Big Data

The reality of today’s dividual data sets – enormous accumulations of data that can be divided, recomposed and valorized in endless ways – is one of worldwide streams, of deterritorialization and of machinic expansion, most succinctly expressed as Big Data. Social media such as Facebook need the self-division of individual users just as intelligence agencies continue to retain individual identities. Big Data, on the other hand, is less interested in individuals and just as little interested in a totalization of data, but is all the more so in data sets that are freely floating and as detailed as possible, which it can dividually traverse – as an open field of immanence with a potentially endless extension. These enormous multitudes of data want to form a horizon of knowledge that governs the entire past and present, and so is also able to capture the future.

The collection of data by economic and state actors, especially secret services, insurance, and banking industries, has a long tradition, but it has acquired a completely new quality with machine-readability and the machinic processing of the data material. This quality applies not only to credit-rating agencies or intelligence agencies, but also to all areas of networked everyday life, all partial data of individual lives, about children, divorces, debts, properties, consumption habits, communication behaviors, travelling habits, internet activities, movements in real space, whereabouts, health, fitness, eating habits, calorie consumption, dental care, credit card charges, cash-machine use, to name only a few. Refrigerators, ovens, thermostats, smart-guide toothbrushes, intelligent toilet bowls, networked offices, networked kitchens, networked bedrooms, networked bathrooms, networked toilet facilities – all controllable via smartphone, all accessible via cloud. This machinic data can potentially be combined, for instance for the logistics of individual thing-movements, and made accessible according to dividual logics.

In order to traverse, divide and recombine these data, cooperation is needed from those who were previously called consumers. Participation means the most comprehensible free (especially in the sense of unpaid) data exchange possible, not only sharing existing data, but also producing new data. Data valorization plays out in the terrain of externalizing production processes and activating consumers, as it has been intensified since the 1990s in all economic areas. Crowds, multitudes, dispersed masses – their modes of existence and living are captured, stretched, appropriated and exploited beyond the realm of paid labor. Scoring, rating, ranking, profiling. Consumers who are activated and generate value with their activity do not have to be paid. The open source model of program development by the crowd has meanwhile become established as a business model and spread to all economic sectors. Free labor in free association (as Marx once wrote), but to the advantage of the enterprises of the New Economy.

Everything is free, but one who does not pay is not a consumer but a product. The fact that this is now widely known hardly seems to open up opportunities for change in the modes of subjectivation. The daily work of the ‘users’ in the social network consists of adding more and more details to the image of themselves and their social environment and thus – posting after posting and like after like – creating an increasingly identifiable target for advertising messages. In the context of accelerated technological developments under conditions of monopolized access to data for a few corporations, and an increasingly exclusive focus on valorisation, new communication structures have emerged. Meanwhile, the bourgeois public has taken note of this with some horror, and under the slogan of ‘fake news’, as it became clear that the usual agnostics of valorization – be it advertising for billionaires with political ambitions, for soft drinks or EU-exits – becomes much more effective in highly efficient and at the same time less regulated and opaque structures.

Under similar auspices of intensified valorization, machine learning is developing, a recent trend that has led to a quantum leap in the development of statistical approaches to artificial intelligence, not least through the opportunities created by big data. The ‘intelligence’ of the software is no longer implemented according to abstract categories and/or sample data; the algorithms themselves (although at the moment still mostly ‘supervised’) generate their logical structures using patterns that they recognize in huge data sets. The advances in artificial intelligence usually accompanying debates on human and machine intelligence have now receded into the background in the face of the massive labor market problems that these technologies will cause in the given economic system.

All of this calls for a reappropriation of the present that carries us to the other side of dividual economy. How, then, can economy be envisioned as not based on individual property, on the dis/possession of each and every individual, but as using the abstract-dividual line to compose new forms of sociality? An economy that implies forms of distribution other than dividends as claims of shareholders: a dividend beyond the realm of measures and metrics, of modularizing and modulating, of number and code, where that which is to be distributed is not well-ordered by “common sense,” as the “best distribution,” but rather as an ever broader and wider distribution, spread, dispersion, proliferation of social wealth?