Big data problems we face today can be traced to the social ordering practices of the 19th century.

Hamish Robertson and Joanne Travaglia in LSE’s The Impact Blog: “This is not the first ‘big data’ era but the second. The first was the explosion in data collection that occurred from the early 19th century – Hacking’s ‘avalanche of numbers’, precisely situated between 1820 and 1840. This was an analogue big data era, different to our current digital one but characterized by some very similar problems and concerns. Contemporary problems of data analysis and control include a variety of accepted factors that make them ‘big’ and these generally include size, complexity and technology issues. We also suggest that digitisation is a central process in this second big data era, one that seems obvious but which has also appears to have reached a new threshold. Until a decade or so ago ‘big data’ looked just like a digital version of conventional analogue records and systems. Ones whose management had become normalised through statistical and mathematical analysis. Now however we see a level of concern and anxiety, similar to the concerns that were faced in the first big data era.

This situation brings with it a socio-political dimension of interest to us, one in which our understanding of people and our actions on individuals, groups and populations are deeply implicated. The collection of social data had a purpose – understanding and controlling the population in a time of significant social change. To achieve this, new kinds of information and new methods for generating knowledge were required. Many ideas, concepts and categories developed during that first data revolution remain intact today, some uncritically accepted more now than when they were first developed. In this piece we draw out some connections between these two data ‘revolutions’ and the implications for the politics of information in contemporary society. It is clear that many of the problems in this first big data age and, more specifically, their solutions persist down to the present big data era….Our question then is how do we go about re-writing the ideological inheritance of that first data revolution? Can we or will we unpack the ideological sequelae of that past revolution during this present one? The initial indicators are not good in that there is a pervasive assumption in this broad interdisciplinary field that reductive categories are both necessary and natural. Our social ordering practices have influenced our social epistemology. We run the risk in the social sciences of perpetuating the ideological victories of the first data revolution as we progress through the second. The need for critical analysis grows apace not just with the production of each new technique or technology but with the uncritical acceptance of the concepts, categories and assumptions that emerged from that first data revolution. That first data revolution proved to be a successful anti-revolutionary response to the numerous threats to social order posed by the incredible changes of the nineteenth century, rather than the Enlightenment emancipation that was promised. (More)”

This is part of a wider series on the Politics of Data. For more on this topic, also see Mark Carrigan’sPhilosophy of Data Science interview series and the Discover Society special issue on the Politics of Data (Science).

One Reply to “Big data problems we face today can be traced to the social ordering practices of the 19th century.”

  1. The 1820-40 era may have been the inventive period for big data, but it first burgeoned in the era around WW I, when the first Progessive Era collected data to transform into policy information that informed concrete reform. One of my favorite stories is when Charles A. Beard, having resigned from Columbia over the US entry into the war against Germany, hired students to collect information about fire plugs in New York City for the Bureau of Municipal Research, founded just before the war. They then realized that more fire plugs signified higher income neighborhoods….
    Big data have long histories.

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