[LINK] "Big Data and the Hyper-Reserve Army"

[rfmcdonald]

At the New APPS Blog, Gordon Hull writes from a Marxist perspective about one big problem with Big Data, particularly when connected to low-paid and low-status employment but not only. It makes it possible for people to be on call quite irregularly, getting hours dispersed so broadly that all other planning becomes impossible.

[T]his of course causes real, quantifiable increases in the levels of stress these workers face, since it makes it nearly impossible for them to juggle their (poorly remunerated) jobs, child care and other obligations. Such workers never had it easy, of course; on a slow day at the grocery store, you could always be sent home early (and without pay for the time you were scheduled but didn’t work). But this is something considerably more intense, I think, because it furthers the processes of real subsumption, where capital extends outside the factory walls and into all aspects of life. In the old way you could say with certainty whether you were at work, or not. Capital extended into the home removes this certainty.

One of the most discussed of such extensions is the direct extension of work into the home, as in the well-worn images of dads spending their entire time on the Blackberry, even during family dinners. At some point in that process, there is a further intensification: you find yourself not just doing one job all the time, but indefinitely many jobs in-between; the job morphs into what Ian Bogost calls hyperemployment (see also here and here). So for one segment of workers, it is impossible to stop working.

Scheduling-by-analytics shows the version of this process for lower socioeconomic strata. Marx had shown how capital depends on creating a “surplus population,” that then could serve as an industrial reserve army of contingent labor. Those workers would be called into the factory when there was extra work to do, and left unemployed and near starvation otherwise. Here we see the transformation of the industrial reserve army into something fitting the needs of post-industrial, service sector capital, abetted by analytics. Big data is very good at segmenting and regrouping formerly opaque-looking blocks of things – time, populations, etc. – and here we see it being used to precisely that effect, segmenting and reconfiguring the time of the working day to align that time as precisely as possible with the needs of employers.

In the factory system described by Marx, it is the steam engine that dictates time. In the contemporary service sector, it is predictions about customer traffic, and producing very granular predictions is the service the new scheduling software provides. The result is both an extension of work into the home that is almost the mirror image of the Blackberry dad, and also an intensification of the production of surplus population. The old Marxist surplus population had an excess of time away from the factory; the beleaguered service-sector employee can’t escape into her house or otherwise be away from work, not because she is working, but because she is not. In other words, the low-level service sector worker cannot escape work, even if she isn’t actually working, and even if she won’t actually be called into work, because the boss might decide at the last second that her services are required (temp workers have had to put up with this for a while, of course; the disturbing part here is these are workers who have “regular” jobs).