Abstract
Starting from an analysis of frequently employed definitions of big data, it will be argued that, to overcome the intrinsic weaknesses of big data, it is more appropriate to define the object in relational terms. The excessive emphasis on volume and technological aspects of big data, derived from their current definitions, combined with neglected epistemological issues gave birth to an objectivistic rhetoric surrounding big data as implicitly neutral, omni-comprehensive, and theory-free. This rhetoric contradicts the empirical reality that embraces big data: (1) data collection is not neutral nor objective; (2) exhaustivity is a mathematical limit; and (3) interpretation and knowledge production remain both theoretically informed and subjective. Addressing these issues, big data will be interpreted as a methodological revolution carried over by evolutionary processes in technology and epistemology. By distinguishing between forms of nominal and actual access, we claim that big data promoted a new digital divide changing stakeholders, gatekeepers, and the basic rules of knowledge discovery by radically shaping the power dynamics involved in the processes of production and analysis of data.
Highlights
The former director of the Oxford Internet Institute, Luciano Floridi, claims that while 180 exabytes of data were collected between the invention of writing and 2006, in 2011, they grew up to 1,600 exabytes (Floridi, 2012, p. 435)
At an epistemological level and within the realm of social sciences, we argue that this is not the case of big data: (1) big data epistemology within the scientific literature is still heavily grounded on basic assumptions of the third paradigm and obey the principles developed by Karl Popper; (2) big data are integrating small data and not replacing them; and (3) theoryand data-driven approaches share commonalities that make them potentially convergent rather than radically divergent
This paper offered an extensive literature review while addressing the problem of defining big data, the harmful diffusion of an objectivistic rhetoric, and the impact of big data on knowledge discovery within the scientific domain
Summary
The former director of the Oxford Internet Institute, Luciano Floridi, claims that while 180 exabytes of data were collected between the invention of writing and 2006, in 2011, they grew up to 1,600 exabytes (Floridi, 2012, p. 435). Big data critics move along three main argumentative lines: (1) data are not neutral representations of society as they are collected through specific modes of production (Mager, 2014); (2) data do not represent the totality of the population but are rather a “misrepresentative mixture of subpopulations” captured in their online environment and subject to various types of biases (McFarland and McFarland, 2015); and (3) the meaning does not emerge from the data itself but is rather from an effort of interpretation performed by fallible human beings (Gransche, 2016).
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