When data is capital: Datafication, accumulation, and extraction - Jathan Sadowski.

Image: Johnny Mnemonic directed by Robert Longo (1995).

 

Sadowski prefaces this article with fact that companies appear to be clamouring to collect data. Data whilst not the same as profit can share a similar rationale. Just as we expect these companies to be driven by profit, this is begat from the data they access. Data is a 'foundational form of capital' encompassing everything from the concept of the smart home, town planning and urban design, consumer devices and enterprise systems. 

The act of gleaning and storing information about people's everyday activities has become normal practice and it shifts how we interact and experience the world; it also places data capital in a position of authority. There is a feedback loop whereby many control systems are predicated on the continual gathering and processing of raw data in order for yet more to be generated in its place. Implications include; "subjecting previously non-commodified and non-monetised parts of life to the local of capitalism," or colonising new 'territories'. This is a continuation of imperialist endeavours which has been insidiously updated for the digital age. 

In Marxian terms, the cycle of capital is 'motivated by exchange-value' and this is - much like the feedback loop - is not complete because capital is requires continuous flow. 'Constant capital' is the 'means of production for commodities' (factories, raw materials, machinery, equipment). Variable capital is the means of subsistence for labour power (the cost of procurement, retention of workers, etc). Pierre Bourdieu introduced other versions of capital that orbited around these notions; cultural capital and social capital:

Cultural capital pertains to person's status success in ways that transcend the idea of a 'human capital' and is intrinsically connected to class and transmitted by a person's family and domestic environment. Social capital is linked to the 'possession of a durable network of more or less institutionalised relationships."

Data is commodified through the digital labour of people posting on Facebook, clicking on Google, wearable fitness trackers and anything else that generates the same wealth of data. There is evidence that industries can benefit from access to this information and shape the data to fit their own motivations and remit. Efficacy, biometrics and geolocation can also be useful in tracking people in their employ.

Data can also:

- Profile and target people: collation of data to create 'categorise and individuals into market segments.' Retailers can be nimble and adjust product and prices based on their customer characteristics and purchasing patterns.

- Optimise systems: minimise waste, improve productivity, trim costs and boost efficacy. The 'uberisation' of employment - rideshare and food-delivery partners delivering from 'ghost restaurants'.

- Manage and control things: by amassing data about something allows control of that medium. Body-worn cameras, drone equipments, licence-plate reader software are all given as examples. 'Find your friend' is an iPhone app which by surrendering your number to a friend or family member allows you to be tracked by that party. 

- Model probabilities: 'predictive' systems, like those used by law enforcement can be used to compile 'heat lists'. Urban control rooms allow similar powers to monitor passenger transport, traffic flows in the effort to provide efficient incident response. 

- Used to build stuff - Uber and other ride-share applications would not exist without having access to real-time data about drivers and its passengers. 

When companies seek record, appropriate, or sell our data it is typically done in the form of a contract - typically the End-User Licensing Agreement. By not agreeing to these terms by ticking yes, your access would be revoked. When this contract is not provided, and your data is still used, we would typically define that as theft. Furthermore, when you do not receive a fair offer of remuneration for work undertaken that is classified as exploitation - both this and inequity are highlighted by Sadowski as the effects of data extraction.