On Minorities and Outliers: The Case for Making Big Data Small

Foucault Welles, B.
Big Data & Society
1(1), 2053951714540613
April 1, 2014

Abstract

In this essay, I make the case for choosing to examine small subsets of Big Data datasets—making big data small. Big Data allows us to produce summaries of human behavior at a scale never before possible. But in the push to produce these summaries, we risk losing sight of a secondary but equally important advantage of Big Data—the plentiful representation of minorities. Women, minorities and statistical outliers have historically been omitted from the scientific record, with problematic consequences. Big Data affords the opportunity to remedy those omissions. However, to do so, Big Data researchers must choose to examine very small subsets of otherwise large datasets. I encourage researchers to embrace an ethical, empirical and epistemological stance on Big Data that includes minorities and outliers as reference categories, rather than the exceptions to statistical norms.

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