Although big data has become a popular term and a hyped concept in recent years, definitional clarity in relation to big data is surprisingly elusive. In technical terms, big data refers to data sets that are huge in volume (at the scale of petabytes, exabytes and zettabytes); highly diverse in type and nature; generated continuously at great velocity in or near real-time; exhaustive in scope (enabling the capture of entire populations – or ‘n=all’ – rather than sampled selections); fine-grained in resolution at the level of indexing individual units; combinable with other networks of datasets; and flexible and scalable enough for new fields to be added and to expand in size rapidly (Boyd and Crawford 2013; Mayer-Schönberger and Cukier 2013; Kitchin 2014a, 2014b). Kitchin and McArdle (2015) suggest that exhaustivity and velocity are perhaps the two most key traits of big data systems. These qualities of big data are in contrast to earlier methods of data collection and analysis that progressed using data produced in tightly controlled ways using sampling techniques that limited their scope, variety, temporality and size (Kitchin and McArdle 2015).