Projects per year
Abstract
Data are ubiquitous and generate unprecedented opportunities to help making decisions within organizations. This has led so-called data-driven Decision Support Systems (DSS) to become critical, if not vital, systems for most companies. The design of such DSS raises important methodological challenges, since data-driven DSS should expose only useful information to decision makers, but data available in a company's database are numerous and not equally supportive. Failing to provide the right data to the right decision-maker may reduce the usefulness of a DSS, and can lead to lower quality decision outputs. This is particularly striking in the case of Self-Service Business Intelligence (SSBI) where users build DSS outputs themselves. In this paper, we elaborate on this idea of data profusion and propose a data selection criterion, namely the decision-making data value. To do this, we discuss the concept of value and its application to data and decision making, we review existing literature and propose a taxonomy of the dimensions of data value in the context of decision making. We also validate this taxonomy with semi-direct interviews and discuss the future research we plan to conduct as a way to apply this approach for the specification of high-value data-driven DSS.
Original language | English |
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Title of host publication | SEKE 2022 Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering |
Pages | 487-492 |
Number of pages | 6 |
ISBN (Electronic) | 1891706543, 9781891706547 |
DOIs | |
Publication status | Published - 2022 |
Publication series
Name | Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE |
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ISSN (Print) | 2325-9000 |
ISSN (Electronic) | 2325-9086 |
Keywords
- Data quality
- Data selection
- Data utility
- Data value
- Decision making
- Decision Support System
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Dive into the research topics of 'Supporting Data Selection for Decision Support Systems: Towards a Decision-Making Data Value Taxonomy'. Together they form a unique fingerprint.Projects
- 1 Finished
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MP: Academic research on the exploitation of local government data
Burnay, C. (PI), Castiaux, A. (PI), Dodeigne, J. (PI), Linden, I. (PI) & Jacquet, V. (Researcher)
1/01/21 → 31/12/22
Project: Research
Student theses
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Reducing the Effects of Information Overload on Governments Decision-Making: Empirical Frameworks to Support Data Selection
Lega, M. (Author), Burnay, C. (Supervisor), Linden, I. (Co-Supervisor), Gnabo, J.-Y. (President), Kolp, M. (Jury), Simonofski, A. (Jury) & CRUSOE, J. (Jury), 24 Apr 2024Student thesis: Doc types › Doctor of Sciences