Academic journal article Global Governance

What Do Big Data Do in Global Governance?

Academic journal article Global Governance

What Do Big Data Do in Global Governance?

Article excerpt

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IN RECENT YEARS, SO-CALLED BIG DATA HAVE RECEIVED A GREAT DEAL OF ATTENTION, but significance with regard to global governance has received much less. Big data build on the exponential growth of data from new sources such as Internet clicks or machine sensors. They stand in contrast to conventional databases bounded and then managed according to specified standards and for predetermined purposes. Big data can involve ongoing streams of data that are processed, analyzed, and immediately supplied to users, in contrast to periodic surveys that may take months or years to process. Three distinctive features of Big data as compared to more conventional data have been identified: volume, variety, and velocity (3Vs). (1)

In this article, we argue that big data are especially relevant to global governance in two ways that differ from more conventional uses of statistics and other data in international affairs. First, big data are linked to automated forms of governance. With big data the human element becomes more entangled with and secondary to the nonhuman objects that are involved such as computers and software. Big data increase the importance of algorithms for producing, managing, and using the data, and they are closely related to the Internet of things and its direct object-to-object communication. The dominance of nonhuman objects is relevant to global governance because these objects and their materiality stretch across national borders in ways that differ from the relationships between humans and objects associated with more conventional international relations. The spaces between nation-states are not empty. Big data rely on technologies to operate globally, reinforcing, extend ing, obscuring, and confounding power in new ways. It is important to understand these changes in power relations as big data expand in international affairs.

Second, and related to this, big data are associated with boundary issues that are not primarily territorial, but rather about access to and control of data. With big data, the clicks of Web users may be collected and put together with other sources of data and made ready for multiple uses not initially imagined by those who have generated and assembled them. The promise of big data involves bringing together previously separate flows of data coming from potentially everywhere on the planet. This development has special implications for national borders. It also has consequences for the construction of new boundaries, specifically between those whose data are included and those whose are excluded, and between those who have access to the data and those who do not. States and private corporations require the boundaries that enable exclusive control or rights over some part of big data, even if they also depend on erasing boundaries, including national ones, which hinder the assembly of the data.

In the remainder of this article, we start with a conceptual discussion of these two features of big data before examining each feature in more detail. We conclude by pointing to the need in future research to investigate in more detail the largely invisible power of those whose create and mobilize big data, including their taken-for-granted automated infrastructures, and the potential for less powerful actors to develop and exploit big data for their own purposes.

Conceptualizing Big Data and Their Paradoxes

Much like the early discussions on the societal impact of the Internet, current debates about big data reflect both Utopian and dystopian elements, just as they include some foundational myths about data and scientific practice. (2) Big data are ridden with paradoxes, and discussing these briefly is relevant for our purpose here. Jonathan King and Neil M. Richards, (3) for example, have highlighted three paradoxes of big data: transparency, identity, and power. First, big data seem to make the world more transparent and potentially more predictable. …

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