As the cultural and media industries have developed into 21st century forms, where large aggregates of personal information (behavioural data) is mined in order to find patterns of correlations so that individuals and target groups can be identified, me and my co-author Göran Bolin explore some of the foundational heuristics that businesses have to rely upon.
We begin by contrasting 20th century audience statistics with those of the 21st century. 20th century intelligence on mass media audiences was founded on representative statistical samples, analysed by statisticians at the market departments of media corporations.
In the 21st century, an age of pervasive and ubiquitous personal media (e.g. laptops, smartphones, credit cards/swipe cards and radio-frequency identification), techniques for aggregating user data build on large aggregates of information (Big Data) analysed by algorithms that transform data into commodities.
While the former technologies were built on socio-economic variables such as age, gender, ethnicity, education, media preferences (i.e. categories recognisable to media users and industry representatives alike), Big Data technologies register consumer choice, geographical position, web movement, and behavioural information in technologically complex ways that for most lay people are too abstract to appreciate the full consequences of.
The data mined for pattern recognition privileges relational rather than demographic qualities. We argue that the agency of interpretation at the bottom of market decisions within media companies nevertheless introduces a ‘heuristics of the algorithm’, where the data inevitably has to be translated into social categories.
In the paper we argue that although the promise of algorithmically generated data is often implemented in automated systems where human agency gets increasingly distanced from the data collected (it is our technological gadgets that are being surveyed, rather than us as social beings), one can observe a felt need among media users and among industry actors to ‘translate back’ the algorithmically produced relational statistics into ‘traditional’ social parameters. The tenacious social structures within the advertising industries work against the techno-economically driven tendencies within the Big Data economy.
Bolin, G. & J. Andersson Schwarz (2015). Heuristics of the Algorithm: Big Data, User Interpretation and Institutional Translation. Big Data & Society, 2(2): 1–12. DOI: 10.1177/2053951715608406