University of Oulu

Nada Elgendy, Ahmed Elragal & Tero Päivärinta (2021) DECAS: a modern data-driven decision theory for big data and analytics, Journal of Decision Systems, DOI: https://doi.org/10.1080/12460125.2021.1894674

DECAS : a modern data-driven decision theory for big data and analytics

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Author: Elgendy, Nada1; Elragal, Ahmed2; Päivärinta, Tero1
Organizations: 1Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
2Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021121360187
Language: English
Published: Informa, 2021
Publish Date: 2021-12-13
Description:

Abstract

Decisions continue to be important to researchers, organizations and societies. However, decision research requires re-orientation to attain the future of data-driven decision making, accommodating such emerging topics and information technologies as big data, analytics, machine learning, and automated decisions. Accordingly, there is a dire need for re-forming decision theories to encompass the new phenomena. This paper proposes a modern data-driven decision theory, DECAS, which extends upon classical decision theory by proposing three main claims: (1) (big) data and analytics (machine) should be considered as separate elements; (2) collaboration between the (human) decision maker and the analytics (machine) can result in a collaborative rationality, extending beyond the classically defined bounded rationality; and (3) meaningful integration of the classical decision making elements with data and analytics can lead to more informed, and possibly better, decisions. This paper elaborates the DECAS theory and clarifies the idea in relation to examples of data-driven decisions.

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Series: Journal of decision systems
ISSN: 1246-0125
ISSN-E: 2116-7052
ISSN-L: 1246-0125
Issue: Latest articles
DOI: 10.1080/12460125.2021.1894674
OADOI: https://oadoi.org/10.1080/12460125.2021.1894674
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
512 Business and management
Subjects:
Funding: This research has been partially funded by the ITEA3 project Oxilate (https://itea3.org/project/oxilate.html).
Copyright information: © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
  https://creativecommons.org/licenses/by-nc-nd/4.0/