On SAGE Insight: What makes Big Data, Big Data?

Article title: What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets

From Big Data & Society

In the main, definitions suggest that Big Data possess a suite of key traits: volume, velocity and variety (the 3Vs), but also exhaustivity, resolution, indexicality, relationality, extensionality and scalability. In this paper, we consider the question ‘what makes Big Data, Big Data?’. Through an analysis that applied Kitchin’s typology of Big Data traits to 26 datasets this study reveals that Big Data do not all share the same characteristics and that there are multiple forms of Big Data. Indeed, this analysis demonstrates that only a handful of the 26 datasets examined held all seven traits identified by Kitchin. It is only through such ontological work, focused on shifting from broad generalities to specific qualities, that we will gain conceptual clarity about what constitutes Big Data and formulate how best to make sense of it and how it might be used to make sense of the world.

Abstract

Big Data has been variously defined in the literature. In the main, definitions suggest that BigData possess a suite of key traits: volume, velocity and variety (the 3Vs), but also exhaustivity, resolution, indexicality, relationality, extensionality and scalability. However, these definitions lack ontological clarity, with the term acting as an amorphous, catch-all label for a wide selection of data. In this paper, we consider the question ‘what makes Big Data, BigData?’, applying Kitchin’s taxonomy of seven Big Data traits to 26 datasets drawn from seven domains, each of which is considered in the literature to constitute Big Data. The results demonstrate that only a handful of datasets possess all seven traits, and some do not possess either volume and/or variety. Instead, there are multiple forms of Big Data. Our analysis reveals that the key definitional boundary markers are the traits of velocity and exhaustivity. We contend that Big Data as an analytical category needs to be unpacked, with the genus of BigData further delineated and its various species identified. It is only through such ontological work that we will gain conceptual clarity about what constitutes Big Data, formulate how best to make sense of it, and identify how it might be best used to make sense of the world.

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Article details
What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets
Rob Kitchin,  Gavin McArdle
DOI: http://dx.doi.org/10.1177/2053951716631130
Big Data & Society
January–June 2016

     
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