The ‘Big Data strategist’ for Oracle, one of the largest software companies in the world, has said, ‘Data is in fact a new kind of capital on par with financial capital for creating new products and services. And it’s not just a metaphor; data fulfils the literal textbook definition of capital.’ (OracleANZ, 2015). This statement points to an emerging political economic shift in which data is created, collected, and circulated as capital. The previous section described how data has been critically analysed in the context of capitalism – implicitly or explicitly – as a commodity. However, as businesses and government bodies begin treating data as capital, there is a need for examining the characteristics and dynamics of ‘data capital.’ This section aims to do so by first reviewing two theories of capital, from Karl Marx and Pierre Bourdieu, then using them to analyse data. In Capital, Volume 1, Marx describes capital as a relationship between money (M) and commodities (C); namely, the ways they circulate and transform, which he simplifies into two general formulas. The first formula represents consumption, C-M-C: a commodity is sold for money which is then used to buy another commodity. Therefore, C-M-C is the cycle of using money to turn one qualitatively different thing (e.g. labour power) into another qualitatively different thing (e.g. coffee). The cycle of consumption is motivated by the use-value of a commodity and it is completed when money is turned into a commodity. The second formula represents capital, M-C-M′: money is used to buy a commodity which is then sold for more money. ‘The value [of money] originally advanced, therefore, not only remains intact while in circulation but increases its magnitude, adds to itself a surplus-value, or is valorised. And this movement converts it into capital’ (Marx, 1990: 252). The cycle of capital is motivated by exchange-value and the cycle does not complete because capital requires continuous circulation. When money is turned into a commodity for consumption, rather than invested to make profit, it ceases to be capital. In addition to ‘money capital’ (i.e., invested funds), Marx distinguishes between two forms of ‘real capital’ employed in the creation of surplus value. Constant capital is the means of production for commodities (i.e., factories, machinery, raw materials, etc.). Variable capital is the means of subsistence for labour power (i.e., the costs of hiring workers). Expanding on Marx’s foundational analysis, Bourdieu theorized two new forms of capital that are distinct from what he terms economic capital, ‘which is immediately and directly convertible into money and may be institutionalized in the form of property rights’ (1986: 242). The other forms Bourdieu theorized, cultural capital and social capital, stand alone in their own right while also being ‘convertible, in certain conditions, into economic capital’ (p. 242). They are, at their root, ‘transformed, disguised forms of economic capital’ (p. 251). Cultural capital contributes to a person’s status and success in ways that go beyond the idea of ‘human capital,’ which focuses on monetary investment in education and skills. Cultural capital is a representation of class and tends to be invested by a person’s family and transmitted from a person’s domestic environment. Bourdieu (1986) identifies three types of cultural capital: embodied (e.g. character traits), objectified (e.g. art collection), and institutionalised (university degree). Social capital, according to Bourdieu (p. 248), ‘is the aggregate of the actual or potential resources which are linked to possession of a durable network of more or less institutionalized relationships of mutual acquaintance and recognition.’ This form of capital accrues by being included in privileged groups, whether that means being inducted through rites of passage (e.g. fraternal orders) or through rites of inheritance (e.g. noble lineage). When people talk about the value of ‘who you know’ and ‘networking,’ they are talking about having and developing social capital. Building from Marx, we can now frame two common analyses of data in terms of a debate about what economic form data represents. On one hand, data is cast as a digital raw material – constant capital – necessary in the production of commodities. It is hard to read media articles and business reports about data without seeing it called ‘the new oil.’ For instance, the cover of a 2017 issue of The Economist (2017b) proclaims ‘The World’s Most Valuable Resource’ above an illustration of offshore oil platforms labelled with the names of major digital platforms like Facebook, Google, and Uber presumably drilling into an ocean of data. On the other hand, data is cast as a commodity produced by the digital labour of people posting on Facebook, clicking on Google, exercising with Fitbits, and all the other things we do that create data and that data is created about (Fuchs, 2014; Till, 2012). The cliché about the ‘free’ services provided digital platforms is that, ‘If you’re not the customer, you’re the product.’1 Through the work of using platforms and devices, people are turned into commodities that take the form of personal data, which is sold to advertisers and data brokers. In the age of mass media, this was termed the ‘audience commodity’ (Smythe, 1981). Now with social media it is called the ‘data double’ among other coinages (Haggerty and Ericson, 2000). Therefore, at the risk of oversimplification, these two ways of analysing data – as raw material and as product of digital labour – can be recast as a debate about the relationship between real capital and commodities in the digital economy. Building from Bourdieu, I suggest a better framing of data is as a form of capital that is distinct from, but has its roots in, economic capital. Data capital is more than knowledge about the world, it is discrete bits of information that are digitally recorded, machine processable, easily agglomerated, and highly mobile. Like social and cultural capital, data capital is convertible, in certain conditions, to economic capital. But, as the next section ‘Deriving value from data capital’ shows, not all value derived from data is necessarily or primarily monetary. Data capital is institutionalised in the information infrastructure of collecting, storing, and processing data; that is, the smart devices, online platforms, data analytics, network cables, and server farms. Importantly, these characteristics of data capital mean it can be continually captured and circulated, thus data collection is driven by the logic of capital accumulation as described by Marx. ‘The circulation of money as capital is an end in itself, for the valorization of value takes place only within this constantly renewed movement. The movement of capital is therefore limitless’ (Marx, 1990: 253). This unending accumulation of capital, represented by M-C-M′-C-M″-C-M‴… , is a defining feature of capitalism. In digital capitalism, data is not a substitute for money, but is rather elevated and put ‘on the same level as financial capital,’ as a report by Oracle and MIT Technology Review Custom (2016: 2) states. The imperative, then, is to constantly collect and circulate data by producing commodities that create more data and building infrastructure to manage data. The stream of data must keep flowing and growing. Ultimately, continuing the cycle of data capital becomes an intrinsic motivation, a driving force, for firms. As Marx explains, ‘Use-values must therefore never be treated as the immediate aim of the capitalist; nor must the profit on any single transaction. His aim is rather the unceasing movement of profit-making’ (Marx, 1990: 254). The same can be said of data. The capitalist is not concerned with the immediate use of a data point or with any single collection, but rather the unceasing flow of data-creating. This point is illustrated by the fact that data is very often collected without specific uses in mind. Indeed, the practice of collecting data first and figuring it out later is increasingly a core part of how businesses and government bodies operate. ‘It does not matter that the amounts [of data] collected may vastly exceed a firm’s imaginative reach or analytic grasp. The assumption is that it will eventually be useful, i.e. valuable’ (Fourcade and Healy, 2017: 13). At a public talk in early 2017, Andrew Ng, an artificial intelligence researcher who has held top positions at Google, Baidu, and Coursera, was candid about this prevailing logic of data accumulation: ‘At large companies, sometimes we launch products not for the revenue, but for the data. We actually do that quite often ... and we monetize the data through a different product’ (Stanford Graduate School of Business, 2017). The conditions needed to convert data capital into economic capital may never arrive, but that does not stop the cycle of accumulation. The shift towards data capital takes advantage of the ideological and regulatory groundwork that has been laid since at least the 1980s to create a political economic landscape conducive to finance capitalism (Konczal and Abernathy, 2015). Under neoliberal governance, financial capital is treated as if it exists in transnational space beyond borders and governance (Major, 2012). The same attitudes are directly applied to data capital. This view was crystallised by Carl Bildt (2015), the former Prime Minister of Sweden and chair of the Global Commission on Internet Governance, in an op-ed for the Financial Times: ‘Barriers against the free flow of data are, in effect, barriers against trade.’ Bildt was rebuking proponents of ‘digital sovereignty’ rules in Europe, which would require non-EU companies to keep data about EU citizens in servers that are geographically based in Europe. Any restraints on the flow of data are said to hinder economic growth and technological innovation (Morozov, 2015). Trade deals like the Transatlantic Trade and Investment Partnership and the Trans-Pacific Partnership (aim to) enshrine the free flow of data across countries and continents (Selby, 2017). Like finance, data is now governed as an engine of growth. If financial firms are free to shuttle capital from country to country, then similarly technology corporations must also be free to store and sell data wherever they want. This means, for example, that a company could collect the personal information of Americans, store the data in Taiwan, and sell it in Europe (Rossiter, 2017). The focus on data and datafication should not be seen as usurping financialisation, but rather as adding new sources of value and new tools of accumulation. There is a long history of crossover between innovations in information technology and innovations in finance (MacKenzie, 2018). Far from being in competition with each other, Wall Street and Silicon Valley are converging around data capital as the new frontier of accumulation and circulation.