transitioned from broad demographic lenses to one based on their monitoring as individuals who give off streams of data, often in real time. Consultancies and technology firms assist retailers in reshaping the shopper, the store, and the deal so that people (consumers) become institutionalized into what Turow et al. understand as taken-for-granted values, habits and expectations of an increasingly data-driven and discriminatory market-place.

In his later book on the topic, Turow (2017) builds on his account of ‘‘common sense” data collection by drawing on Jackson’s 1968 notion of the ‘‘hidden curriculum”, used to refer to the social norms and rules in education that become accepted through repetition and the implicit values laid out in schools that connects young people to the structures of power in society and defines their relationships to them. This can be broadened to the education that people receive through media and culture about all institutions (Gerbner 1972), including, as Turow argues, the retail space. There is a prevalence of symbol systems that designate, for example, the meaning of trendy clothing, outdoor happiness, and wealth – codes that shoppers have come to take for granted. We are now, Turow states, ‘‘on the cusp of a retailing era that is adding an entirely new layer of routine surveillance activities and that carries with it the accompanying underlying lesson that it is common sense for shoppers to accept individualized profiling and deal making as part of the process of buying things.” (2017, 18).

Whilst Turow’s focus is particularly on data-driven surveillance in the retail space, his analysis of the normalization of surveillance infrastructures in everyday life finds echoes far beyond it and are pertinent in relation to post-Snowden debate more broadly. Indeed, when the documents leaked by Snowden were first published in June 2013, they both confirmed and surprised prevalent understandings of surveillance practices. The documents detailed a continuation of the development of what had previously been described as the “surveillance society” (Rule, 1973; Lyon, 1994), but they also represent a significant juncture in how surveillance is conceptualized and discussed. Classic conceptions such as Foucault’s ‘‘panopticon” or Orwell’s ‘‘Big Brother” struggle to account for these technological developments and later incarnations of surveillance practices (Browne, 2015). Moreover, concepts such as the ‘‘surveillance state” and ‘‘surveillance society” are no longer adequate for describing the form contemporary surveillance takes. As was made explicit in the Snowden leaks, the state is no longer the only, perhaps not even the main, arbiter of surveillance. Instead, we are confronted with what Harcourt (2015, 66) describes as a new ‘‘oligarkhia” made up of an ‘‘amalgam of the intelligence community, retailers, Silicon Valley, military interests, social media, the Inner Beltway, multinational corporations, midtown Manhattan, and Wall Street.” This ‘‘oligarkhia” is the product, in part, of shared interests in security (from foreign corporate espionage, cyber hacking, malevolent actors etc.) among government and technology companies, in conjunction with the rise of neoliberalism and the associated trend toward deregulation, outsourcing and privatization.

For Lyon, the contemporary nature of surveillance also cannot be sufficiently understood in terms of ‘‘surveillance society’, which he understands as a concept originally used to indicate ‘‘ways in which surveillance was spilling over the rims of its previous containers – government departments, policing agencies, workplaces – to affect many aspects of daily life.” (2017, 826). What is missing from these accounts are the active roles played by surveillance subjects, paying more attention to the ways in which citizens, consumers, employees etc. experience and engage with surveillance. Rather, Lyon suggests, in line with Turow’s analysis, we need to understand surveillance within everyday practices and in the very fabric of society’s culture. By advancing the concept ‘‘surveillance culture”, therefore, Lyon is seeking to highlight how surveillance is becoming part of a whole way of life: ‘‘From being an institutional aspect of modernity or a technologically enhanced mode of social discipline or control, it is now internalized and forms part of everyday reflections on how things are and of the repertoire of everyday practices.” (Lyon 2017, 825).

In placing emphasis on subjects as active participants in surveillance, Lyon points to a more complex power dynamic than have previous discussions of surveillance which focused mainly on the ways in which surveillance is exercised from ‘outside’, by one actor over another. Rather, we need to consider how different ‘‘surveillance mentalities and practices” come to be manifested (Lyon 2017, 828). This speaks to the active participation by citizens in how data is generated which marks part of the human interaction with digital environments. That is, data is collected based on what might be described as ‘voluntary’ activities by ‘ordinary citizens’, who ‘choose’ to share data about themselves. Or as Harcourt (2015, 19) argues, perhaps not so much out of actual choice, but rather ‘‘a feeling of necessity”. Digital infrastructures lure us into participating in data extraction not just in their ubiquity but also in the ‘‘seductive surveillance” that marks their technological manifestations (Troullinou 2016). The technologies which end up facilitating surveillance, Harcourt (2015) argues, ‘‘are the very technologies that we crave.” Harcourt goes as far as to argue that we now live in a society of exposure and exhibition; an ‘‘expository society” that takes the architectural structure of a mirrored glass pavilion in which we are not only seen but in which ‘‘we play and explore, take selfies and photograph others” (Harcourt 2015, 107).

Entire populations are integrated into systems of tracking and monitoring, continuously and in real-time; what Andrejevic (2017) describes as a shift from panoptic modes of surveillance to ‘‘environmental surveillance” – the replacement of selective disciplinary surveillance with total perpetual monitoring and on-going intervention. Power in such a society, Harcourt (2015) argues, circulates by a new form of rationality, one that is driven by algorithmic processes based on a ‘‘digital doppelgänger logic” in search of our data double. That is, the continuous collection of data abstracted from the digital traces left behind as we interact with our digital environments is used to identify, classify, assess, sort, or otherwise ‘‘control the access to goods and services that define life in modern capitalist society.” (Gandy 1993, 15). Indeed, as Van Dijck (2014, 198) has outlined, ‘‘metadata appear to have become a regular currency for citizens to pay for their communication services and security – a trade-off that has nestled into the comfort zone of most people.” We are seeing the gradual normalization of this datafication as a new paradigm in science and society. Such normalization is driven by the ideology of what Van Dijck refers to as ‘‘dataism”. She describes this as showing

characteristics of the widespread belief in the objective quantification and potential tracking of all kinds of behavior and sociality through online media technologies. Besides, dataism also involves trust in the (institutional) agents that collect, interpret, and share (meta)data culled from social media, internet platforms and other communication technologies. (Van Dijck 2014, 198).

In outlining dataism as the ideological component of the datafication paradigm, Van Dijck highlights how this paradigm is being advanced on a set of assumptions that are deeply contested. Not only is there an assumption that (objective) data flows through neutral technological channels, but also that there is ‘‘a self-evident relationship between data and people, subsequently interpreting aggregated data to predict individual behavior.” (2014, 199). These assumptions are further embedded in society through the active attempts to maintain the integrity of the system via government regulation aimed at assuring public trust in (private) data infrastructures by limiting excesses and harms.

Surveillance Realism Post-Snowden

The Snowden leaks and its aftermath are an important component in further outlining the nature of this datafication paradigm. Having served as contractor for the National Security Agency (the NSA), Edward Snowden gained privileged access to information about secret surveillance programs run by the NSA and British Government Communications Headquarters (GCHQ), amongst other agencies. In 2013, he leaked this information to leading global media organizations. Starting in June 2013, organizations such as The Guardian, the Washington Post, Der Spiegel and The New York Times began to publish detailed and wide-ranging stories revealing the unprecedented extent to which our activities and behavior in digital environments are tracked, monitored, analyzed and stored. While the leaks focused on surveillance by state agencies, they also highlighted the ‘‘oligarkhia” of state, corporate and commercial actors mentioned above.

Whilst protests were prevalent in the immediate aftermath of the Snowden leaks, in particularly in the United States with the Stop Watching Us protests, and in Germany with the Freedom Not Fear protests, dominant interpretations have suggested that there was little response from the broader public concerning these revelations of surveillance (Cable 2015). Often this has advanced a narrative that people either do not care, or feel that surveillance practices are largely justified in a perceived trade-off between security and privacy (Mols and Janssen 2017). Yet such an analysis is too simple and neglects the complexities of the contemporary digital environment. In this section I build on the above arguments with regards to shifts in the nature of data-driven surveillance, focusing particularly on the immediate aftermath of the Snowden leaks. I advance the concept of surveillance realism as a way to articulate the context in which we are to understand public debate and responses to the revelations of mass data collection and analysis. I use this concept to describe the nature of acceptance and resignation in relation to the increasing mass collection of data across social life and the active marginalization of alternatives, despite widespread unease and concerns about these infrastructures and systems.

The justification and normalization of data-driven surveillance has been actively advanced in public debate. When the Snowden leaks were first published, we saw a quick convergence across media, government and security services around a discourse of threat and (in)security. In the UK, condemnation of both Snowden as a whistle-blower and The Guardian newspaper for publishing the documents was prevalent across political parties, and state agencies responded with overt force to stop information from being released. This became symbolized in the now notorious confrontation at The Guardian where editorial staff was forced to destroy hard-disks and files under the watchful eye of the British intelligence agency, GCHQ, in a feeble attempt to prevent further publications of the Snowden files. Within months of the first publications the sitting director of MI5 made a rare public statement condemning the newspaper, accusing it of ‘‘handing the advantage to the terrorists” (Whitehead 2013). This argument was replicated in mainstream media debate with the majority of newspapers advancing the opinion that the publication of the Snowden leaks had compromised the work of the intelligence services (Wahl-Jorgensen and Bennett 2017). In fact, editor of The Independent Chris Blackhurst went as far as to publish an editorial explaining that he would not have published the leaks had it been up to him, stating ‘‘if MI5 warns that this is not in the public interest who am I to disbelieve them?” (Blackhurst 2013).

As Wahl-Jorgensen and Bennett (2017) have illustrated, the media debate on surveillance and data collection following the Snowden leaks became marked by an overarching discourse of securitization that situated surveillance firmly within a terrorism context. This is despite the fact that journalists themselves are often critical about surveillance practices, acknowledging not only the increasing normalization of datafication, but also raising concerns about the limited public knowledge of the extent of data collection and critiqueing the media’s contribution in providing justifications for it (Hintz et al. 2018). However, as Wahl-Jorgensen and Bennett (2017) point out, data-driven surveillance was discursively justified by stories about the Snowden revelations and its aftermath through the reliance on official sources expressing the view that surveillance should be increased or is acceptable/necessary; the most frequently expressed opinion in newspaper coverage. Sources expressing this view suggested that surveillance is crucial to national security, and is particularly important to strengthen in light of terrorist threats. As Wahl-Jorgensen and Bennett state, ‘‘the prominence of opinions that justified surveillance in the name of national security in mainstream media is not accidental. Rather, there is evidence to suggest a longer-standing legitimation of state interventions through a reference to concerns about state security in the British context.” (2017: 10). That is, as they put it, the idea of national security constitutes a discursive ‘‘trump card” overriding all other claims. This narrative was intermittently supported with statements from intelligence agencies which claimed that surveillance played an active role in curbing terrorist attacks (cf. Bakir 2015).

In such a context, a widespread logic that this also means mass surveillance is primarily a concern for those who have ‘‘something to hide” (i.e. terrorists, criminals, and other social deviants) became manifest. As Wahl-Jorgensen and Bennett (2017) suggest, the ‘‘nothing to fear” position ‘‘offers a common-sense articulation of the idea that being under constant surveillance is not only a fact of life in contemporary societies but also entirely acceptable given the constant terrorist threat.” Whilst the Snowden leaks constituted a global media event in which coverage differentiated across social, historical and political contexts with reference to national and geopolitical interests (Kunelius et al. 2017, Hintz et al. 2018), the ‘‘nothing to hide, nothing to fear” pretext as a way of describing public responses and attitudes has been prominent across national contexts (Lyon 2015, Mils and Janssen 2017). Although blogs and alternative media provided a space for more critical arguments that highlighted a lack of transparency surrounding intelligence agencies and violations of privacy, ‘‘the mediated public debate on the issue has, in the longer run, contributed to rendering such concerns less visible and marginalized” (Hintz et al. 2018, 77).

Research on public attitudes to post-Snowden data collection highlights that whilst many people actually have these concerns, the justification for surveillance is often widely internalized (Dencik and Cable 2017) along with a trust in institutions that collect data, as pointed out by Van Dijck (2014). This is not to suggest that people are either passive or apathetic in relation to their digital environments, but rather that attitudes and practices are continuously negotiated in relation to the way data-driven systems have become integrated and mediated in society. In fact, numerous studies have shown that people feel a ‘‘lack of control” over how information is collected (Eurobarometer 2015) and are ‘‘bewildered and fearful” about the use of their data (Gompertz 2016) but do not necessarily act according to such concerns. In explaining the ‘‘privacy paradox”, for example, Hargittai and Marwick (2016) emphasize pragmatism as a central component. This is the paradox that emerges from a prominent concern with privacy in the digital environment that is not manifested in actual online behavior. Focusing on young people in particular, they outline how people experience ‘‘privacy fatigue” and confusion about the data-driven systems in place, which leads to an acceptance of their data being collected as a pragmatic response in the negotiation with digital infrastructures. Moreover, research has shown that, despite an increasing awareness of surveillance and a prevalent unease with the implications of such systems, people feel largely disempowered to fundamentally challenge the nature of data collection (Dencik and Cable 2017). Rather, they come to negotiate their own position and vulnerability as part of an everyday practice within what they recognize as being relatively limited parameters, such as adjusting privacy settings on social media, or refraining from sharing certain content or not engaging in particular searches (Marthews and Tucker 2015, Penney 2016, Hampton et al. 2014).

In other words, the sheer ubiquity of surveillance infrastructures and their embeddedness in ordinary aspects of social, political and cultural participation make it difficult to think they can be challenged. Adjusting to this ‘‘reality” pragmatically is a key tenet of what Draper and Turow (2017) term a ‘‘sociology of digital resignation”. Crucially, for Draper and Turow, a sociology of digital resignation suggests that these developments are not natural or inevitable, nor that people are simply passive agents in the process. Rather, in addition to the nature of public debate discussed above, they stress the ways in which resignation to mass data collection has been actively manufactured through a number of different practices, such as obfuscation in privacy agreements between users and platforms, or simply by making services inaccessible if personal data is not shared. These defaults are ingrained in the general standards and design of digital infrastructures and are advanced in the operations of data mining practices. At the same time, people continuously navigate this environment, negotiate costs and benefits, adjust settings where pragmatically possible, but with the recognition that any actual control over the environment is limited. In recognizing the extent of a prevalent ‘‘surveillance culture”, as Lyon (2017) suggests, such resignation also illustrates the increasing struggle to actually imagine alternatives. Rather, in line with Fisher’s ‘‘realism”, expectations become lowered, and data-driven surveillance, along with its perceived infringements upon civic rights, becomes a small price to pay for being protected from terrorism, or for being able to participate in society through digital means. This surveillance realism is a realism that speaks to a hampered imagination where datafication and surveillance is seen as the only legitimate response to social ills.

Whilst the Snowden leaks provided opportunities for reflection and substantial reform on data collection, any substantial overhaul of digital infrastructures was kept at bay. Although new legislation was introduced in places like Brazil with the Marco Civil Act, and changes to the Freedom Act in the US intended to curb surveillance powers, any fundamental questioning of surveillance, and indeed of a data-driven digital economy underpinning contemporary surveillance culture, was undermined. In fact, several countries have extended surveillance powers and enabled further data collection following the Snowden leaks, with the UK’s Investigatory Powers Act leading the way (Hintz and Dencik 2016). The political discourse on surveillance in the aftermath of the leaks restricted the policy debate within very limited parameters and served to advance hegemonic powers. Disputes circulated around definitions of surveillance (e.g. at point of collection vs. point of analysis), infrastructure security and the need for encryption, and the nature of state-corporate relations in the sharing of data (Hintz and Brown 2017). However, political consolidation around the justification for mass data collection marginalized possibilities for fundamental opposition. In what can be considered atypical fashion, digital rights and civil liberties groups were invited to the table through consultations, and were allowed to participate in the policy process, but their participation became predominantly token in nature, being allowed to provide expertise and winning some battles on specific aspects of surveillance policy at the expense of any fundamental review of surveillance practices and mass data collection. In what can be considered a ‘‘compromised equilibrium”, in Gramscian terms, that comes to stabilize surveillance as ‘‘common sense”, Hintz and Brown point out that the recognition of campaign groups and advocacy organizations as legitimate actors ‘‘has enabled civil society to participate in a key policy process, but it has also risked the normalization of surveillance as principled opposition is replaced by collaboration, and it has exposed differences in civil society agendas.” (2017, 794).

These developments speak to a context in which the Snowden leaks, and their aftermath, whilst creating awareness and unease with digital infrastructures, also illustrate the perseverance of surveillance culture and dataism. When Fisher described capitalist realism, he wrote of the credit crisis of 2008: ‘‘The speculations that capitalism might be on the verge of collapsing soon proved to be unfounded. It quickly became clear that, far from constituting the end of capitalism, the bank bail-outs were a massive re-assertion of the capitalist realist insistence that there is no alternative.” (2009, 78). In this spirit, the aftermath of the Snowden leaks, in terms of both policy and technology developments (just think of the proliferation of Artificial Intelligence, Internet of Things, ‘‘smart” cities and ‘‘smart” homes), largely enabling further and wider data collection and data sharing, has made clear that Snowden’s revelations did not constitute the end of surveillance. In fact, the Investigatory Powers Act was, to use Fisher’s words, a massive re-assertion of the surveillance realist insistence that there is no alternative.

Beyond Surveillance Realism?

So what power does imagination have in a state of surveillance realism? Imagination has the potential for both oppression and emancipation; it may limit or expand that which we see as possible. As Castioradis as well as Ricoeur and Taylor make clear, a concern with the creative and collective dimensions of imagination as social imaginaries is also a concern with the ways the instituted order of society is problematized in the search for ‘‘the possible” as opposed to ‘‘the given” (Adams et al. 2015). Any established social order always includes resistance, and the aftermath of the Snowden leaks also included continued and new challenges to surveillance realism. However, the nature of resistance in any instituted order is also partly generated and shaped by the circumstances of that society. In his analysis of the anti-capitalist movement at the time of Fisher’s writing, he describes the staging of protests as ‘‘a kind of carnivalesque background noise to capitalist realism” where the suspicion was that the actual aim was not to replace capitalism but to mitigate its worst excesses (2009, 13). Although Fisher has been criticized for succumbing to ‘‘Left melancholia” (Hoffman 2016), an interesting aspect is his concern with the rejection of political organization in formulations of resistance at the time, and a turn to moral critiques of capitalism that only reinforce capitalist realism. In discussing the limits of confining critique to the moral realm, he states: ‘‘Poverty, famine and war can be presented as an inevitable part of reality, while the hope that these forms of suffering could be eliminated easily painted as naïve utopianism.” (Fisher 2009, 20). Capitalist realism, therefore, according to Fisher, can only be threatened ‘‘if it is shown to be in some way inconsistent or untenable; if, that is to say, capitalism’s ostensible ‘realism’ turns out to be nothing of the sort.” (ibid.).

In her analysis of environmental movements in late capitalism, Naomi Klein (2014) has posited a similar critique. Whilst there has been an increased awareness of climate change and a substantial normative overhaul in media and political debate on the crisis of the environment (Castells 2009), there has also been an ongoing concern with ways in which to engage public response and resistance. A key issue has been the marketization of environmental concerns such as the notion that it is possible ‘‘buy yourself green” through more environmentally ethical consumption. This has, at one and the same time, attributed socio-economic status to environmentalism as an aspirational consumerist lifestyle whilst also individualizing the response. That is to say, the onus is on the individual consumer as the emblem of environmental resistance (Scott 2010). More broadly, Klein (2014) highlights the limitations of engaging with the environment as a question of moral conscience, or to frame it as an opportunity for competitive advantage in a market economy, instead of linking it more directly to questions of economic justice. ‘‘A different kind of climate movement”, Klein contends, ‘‘would have tried to challenge the extreme ideology that was blocking so much sensible action, joining with other sectors to show how unfettered corporate power posed a grave threat to the habitability of the planet.” (Klein 2014: 20).

In thinking about resistance to data-driven surveillance post-Snowden these concerns can inform a useful critique. Whilst there has been an increased awareness and important prominent developments in activism that has sought to challenge dominant trends of datafication (Milan and van der Velden 2016), resistance in the aftermath of Snowden has also been pursued through particular avenues. Most notably, these have been technological pursuits to self-protect against surveillance (what Milan and van der Velden refer to as ‘‘reactive” data activism) and lobbying around policy pertaining to privacy and data protection (Dencik and Hintz 2017). Forums to provide secure digital infrastructures proliferated in the wake of the Snowden leaks, with ‘‘numerous digital rights and internet freedom initiatives seizing the moment to propose new communication methods for activists (and everyday citizens) that are strengthened through encryption.” (Aouragh et al. 2015: 213). Increase in the use of privacy-enhancing tools such as the TOR browser, GPG email encryption, and encrypted messaging software such as Signal, indicate a rising consciousness and concern with surveillance practices. Alongside this, digital rights and civil liberties groups, such as Open Rights Group, Big Brother Watch, Article 19 and Liberty in the UK, have regularly issued statements regarding their concerns about surveillance, organizing public debates and lobbying legislators particularly around the Investigatory Powers Act and data protection regulation. This has been accompanied by litigation activism where groups have taken governments to court over particular policies, and made concerted efforts to change technical standards and protocols within relevant institutions and bodies, such as the Internet Corporation for Assigned Names and Numbers (ICANN) (Dencik et al. 2016).

These efforts have been significant in creating a contested environment for advancing mass data collection and for pushing back on certain surveillance practices. However, resistance of this kind has also struggled to challenge the wider social imaginary and provide a substantial threat to surveillance realism (Dencik et al. 2016). Partly, an issue with technological responses to mass data collection is the risk that they come to advance individualized understandings of resistance in which the onus is on the individual to change their own behavior. This means that challenging data collection becomes an individualized act based on perceived skill and ability to engage in privacy-enhancing digital practices, such as downloading encrypted software, using anonymised browsers, and changing security settings. Lobbying for policy reform and engaging in litigation activism, meanwhile, is often bounded by technical and issue-specific expertise that confines the debate to a small constituency of experts (Hintz and Brown 2017).

Moreover, resistance to data-driven surveillance following the Snowden leaks has often focused on trying to mitigate the excessive harms of datafication rather than questioning developments at a fundamental level. Gürses et al. (2016) highlight how this has led to digital rights campaigns centred on ‘targeted’ surveillance as a more benign alternative to ‘mass’ surveillance, and an emphasis on proportionality as the overarching goal. As Gürses et al. argue, this constitutes depoliticized framings that are unable to account for the ways in which surveillance has been historically central to the control of particular communities and as a way to limit and suppress dissent. Furthermore, such framings serve to entrench the constructed trade-off between privacy and security that underpins the surveillance realist narrative that mass data collection is, indeed, a necessary and inevitable part of contemporary society. Although these responses are shaped, in part, by what is perceived as possible, what is missed in these efforts is a form of resistance that explicitly highlights how datafication and data-driven surveillance relates to dominant economic interests and political agendas in advanced capitalist societies. These processes are neither accidental nor inevitable but serve a particular form of social organization. In such a context, identifying infringement upon individual privacy as the core harm produced by mass data collection may do little to reveal the power structures that shape digital infrastructures. And individual technological self-protection may do little to overcome or change them.

Rather, an analysis of data-driven surveillance as a ‘‘realism” invites a more systemic critique of datafication in which resistance is intimately linked to questions of social and economic justice; what has also been referred to as a ‘‘data justice” movement (Dencik et al. 2016). In advancing such a framework, the aim would be to situate data-driven surveillance in the context of the interests driving such processes, and the social and economic organization that enables them. For example, this involves engaging with the ways in which data collection and analysis embodies historical institutionalized forms of discrimination and exclusion that limits opportunity and participation for certain communities in society (Gangadharan et al. 2015, Eubanks 2018). Similarly, the asymmetries of power between those who collect and analyze data and those who are subject to such data collection and analysis as an inherent feature of datafication (Citron and Pasquale 2014), are seen as an expression of the increasing concentrations of power in fewer hands and related to a wider trend of privatization and deregulation (along with a shift in decision-making away from the public realm). Or, the corporate and centralized nature of data systems is understood in relation to the organization of the digital economy and the labour relations and governance that sustain it (Scholz 2017). In this sense, questions of economic and social justice precede any analysis and development of data infrastructures and their position in society.

Although there is not the space to outline it fully here, resistance, through this lens, would involve dynamic collaboration between different groups and movements in civil society that combine economic, social, cultural, ecological and technological dimensions in articulating both problems and solutions (see also Hintz et al. 2018). Pointing to surveillance realism in this context is therefore about inviting a more active politicization of data processes that, to borrow from Ricoeur’s (1986) assertions, articulates the ideological imagination as a way to nurture the utopian imagination (here I also take inspiration from writings on ‘‘real utopia” such as those of Olin Wright 2010). As Fisher notes, ‘‘nothing is inherently political; politicization requires a political agent which can transform the taken-for-granted into the up-for-grabs.” (2009, 79). Surveillance realism identifies contemporary (often undesirable) mass data collection as a contingency that has been actively constructed as an inevitability, which can therefore also be challenged and reconstructed.

Conclusion

In drawing on Fisher’s notion of ‘‘capitalist realism” as a way to understand the contemporary social condition with regards to data-driven surveillance, and the datafication paradigm more broadly, this article posits a way to reveal the contingency and construction of our current digital environment. The perceived necessity and inevitability of mass data collection is one that has been advanced partly through a compromised equilibrium, to use Gramsci’s term, in which the normalization and entrenchment of a surveillance culture has stabilized the nature of contemporary digital infrastructures as ‘‘common sense”. This, in turn, has established a social imaginary of resignation to ubiquitous data collection despite prevalent feelings of unease and recognition of discriminatory and suppressive effects. In a context of surveillance realism, the injustices and fallacies of the system become a small price to pay to fight off inefficiency, threats and terror.

The Snowden leaks constitute a significant moment in the advancement of surveillance realism. They provided unprecedented insights into the extent of datafication and created increased awareness of surveillance practices. This led to outbursts of resistance, which focused particularly on what we might think of as techno-legal solutionism. Such a response, however, has been unable to transform the social imaginary and pose a substantial threat to surveillance realism. Rather, it has struggled to overcome enclosed expert discourses and individualized acts of resistance that have been confined to mitigating the worst excesses of mass data collection, sometimes even advancing the inevitability of the model through its suggested compromises. The UK’s Investigatory Powers Act in the wake of the Snowden leaks has come to symbolize the re-assertion of the surveillance realist insistence that there is no alternative.

The inability to articulate a coherent alternative to surveillance culture and dataism speaks to the politics of imagination that is played out in the kind of ‘‘realism” that Fisher described. It is one in which our aspirations and hopes are formatted to fit the hegemonic system. In advancing a critique, therefore, suitable for an emancipatory politics, it becomes essential to destroy the ‘‘natural order” of surveillance realism in order to make what seems impossible attainable. That is, the challenge becomes one in which the issue is not simply to harvest the resources available to mitigate the excessive harms of the current datafication paradigm, but is one in which we have to expand the limits of our imagination and reassert the possibilities of another world, another way of organizing society.

Acknowledgements

The author wishes to thank two anonymous reviewers as well as Alex J. Wood for the very useful comments on earlier drafts of this article.

Introduction

The progressive datafication of social life is often perceived as a threat to democratic publics. Critics warn that filter bubbles would undermine dialogue and consensus, that social discrimination will be reinforced (Barocas and Selbst 2016) or that ‘surveillance capitalism’ is fundamentally anti-democratic (Zuboff 2015). Yet as “democratic power is calculated power” (Rose 1999, 200), datafication is also driven by democratic visions and closely linked to notions of political accountability, fairness, and citizen empowerment. Forms of datafication driven by commercial players or governments have always been accompanied by civil society actors or journalists who have utilized data and related forms of quantification to advance their goals. However, given the new structures of (data) power that shape the workings of governments and businesses today, Milan and Van der Velden (2016, 6) suggest that it is increasingly important to investigate “how activism evolves in relation to big data”. They argue that new forms of ‘data activism’ are “enabled and constrained by data […] and this special relation shapes tactics, identities, and modes of organizing” (Milan and Van der Velden 2016, 3).

In this article, I explore ways in which activism is enabled by datafication by looking at a group of actors who not only reacted to processes of datafication, but pro-actively embraced them: civic technologists. Civic tech is an umbrella term for diverse projects that attempt to make engagement easier for citizens, improve communication and feedback between governments and citizens, and strengthen political accountability. Among other things, civic technologists develop parliamentary monitoring websites, tools to help citizens report local infrastructure problems to local government, or freedom of information (FOI) websites that help users to submit freedom of information requests to public institutions. In its modern incarnation, civic tech is the result of a convergence between “communities of technological and political openness” (Yu and Robinson 2012, 195). Early examples include the British FaxYourMP (2003), which helped citizens to find and contact their representatives in UK parliaments (Townend 2008), or the monitoring website GovTrack.us (2004), which made information provided by the US Congress more accessible (Yu and Robinson 2012). From these early volunteer experiments, civic tech has grown substantially in recent years as it has been embraced by governments, corporations and foundations (Baraniuk 2013).

In addition to their success and growth, civic technologists are relevant because they act as pioneers for the use of data to facilitate civic engagement. While there have been predecessors of the tools they developed, the way they utilized data to make them accessible and offer additional services was novel. They took information that was available elsewhere and made it machine-readable, shared it openly, and built services on top of it, e.g. the ability to type in one’s post-code to find one’s representative in the British parliament on FaxYourMP. Through such practices, they combined concepts of legal and technical openness in new ways (cf. Yu and Robinson 2012). Being pioneers of this type of work is also part of the self-conception of at least some civic tech organizations. They possess a sense of mission and make teaching their (data) skills and experiences to other civil society or media organizations an important part of their work.[1] Finally, non-profit civic tech organizations such as the Sunlight Foundation in the US or mySociety in the UK were also among the first to advocate for open data policies, and supported related freedom of information policies (Schrock 2016).

However, despite civic tech’s success and potential influence, it has received little attention in media and communication studies to date. Most of the research that does exist is interested in how the phenomenon might reinforce existing power structures (cf. Gregg 2015), or who is using civic tech applications and in what way (cf. Cantijoch, Galandini, and Gibson 2016). What is missing is a nuanced understanding of the practices, ideas and motivations that guide civic technologists themselves and how those practices and ideas provide orientation for others. Critically examining civic technologists themselves is relevant because the broader impact of civic tech goes beyond the impact of individual civic tech applications. As Hepp (2016, 919) describes, pioneer communities such as civic technologists are influential in the sense that they develop “a horizon of possibility to which the everyday media appropriation of others orients itself, or at least can do so” (Hepp 2016, 919). Accordingly, we also need to be sensitive to how the practices and imaginaries civic technologists develop provide orientation for others, i.e. how other actors adapt and modify them.

This paper therefore critically examines how civic technologists understand and use data to “meet their social ends” (Couldry and Powell 2014, 2). What are the key practices of civic technologists in relation to data and how do they themselves understand what they are doing? Addressing these questions will contribute to our understanding of how activism is enabled or constrained by datafication, as it provides a basis for subsequent studies to examine if and how these practices and imaginaries can be found elsewhere, and how they might have been modified.

I present findings from a case study about the British non-profit organization mySociety. Founded in 2003, mySociety is one of the oldest and most influential civic tech organizations and arguably represents ‘best practice’ in the extremely diverse civic tech sector. Its UK websites have millions of users (mySociety 2015) and the organization had a direct influence on British policy-making (cf. www.parliament.uk 2014). Some of its more popular projects include FixMyStreet, which lets citizens report local problems like broken streetlights or potholes to local government; its right-to-know website WhatDoTheyKnow which helps users to submit FOI requests to public institutions; or its parliamentary monitoring website TheyWorkForYou which gives detailed information about voting records and makes parliamentary speeches more accessible. mySociety’s projects are also prominent internationally and customized versions of its tools are used in 44 different countries (mySociety 2015), which let mySociety to transition from a UK-centric to an international organization. While it is not representative of the phenomenon as a whole, mySociety’s success and international influence provide a good starting point for studying the values and practices which shape civic technologists’ use of data.

In the following, I shall first discuss my methodological approach. I follow a practice theory approach and use the concept of ‘imagined affordances’ (Nagy and Neff 2015) as a lens which helps amplify how members of mySociety themselves understand data and how they imagine it to advance their agenda. The remainder of the article describes how the data practices of mySociety relate to their broader imaginaries. In the conclusion, I will reflect on the implications for studying civic tech and data activism in general.

Researching the Imagined Affordances of Data

To examine how members of mySociety understand and use data to meet their own ends, this paper relies on the concept of ‘imagined affordances’ (Nagy and Neff 2015). According to Nagy and Neff (2015), whatever actions a particular technology enables or constrains does not solely depend on its features or its material properties, but also on the perception of users and designers. Both may have “expectations about their communication technologies, data, and media that, in effect and practice, shape how they approach them and what actions they think are suggested” (Nagy and Neff 2015, 5). Applied to the subject of this paper, what data enables civic technologists to do does not solely depend on the properties of the data they collect or re-use, or on the applications they are able to develop with it; it also depends on how civic technologists themselves understand and perceive how data can serve their agenda. These perceptions and understandings are the basis for the “horizon of possibility” (Hepp 2016, 919) they develop as a pioneer community, which affects the perceptions of other actors concerning how data can be used to facilitate forms of civic engagement and activism.

To study the imagined affordances of data for members of mySociety, this paper relied on a methodological approach inspired by practice theory. First, a focus on practices aligns well with the concept of imagined affordances because affordances enable or constrain certain actions, and people make sense of affordances “in and through practices” (McVeigh-Schultz and Baym 2015, 2). Second, practices are useful for examining the role of pioneer communities because they act as exemplars. This means that they not only communicate ideas and visions about how a technology can be used, but they also become influential because they demonstrate their own visions and thereby affect the perceptions of others. The practices they develop are expressions of their broader visions and we have to consider them inseparable if we want to understand their influence.

Accordingly, I employ methods that helped exploring what members of mySociety are doing and how they themselves understand and categorize what they are doing in relation to data (cf. Couldry 2004, 2012). I followed a constructivist grounded theory approach (Charmaz 2006). Grounded theory was chosen because of its core principle of theoretical sampling, i.e. an initial data sample is continuously expanded with new data to systematically elaborate and refine the theory. This approach was useful for exploring the open-ended range of practices (Couldry 2012) and to more fully reconstruct the perspectives of the research subjects without applying pre-conceived concepts.

Spread over three rounds of data collection, I conducted five semi-structured interviews with members of mySociety, including its founder and former CEO, two senior developers, a member of the international team, and a member of mySociety’s research team. These interviews had an average length of one-and-a-half hours. A large part of each interview consisted in the reconstruction of a particular project. Each interviewee was asked to pick a project that illustrates her or his work and then walk me through the development process: What was the initial idea behind the project, what were the different steps and phases for implementing the project, what happened after the initial release? Reich (2013, 422) calls this ‘reconstruction interviews’ because it reconstructs “technological ‘biographies’”. Exploring the development of a project in-depth vividly illustrated the practices and routines of my interviewees. If not mentioned by the interviewees, specific questions about the role of data were asked. After one project was explored, I asked whether this was a ‘typical’ project and if there are very different examples. If there were, I explored those as well. Other questions addressed self-understandings (preferred job title, understanding of civic tech) and personal or organizational ambitions and values.

These interviews were complemented by 17 documents found online: mySociety’s homepage (including the use of the Internet Wayback Machine to retrieve older versions), project specific websites, blog posts and forum discussions from Tony Bowden (who has been working for mySociety since 2009), other interviews given by different mySociety members to newspapers or bloggers (e.g. Townend 2008), as well as presentations given by members like Tom Steinberg or Dave Whiteland available online (UsNowFilm 2008; Arcopix 2014; IndigoTrust 2011; mySociety 2014). Some of these documents were included in the initial data sample, others were added later following the theoretical sampling. Moreover, I conducted ethnographic research on two separate conferences which were visited by several mySociety members: The Open Knowledge Festival 2014 in Berlin and the Mozilla Festival 2015 in London. These conferences helped to get an impression of the larger community mySociety is involved with and provided a helpful guidance for both the interviews and the analysis.

How mySociety Members Imagine the Affordances of Structured Data

In the following, I describe the imagined affordances members of mySociety hold around structured data. First, I will explore mySociety’s mission and self-understanding in more detail to give a dense description of the broader ambitions and imaginaries that drive this organization. Then I show how data is used to facilitate this mission by describing four imagined affordances: deep linking into documents to engage citizens with the processes of governments; making the performance of governments legible to affect how they implement laws and public services; affecting its users’ perceptions by demonstrating their impact to them; and scaling technological solutions to support a distributed form of agency.

mySociety’s Mission: Facilitating Engagement

mySociety’s self-proclaimed mission is to “help citizens demand better […] our web tools and apps are breaking down the barriers around governments” (mySociety 2016b). Its tools are supposed to give “greater access for citizens to the work of government and the democratic process”, which essentially means improving how publics can monitor and provide feedback to governments: “We believe that governments tend only to get better at serving the needs of citizens when citizens are capable of demanding better, creating a virtuous circle that leads steadily to better government” (Cridge 2015). While this statement might imply advocacy for specific policy changes, mySociety understands its role as a ‘mere’ facilitator of civic engagement, not as an advocacy organization.

Members generally reject the idea of being a gatekeeper that stands between the citizens and their governments. Instead, they suggest to provide the means by which others are able to take actions more effectively: “What we do is present the facts: This is how your MP [Member of Parliament] voted, this is where the money went, this is what was said. It’s then up to other people to do with that what they will, which might well be using it to promote a cause” (Interview: Research Team). The idealized and simplified scenario that members sketch out rhetorically to describe their role can be outlined as follows: Before mySociety enters the arena, citizens are apathetic and disengaged because engagement is too difficult and time-consuming due to high barriers raised by governments (in most cases unintentionally). mySociety identifies these barriers and then ‘drops’ its tools into the public arena to make engagement easier for citizens, which subsequently facilitates engagement between citizens and their governments and leads to better governance.

Underlying this approach is the assumption that more means for citizens to provide feedback to elected representatives leads to ‘better’ outcomes, i.e. more representative and therefore more democratic outcomes. Given its self-understanding as a ‘facilitator’, mySociety is not advocating for specific outcomes, but is concerned with the processes by which outcomes are generated: “We’re in favour of a vibrant, healthy, lively democracy. That means a rude and obnoxious place. Although we don’t want to do that ourselves, it’s entirely appropriate that we should facilitate other people to” (Steinberg quoted in Krotoski 2010). This approach builds on the principles of open source culture (Kelty 2008; Lewis 2012). “The essence of open source,” Weber (2004, 56) describes, “is not the software. It is the process by which software is created”. As the ideas and practices of the ‘open source process’ were increasingly applied outside of software development (most prominently with Wikipedia) they formed the basis for a larger technological and cultural phenomenon that Jenkins (2006) has described as participatory culture, a culture “which posits that knowledge is richest and most accurate when it reflects the pooled inputs of a distributed population, as opposed to the expertise of a single agent” (Lewis 2012, 847).

MySociety builds on previous forms of participatory culture and has particularly strong connections to technology-driven open data initiatives and rights-based open government or freedom of information initiatives, both of which are interested in applying the ‘logic of open participation’ (Lewis 2012) to institutionalized politics (Schrock 2016; Janssen 2012; Yu and Robinson 2012). mySociety was an early supporter of open data in the UK and its founder was part of a group that articulated the ‘8 principles of open government data’ (OpenGovData.org 2007). It also promoted freedom of information laws through its website WhatDoTheyKnow, which helps users to submit FOI requests to public institutions, and advocated for strong FOI legislations. Yet despite this strong connection, advocating for open data and FOI is not mySociety’s main purpose, they are rather perceived as “resources that mySociety needed to function” (Interview: Former CEO). This is because mySociety does not just build on participatory culture, it also extends it in important ways. Participatory culture relied on the connectivity of internet technologies to establish new forms of governance based on collaboration and sharing. As Lewis (2012, 848) describes, participatory culture is based on a forging of technology and culture, in that digitalization “enables greater user participation on a seemingly infinite order, and the socio-cultural context of this technology has encouraged greater participation to achieve normative aims of collective wisdom and well-being”.

MySociety similarly wants to create a more collaborative and participatory process for achieving better outcomes, but it does not solely rely on the connectivity enabled by internet technologies and ways of ‘coordinating collaborations’ (Kelty 2008). Its civic tech applications are not primarily about connecting people, but about facilitating them, i.e. enabling them to engage with governments in ways that go beyond ‘mere’ connectivity or access to information. In other words, mySociety is extending participatory culture by drawing attention to the conditions that would allow and encourage people to participate. Civic tech at mySociety is essentially about feasibility, in that it aims at making engagement more feasible for citizens by removing frictions such as needing to find out who represents them in parliament and how to contact them.

MySociety’s imagined affordances of data are closely tied to this broader mission of creating a participatory culture. To illustrate the fundamental importance of structured data for mySociety, a member uses the analogy of cooking ingredients:

If the useful thing is a cake that people want to eat, you’re interested in the ingredients…But you don’t want raw ingredients like wheat, you need the flour. Some processing has to be done to the ingredients before they are ingredients that you can sensibly make a cake with…Without the structured data, you wouldn’t be able to offer that service and until you can offer that service you couldn’t really prove that the demand for it would be so great. (Interview: International Team)

This suggests that structured data, if ‘served’ correctly, would increase engagement and subsequently alter the relationship between citizens and their governments because it shows that the ‘demand is great’. In the following, I unpack this statement and describe the imagined affordances of data underlying it.

Improving Engagement with Governments: Deep Linking

One of the most popular services on mySociety’s parliamentary monitoring website TheyWorkForYou are email alerts. For example, users can search for keywords in parliamentary discussions and then sign up to regularly receive emails informing them when their keyword comes up in future discussions. This service was significant because British parliaments used to publish transcripts of speeches as PDF files. To monitor keywords or what individual MPs are saying in parliamentary discussions, one needed to download these PDFs regularly and search through them individually. The email alerts at TheyWorkForYou turned this monitoring process into something people are able to do along the way, without investing considerable time and effort.

The key practice behind this service is the idea of ‘deep linking’: “The idea that there is councilor Jones who said ‘This is what we have to do in my home town!’ and you can cite it directly” (Interview: International Team) – similar to Twitter, where every individual Tweet has its own URL which can be shared or embedded on other websites. For deep linking, mySociety (2016c) considers transcripts “made of nicely structured data […] hard to beat”. If documents are in a format that does not allow deep linking, “you can’t cite, you can’t share, you can’t show specific utterances […] that pretty much stops details in documents being called out in debates” (Whiteland in mySociety 2016a). Today, it uses a data standard for modeling parliamentary speeches called Akoma Ntoso. It allows granular filtering (everything this person has said), an analysis of the speakers’ behavior (what was said, how and when?), and the ability to link speakers and what they say to other statements and events: “We would like to see people…build sites like ‘all public statements by the Prime Minister’” (mySociety 2016c). This creates a level of “semantic understanding” with an “awareness of speakers” (Interview: International Team) that is necessary for the services mySociety develops.

The ability to link deeply into a document using structured data is considered “really important in public discourses about documents” (Interview: International Team). More fundamentally, documents are considered important because mySociety is “concerned with the process of government and most government […] is actually the business of making laws, and laws traditionally have been written down” (Interview: International Team).

From the perspective of mySociety members, enabling deep linking into documents and providing services around it improves the public’s awareness of, and engagement with, the businesses of governments (as those businesses are captured in documents). As mySociety explains:

Transcripts are a kind of oil that greases the wheels of well-functioning societies. They let people discover when powerful people have made pronouncements that affect less powerful people. We believe that by making transcripts function better, more people will end up learning about decisions and opinions that affect their lives. (mySociety 2016c)

In the interviews, members also frequently referred to a claim by mySociety’s founder: “everything you can and cannot do in your life has been decided by more powerful people in a meeting” (cf. Whiteland in mySociety 2016a). Deep linking is about improving access to such meetings to help figure out “who was responsible for things” (Interview: Former CEO) by improving the accessibility of those documents which capture what was said by whom. Put simply, deep linking should help to keep track of where and when powerful people make decisions.

Taken together, mySociety is imagining the affordances of structured data in this case as a basis for a document-driven monitoring tool to help engage the public with the businesses and decision-making processes of governments. Through deep linking, structured data is imagined to “even [affect] an apathetic population, it affects the way that they behave and what they know about what’s going on in their own society” (Whiteland in Arcopix 2014). This makes turning documents into structured information a central part of mySociety’s mission of creating a more participatory culture. Importantly, deep linking is more likely to facilitate individuals who are already highly engaged and interested in the processes of governments to begin with, either privately or professionally (e.g. journalists or activist groups). As I will discuss below, mySociety also imagines data to help engaging citizens who are not necessarily interested in politics or feel disengaged and powerless.

Mediating between Governments and Their Publics

On FixMyStreet and WhatDoTheyKnow, mySociety collects all the problem reports or freedom of information requests by its users, tracks the responses by public institutions, and makes both publicly accessible. By making the resulting databases public, mySociety creates new forms of legibility and assessability. The database on FixMyStreet enables the analysis of a city’s infrastructure problems by the public and makes the performance of governments, e.g. how fast they fix problems in specific regions, legible. WhatDoTheyKnow similarly allows an assessment of how different public institutions respond to FOI requests and subsequently how the law is being practiced.

One reason for mySociety to ‘aggressively’ impose transparency on governments is its reliance on their cooperation. Its applications are built on top of services provided by governments and cannot exist independently without them. If public institutions refuse to cooperate, for example by ignoring reports sent via FixMyStreet, their tools would be of little or no use. By imposing transparency and allowing the public to assess their performance, mySociety makes it more difficult for authorities to ignore them. It is a way of pushing institutions to cooperate and to adopt mySociety’s emphasis on user-friendliness and accessibility (see below). This is described as “the one bit of activism that we occasionally engage in” (Interview: Research Team).

A less obvious aspect of this ‘one bit of activism’ is how mySociety’s use of data enables it to mediate between the bureaucratic and legal processes of governments and the users of its applications. The way mySociety’s tools work is usually not a direct reflection of how public institutions or legislations are working:

We think about the aim of the software as not being necessarily to model exactly the processes as they already exist in the world, but to make the software embody a slightly better way of doing things. (Interview: Senior Developer 2)

In this sense, mySociety’s tools are “intended to provoke some friction” (Interview: Senior Developer 2). In the cases of FixMyStreet and WhatDoTheyKnow, the laws and regulations did not prohibit the publication of problem reports or FOI requests, but it also was not something that was suggested. In addition, mySociety reinterprets how services and laws should be implemented in smaller ways, for example by forwarding reports to councils via email, while councils prefer people to use a form on their website; or by allowing users to send FOI requests to institutions that were not subject to the FOI law on WhatDoTheyKnow (mySociety 2011).

MySociety developer Tony Bowden (2010) described this principle as ‘dreaming out loud’: “simply act the way you want the world to be, then wait for reality to catch up” – by finding ways to make institutions cooperate and push ‘reality’ in that direction. This ‘dreaming out loud’ principle fundamentally depends on mySociety’s use of data to make the performance of governments legible and assessable for the public.

However, mySociety cannot simply invent new ways of how governments should work and then impose this vision on institutions. While they are intended to create frictions, the software tools and data structures mySociety develops need to reflect the real-world processes they are supposed to represent to a large enough degree “to actually work” (Interview: Senior Developer 2). Instead of ‘reinventing’ government, mySociety’s tools are compatible with existing services and legislations, but simultaneously utilize data to asses and affect how they are being implemented: “that gap between the way the law works, the way institutions implement the law and the way perhaps it might be ideal from a citizen’s point of view is an important one and one where I think it’s significant to build an artifact that demonstrates that” (Interview: Senior Developer 2). Using data to demonstrate and potentially close this gap is mySociety’s definition of ‘empowerment’: people are empowered by giving them tools which enable them to “see and be able to do what they are legally entitled to as easily as possible” (Interview: Research Team).

This understanding of empowerment encapsulates how mySociety imagines to affect the relationship between citizens and their governments through data. It uses data to facilitate the use of preexisting rights and services, not to fundamentally change them. It envisions its tools to act like a ‘layer’ that translates the bureaucratic and legal procedures followed by public institutions into user-friendly interfaces with accessible language for citizens. They are supposed to both reflect existing processes and visions of how these processes should ‘ideally’ work. The legibility and assessability created by data is imagined by mySociety to affect the implementation of rights and public services in ways intended to make engagement easier for citizens and thus ‘push reality’ closer to its vision of a more participatory culture.

Changing Perceptions: Providing a Sense of Agency

While facilitating already interested users via deep linking is important to mySociety (see above), it ultimately aims at engaging people who are usually not engaged. mySociety aims at a more long-term change in perception about “what is normal rather than what is exceptional” (Interview: Former CEO). Using TripAdvisor as an example, the former CEO explains that it “has caused a massive power shift in the hotel industry from the people who run the hotels towards people who stay in hotels”, even though it is not a “campaign for better hotels”. Services such as TripAdvisor would change people’s expectations about things like going to hotels if they are popular enough, i.e. widely used (cf. Steinberg in IndigoTrust 2011). In a similar vein, mySociety wants its tools to be “popular rather than idealistic” (Interview: Former CEO) and has a strong emphasis on usability, i.e. on making sure its tools are easy to use and provide “the same level of service that the best [commercial] websites we use nowadays would have” (Interview: Senior Developer 1).

In order to simultaneously achieve popularity and promote a more participatory culture, mySociety tries to ‘harness self-interest’ (Bowden 2014b) by generating “public good from private desire” (Steinberg in Nestoria 2008). It aims at developing services that address individual end-users, but use the data gathered from the engagement of these users to create “public value” (Steinberg in UsNowFilm 2008) on top of it. Crowdsourced databases such as those created on FixMyStreet or WhatDoTheyKnow are key to this. FixMyStreet helps individuals to fix their specific problem, but it also collects all the reported problems to create a public database about local problems that can be useful for others, for example local journalists who can sign up to get email alerts for problems reported in a specific region.

By bridging individuals with collectives in this way and by emphasizing ease-of-use, mySociety ultimately wants to affect the perception of users about their own agency. For example, when a problem reported via FixMyStreet has been fixed, it will be removed from the public city map and the user who reported it will receive an email encouraging her to report more, or to try out mySociety’s other projects for contacting her representatives. This way, mySociety is not only solving an individual’s problem, but is also trying to demonstrate the public value created by its action. This is intended to give users a “sense of agency…some ability to change their environment” (Interview: Senior Developer 2) by demonstrating that their actions do have an impact:

[W]hat we often see as apathy is really just learned helplessness. People feel powerless, because they don’t believe they can make a difference. And the best way to change that is not to argue with them […] It’s to simply show them that they do actually have power. That what they do can have an effect, not only for themselves but for people around them. (Bowden 2014b)

Here, the legibility and assessability of crowdsourced data is imagined to create a more abstract, cultural and psychological change in perception.

Taken together, the crowdsourced databases mySociety creates are imagined to affect both governments and the public. On the one hand, they enable mySociety to push governments and influence how laws and public services are being implemented, as they make the performance of governments legible in new ways (see above). On the other hand, it uses the data generated by its users in ways intended to change how they feel about engagement by providing easy-to-use tools and by demonstrating that their actions had an impact. Databases are imagined to affect emotions and perceptions in order to advance a vision of a more participatory culture, which illustrates the importance of emotions and perceptions around data (cf. Kennedy and Hill 2017). As I show in the next section, data is also key to mySociety’s ambition to support the development of similar applications elsewhere.

Scaling Civic Tech: Supporting a Distributed Form of Agency

The bulk of mySociety’s work today is based on collaborations with groups in other countries to create national versions of projects originally created in the UK, especially Alaveteli (an international version of its right-to-know site WhatDoTheyKnow) and tools for parliamentary monitoring. In part, this international orientation is driven by funding opportunities, but also because numerous groups in other countries expressed a desire to have customized versions of mySociety’s UK tools. While partly self-interested, members share the desire to support groups that similarly want to promote a form of participatory culture elsewhere. They think of themselves not merely as ‘tool suppliers’, but as part of an international civic tech community.

MySociety essentially wants to promote its values and practices in many different contexts by supporting local groups with developing customized versions of its tools. To achieve this, it has to accommodate the fact that “people in different places care about different aspects of politics. In some countries what really counts is how politicians vote, in others the crux is campaign finance contributions” (Steinberg 2012). Data is seen as both a key problem and a solution to this approach. One of the main obstacles for transferring an existing application to a new country or for building new civic tech applications from scratch is the design of consistent data models that adequately reflect the structures and legislations of the respective national government. A data model designed to capture the British parliamentary system cannot simply be transferred to another country. If they are not already available (as open data), developing such data models is complex.

Therefore, mySociety is one of the founding members of Poplus, a ‘global civic tech federation’ of organizations similar to mySociety, like Code for America in the US or g0v in Taiwan. The basic idea that drives Poplus is that “pretty much every tool in the civic and democratic space can be broken down into some parts that are universal, with usually only a little bit of local glue holding them together”:

Although pretty much every Parliament has different processes, they’re still dealing with the same raw ingredients – people, parties, speeches, bills, votes, etc. – and if we could create standardised ways of modelling each of those things, it would be a lot less work for people to combine these in the way that makes most sense for their own situation. (Bowden 2014a, emphasize added)

In connection to Poplus, mySociety supports data standards for modelling government structures and develops EveryPolitician, a project that literally collects data about every politician in the world. In this project, mySociety makes “data editorial decision[s]” (Interview: International Team) about how the ‘basic elements’ that can be found in every government are represented in a consistent manner across countries to ensure tools can be easily deployed internationally.

On a technical level, mySociety is trying to reduce problems of scale through data structures and tools that standardize how common ‘ingredients’ of government systems are formalized. However, members perceive this not merely as a technological detail, but as a way to facilitate mySociety’s vision of a more participatory culture elsewhere. By enabling other groups with similar values and social imaginaries to create their own, local versions of civic tech applications that serve their particular needs, mySociety is essentially trying to support a distributed form of agency. By promoting data standards and reusable tools, it tries to create the conditions necessary for supporting its values and approaches in as many different contexts as possible.

Conclusion: The Cultural and Historical Situatedness of Affordances

This article examined the imagined affordances members of mySociety hold around data to gain a deeper understanding of how civic technologists rely on data to meet their own ends. mySociety is trying to facilitate civic engagement and, by extension, create a more participatory culture. Taken together, it is imagining the affordances of structured data in ways that are supposed to expand the agency of publics towards governments: ways intended to enable citizens to better influence and interact with governments or other powerful institutions. Four imagined affordances have been identified.

First, members of mySociety use structured data to make ‘deep linking’ into documents possible, thereby aiming to increase the publics’ awareness of, and engagement with, the processes of governments. Second, crowdsourced databases enable mySociety to mediate between governments and their publics. By collecting data to monitor the performance of governments, and by making this data publicly available, mySociety is trying to push governments to cooperate with its services and thereby affect how existing laws and public services are being implemented in supposedly more user-friendly, i.e. ‘citizen oriented’ ways. Third, members of mySociety are trying to use data to affect the perceptions and self-understandings of their users. Crowdsourced databases are supposed to help connect the individual with the collective by demonstrating to individual users that their actions have an ‘impact’ and create a kind of ‘public value’. Fourth, members of mySociety are scaling technological solutions to support a distributed form of agency that should enable groups in other countries, which similarly want to promote a more participatory culture, to develop customized versions of mySociety tools that serve their particular needs.

The way mySociety is using data to apply notions of participatory culture to politics suggests that we should understand the role of civic technologists in the public arena as facilitators of engagement. Facilitating means that data is used in ways that are supposed to enable others to take actions themselves. mySociety does not advocate specific outcomes (such as particular policy changes), but is concerned with how the processes by which outcomes are generated are designed. It aims to enable users to easily engage with authorities in order to ensure that decision-making “reflects the pooled inputs of a distributed population” (Lewis 2012, 847). In other words, mySociety wants to influence the conditions by which others participate in the public arena without directing the public discourse or influencing policy making. At the same time, it sees its role as complementing actors who do want specific outcomes, like advocacy groups within civil society, or professional journalists who emphasize gatekeeping.

By showing how members of mySociety understand and use data to facilitate civic engagement and realize a more participatory culture, this article draws attention to the fact that “the political and democratic possibilities of data” (Milan and Van der Velden 2016, 8) cannot be determined in an abstract way. What data affords to whom does not only depend on the technological properties of data, but is fundamentally social, and both culturally and historically situated. This has implications for studying data activism in general and civic tech in particular. Related to data activism, the findings presented here invite us to extend Milan and Van der Velden’s (2016) conceptualization of data activism as creating novel epistemic cultures around datafication within civil society. The epistemic cultures and related social imaginaries promoted by mySociety are not novel: the practices and imaginaries described here build on participatory culture, which itself has roots predating computer culture and notions of open source software (Tkacz 2012). mySociety’s practices and epistemologies did not appear out of nowhere and the epistemic cultures they create are not necessarily novel, but they develop imagined affordances around data to implement these ideas in new ways. To understand imagined affordances, historical trajectories are just as important as the new elements added by data activists.

Studying imagined affordances does have a lot of potential for further developing and refining notions of data activism. First, imagined affordances provide a useful angle for moving beyond broad dichotomies of pro-active and re-active forms of data activism (Milan and Van der Velden 2016) and for studying how the epistemic cultures developed by data activists are connected to data in nuanced ways. Especially when combined with a focus on practices, it is a powerful tool for illustrating the “distributions of agency and organising forces” (Tkacz 2012, 404) activists set in motion. Second, the article shows how imagined affordances can be used as an integrative framework for studying how data activists affect the distribution of knowledge and power. I described how mySociety is trying to utilize the legibility and assessability created by data to change the perceptions of users about their own agency. Subsequent studies could examine how the practices and social imaginaries of data activists interrelate or clash with the self-understanding and perception of different groups in the public arena, and how different imagined affordances by various types of users emerge around civic tech applications.

Applied to civic tech, the research approach developed here calls for a more differentiated picture of the civic tech sector. Civic tech organizations and their funders are very much interested in a “coherent and clearly articulated vision and sense of shared identity for civic tech” (Donohue 2016). Yet while the diverse actors in the field of civic tech might align inasmuch as they are all interested in open data, reusability and a vague sense of improving ‘civic life’, their interests might eventually clash. For example, European non-profits like mySociety or the Open Knowledge Foundation Germany advocate for governments to copy civic tech applications. At mySociety, this even goes as far as stating that most mySociety projects “shouldn’t need to exist at all” (Interview: Senior Developer 1). Elsewhere, civic tech is closely aligned with start-up culture and attempts to create new industries. Wanting public institutions to copy civic tech applications in order to change the relationship between citizens and these institutions, or favouring the ‘government as a platform’ paradigm (O’Reilly 2010) and wanting public institutions to ‘step back’ and foster ecosystems of for-profit services, has very different implications for civic life (Baack in DataDrivenJournalism.net 2016).

Pointing out such differences is also important for developing a more differentiated critique of civic tech. Civic tech and open data initiatives have been said to uncritically support a neoliberal agenda driven by commercial interests rather than government accountability or citizen empowerment (cf. Bates 2012; Slee 2012; Gregg 2015). Moreover, their focus on technological solutions would merely reinforce existing power relationships by ‘empowering the empowered’ (Gurstein 2011). While this critique is important, it does not equally apply to every actor in the civic tech sector. We should not discard the agency and intentions of non-profit organizations such as mySociety, who are aware of these discussions and conduct research to better understand the impact of their applications. Members of mySociety constantly experiment and explore ways to support and encourage civic engagement. In this sense, they are “potential ‘laboratories’” (Hepp 2016, 929) that we should not ignore if we want to formulate normative principles for making processes of datafication more democratic and for creating more self-aware and agentic publics (Kennedy and Moss 2015).

The imagined affordances identified and described here can help to map differences among actors in the civic tech sector: Are other civic tech organizations relying on data in the same way, and do they promote similar ends with them? Are actors outside the field of civic tech adopting them, and in what way? Which actor in the civic tech sector influences what field? Given civic tech’s status as a ‘pioneer community’ (Hepp 2016), tackling these questions is one way to conduct empirical research to trace and understand how forms of civic engagement and activism are changing due to their growing reliance on data.