In a post yesterday, I expressed my discomfort with how Nick Srnicek invokes the notion of data as a raw material in his Platform Capitalism. In a footnote on loc 1102-1121, he offers a Marxist justification for this use:
I draw here upon Marx’s definition of raw material: ‘The land (and this, economically speaking, includes water) in the virgin state in which it supplies man with necessaries or the means of subsistence ready to hand, exists independently of him, and is the universal subject of human labour. All those things which labour merely separates from immediate connexion with their environment, are subjects of labour spontaneously provided by Nature. Such are fish which we catch and take from their element, water, timber which we fell in the virgin forest, and ores which we extract from their veins. If, on the other hand, the subject of labour has, so to say, been filtered through previous labour, we call it raw material; such is ore already extracted and ready for washing’ (Marx, 1990: 284–5, emphasis added).
The distinction between natural and processed helps address the charge that data-as-raw-material naturalises data but it doesn’t avoid it all together. It recognises that usable data is always processed and cleaned prior to use, ‘filtered through previous labour’ both directly (wrangling etc) and indirectly (socio-technical systems of generation, capture, storage and analysis).
But does it obscure the character of its applications? The original sources of this ‘raw material’ are intensely social, unlike that extracted from nature, arising from the digitalisation of action and transaction within a market economy. The applications of this ‘raw material’ are intensely variable, with data sometimes being a direct source of value that can be realised through a linear activity, other times being an informational good that can be used to coordinate and calibrate and perhaps more often being a source of confusion and speculation.
It would take constructionism too far to deny the ontology of data. Indeed, I think a concern for the ontology of data is a crucial vector through which we can understand the ideology of data. But we nonetheless need to carefully unpick the way ‘data’ is represented by social actors, the interests served by this and the discursive architecture through which these interests are advanced in different organisational settings.
There’s a fascinating example of this ideology which Srnicek quotes on loc 1296:
If collecting and analysing this raw material is the primary revenue source for these companies and gives them competitive advantages, there is an imperative to collect more and more. As one report notes, echoing colonialist ventures: ‘From a data-production perspective, activities are like lands waiting to be discovered. Whoever gets there first and holds them gets their resources –in this case, their data riches.’ For many of these platforms, the quality of the data is of less interest than their quantity and diversity. Every action performed by a user, no matter how minute, is useful for reconfiguring algorithms and optimising processes. Such is the importance of data that many companies could make all of their software open-source and still maintain their dominant position due to their data. Unsurprisingly, then, these companies have been prolific purchasers and developers of assets that enable them to expand their capacity for gaining information. Mergers relating to big data, for instance, have doubled between 2008 and 2013.
How seriously should we take this? My instinct is to say ‘not very’. The notion that all data is of potential value is licensing massive investment and expansion, entrenching the position of precisely those who are speaking most authoritatively in relation to this transition. To be sceptical of a gold rush doesn’t mean you’re denying the existence of gold, it just means you’re sceptical about the exuberance surrounding the search and how it is being deployed by some to benefit their own self-interest. The distinction between the ontology of data and the ideology of data matters.