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

Big Data has been variously defined in the literature. In the main, definitions suggest that Big Data possess a suite of key\udtraits: volume, velocity and variety (the 3Vs), but also exhaustivity, resolution, indexicality, relationality, extensionality and\udscalability. However, these definitions lack ontological clarity, with the term acting as an amorphous, catch-all label for a\udwide selection of data. In this paper, we consider the question ‘what makes Big Data, Big Data?’, applying Kitchin’s\udtaxonomy of seven Big Data traits to 26 datasets drawn from seven domains, each of which is considered in the literature\udto 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\uddefinitional boundary markers are the traits of velocity and exhaustivity. We contend that Big Data as an analytical\udcategory needs to be unpacked, with the genus of Big Data further delineated and its various species identified. It is only\udthrough such ontological work that we will gain conceptual clarity about what constitutes Big Data, formulate how best\udto make sense of it, and identify how it might be best used to make sense of the world