Data science in organizations: conceptualizing its breakthroughs and blind spots

Cybulski, Jacob L and Scheepers, Rens 2021, Data science in organizations: conceptualizing its breakthroughs and blind spots, Journal of information technology, pp. 1-22, doi: 10.1177/0268396220988539.

Attached Files
Name Description MIMEType Size Downloads

Title Data science in organizations: conceptualizing its breakthroughs and blind spots
Author(s) Cybulski, Jacob LORCID iD for Cybulski, Jacob L
Scheepers, RensORCID iD for Scheepers, Rens
Journal name Journal of information technology
Start page 1
End page 22
Total pages 22
Publisher SAGE Publications
Place of publication London, Eng.
Publication date 2021-02-26
ISSN 0268-3962
Keyword(s) data science
big data
machine learning
artificial intelligence
Summary The field of data science emerged in recent years, building on advances in computational statistics, machine learning, artificial intelligence, and big data. Modern organizations are immersed in data and are turning toward data science to address a variety of business problems. While numerous complex problems in science have become solvable through data science, not all scientific solutions are equally applicable to business. Many data-intensive business problems are situated in complex socio-political and behavioral contexts that still elude commonly used scientific methods. To what extent can such problems be addressed through data science? Does data science have any inherent blind spots in this regard? What types of business problems are likely to be addressed by data science in the near future, which will not, and why? We develop a conceptual framework to inform the application of data science in business. The framework draws on an extensive review of data science literature across four domains: data, method, interfaces, and cognition. We draw on Ashby’s Law of Requisite Variety as theoretical principle. We conclude that data-scientific advances across the four domains, in aggregate, could constitute requisite variety for particular types of business problems. This explains why such problems can be fully or only partially addressed, solved, or automated through data science. We distinguish between situations that can be improved due to cross-domain compensatory effects, and problems where data science, at best, only contributes merely to better understanding of complex phenomena.
Notes In-press
Language eng
DOI 10.1177/0268396220988539
Indigenous content off
Field of Research 08 Information and Computing Sciences
15 Commerce, Management, Tourism and Services
17 Psychology and Cognitive Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Persistent URL

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 94 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Mon, 22 Mar 2021, 08:19:35 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact