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

By Rob Kitchin, Gavin McArdle. 2016

500 citations recorded by the source corpus.

Big Data has been variously defined in the literature. 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. 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, Big Data?’, 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 Big Data 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.

Subfields: Geography, Planning and Development, Information Systems, Information Systems and Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Geography, Planning and Development

1. David Beer
2. Paul Waddell
3. Geoff Boeing

In Information Systems

1. Richard F.J. Haans
2. Timothy R. Hannigan
3. David Beer
4. Sarah Kaplan
5. P. Devereaux Jennings
6. Vern L. Glaser
7. Keyvan Vakili
8. Hovig Tchalian

In Information Systems and Management

1. David Beer
2. Sarah Kaplan
3. P. Devereaux Jennings
4. Geoff Boeing

Citations in the corpus

• Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, background): “A simple dataset, which is also called a good dataset in general terminology, is much easier to use, tends to allow for effective and easy data manipulation and calculations for some sort of meaningful statistical analysis [37].”
• Topic Modeling in Management Research: Rendering New Theory from Textual Data (2019, background): “On the whole, topic modeling tends to be applied more frequently to sampled corpora than to a single, homogenous corpus (Borgman, 2015; Kitchin & McArdle, 2016).”
• New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings (2016, background): ““Big data,” though it varies in interpretation, is more than just a buzzword—it is a type of data that is meaningfully different from traditional and necessarily smallerscale data (Mayer-Schönberger and Cukier 2013; Kitchin and McArdle 2016).”
• How should we do the history of Big Data? (2016, background): “We may need further work that delimits the particular material and ontological properties of the current form that this social data takes – such as the work being conducted by Kitchin (2014; see also Kitchin and McArdle, 2016) – but let us turn our attention elsewhere for the moment.”