To trace or map a worker’s data stream is to situate the worker in the context of the datafication of employment and social relations at large (consumption/communication/ work). We might understand ‘datafication’ as practices serving the purposes of dataveillance, targeted marketing, predictive analytics or algorithmic governance, for instance. These situate the worker at the intersection of vast ‘data assemblages’ or ‘data ecosystems’ in which the worker/user/consumer is connected to an array of technologies, databases, analysts and firms. Today’s digital economy is constituted by relatively novel infrastructures that mediate these relations, including the Internet of Things (IoT) and its industrial form, [1] (big) data analytics, and cloud computing.

• How might we implement an archaeology or critical anatomy of worker data
  and of data flows and assemblages?

• What tools (conceptual, technological) can be used to do so?

• What is the value of such critical mapping for an understanding of power in the economy today and for corresponding policy?

Such mapping aims at grasping the web of ramifications that link the worker, as the active or passive source of data, to vast systems, strategies, and infrastructures of data capture, processing, storage, circulation, and monetisation (data rent). It looks to render apparent the processes by which datafication instantiates particular power relations that lead to an increasing informational asymmetry between data collectors and datafied subjects, i.e. digital inequity based on the exclusion of subjects from ownership of the data extracted from them. Mapping also makes visible the processes and techniques that obscure datafication from being unserstood as a practice that captures or extracts data/value from workers/users and the labour processes that constitute it.

Mapping can reveal in visual form how data is taken from subjects through a highly valuable techniques and material infrastructures of capture that then exclude workers/consumers/users from its ownership, all whilst data constitutes the most sought after raw material in the digital economy. This is a digital form of ‘primitive accumulation’ rendered possible by processes of digital enclosures, like the black-boxing mechanisms of corporate secrecy borders and patenting.

By helping to highlight these mechanisms, and denaturalise our common sense understandings of data use, mapping can show that data is not ‘given’ nor ‘gratuitous’ nor ‘abstract’ (digital). Rather, it is taken (capta) and its capture, archiving and processing rely on material infrastructures and procedures with economic and environmental consequences. In this sense, the Data Archeogram represents only the beginning – at a necessarily low resolution – of what mapping the data economy can achieve for those seeking to understand and ultimately change the current system.

Armelle Skatulski is a researcher at the Royal College of Art and researcher at Autonomy. Her work focuses on the critical potential of the photographic documentation of work-related accidents in the analysis of the normalisation of risk at work. Her research considers the accident as an economic problem from a bio-political perspective and traces procedures that disqualify the abnormality of death in the context of work. As part of her partnership with Autonomy, Armelle has pursued a schematic approach to the flows of data and power from the worker, to the workplace, to the data infrastructure of extraction.