Just as data wasn’t always “big,” it wasn’t always cheap enough to accumulate like giant fatbergs in AWS’s digital sewers (data is the new fatberg). Governments, corporations, and institutions have long collected large data sets and wielded them as a tool of power, but those data weren’t nearly as interconnected, accessible, or easy to analyze as they are today. The transformation of data into “cheap data” required massive computing power, algorithmic accuracy, and cheap storage. Each of these was built on the backs of other cheaps: cheap energy (from fossil fuels), cheap money (often from Silicon Valley), cheap labor, and cheap nature (in the form of extracted minerals and metals) were all enlisted in the development of powerful and omnipresent computing technology used to transform data from just a collection of info points into an omnipresent strategy for profit making. This litany of enabling conditions didn’t conjure cheap data into existence. But I suspect that they created an imaginative fissure through which a new frontier could be glimpsed.
This touches on the idea of technology as a system, with a part of this system being cheap work.
At the cheap data frontiers, industrial workers (cheap labor) like those working in Amazon fulfillment centers are tracked and monitored, doing double time for employers who profit from their labor while also accumulating screeds of data about the movement of their bodies in space, their time spent per task, and their response to incentives. Friends and families provide uncompensated but necessary social support (cheap care) for one another on digital platforms like Facebook, helping maintain social cohesion and reproducing labor forces while also producing waterfalls of valuable data for the platform owners. This magic trick, where cheap data is gleaned as a byproduct of different kinds of cheap work, is a great coup for capital and one more avenue for extraction from the rest of us.
These demands on cheap work also bring with them further costs to employees who wear the mental costs.
Recent research has highlighted the stress and horror experienced by precarious workers in the digital factory, who annotate images of ISIS torture or spend their days scanning big social platforms for hate speech and violent videos. As with all cheap things, cheap data relies on massive externalities, the ability to offload risk and harm onto other people and natures, while the profits all flow in the opposite direction.
All in all, Pendergrast calls calls for a review of data collection, with a focus on small data and sovereignty.
These demands that Indigenous peoples retain sovereignty over their own data, refuse to let it be stored by AWS or reused without their consent, and re-inscribe it with Indigenous principles point towards an alternative data future in which data is slower, smaller, and less alienated. In this future, some kinds of data collection and use may be abolished entirely, as Ruha Benjamin suggests for algorithms and surveillance that amplify racial hierarchies; while other kinds of collection may continue, but in a less-networked way that is controlled and decided by the communities to whom the data pertain.
John Philpin frames this all around .