Big Data and the Little Big Bang: An Epistemological (R)evolution
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fdata-03-00031.pdf
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850.58 KB
Format
Adobe PDF
Checksum (MD5)
70d0026fc35b53e3ab2c2dfdfa6efd99
Author(s)
Balazka, Dominik
Rodighiero, Dario
Date Issued
September 2020
Journal
Frontiers in Big Data
Publisher
Frontiers
Citation
Balazka, Dominik and Dario Rodighiero. "Big Data and the Little Big Bang: An Epistemological (R)evolution." Frontiers in Big Data 3 (September 2020): 31 © 2020 Balazka and Rodighiero
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Program in Comparative Media Studies/Writing
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/fdata.2020.00031
