Projets par an
Résumé
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.
langue originale | Anglais |
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titre | SEKE 2022 Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering |
Pages | 487-492 |
Nombre de pages | 6 |
ISBN (Electronique) | 1891706543, 9781891706547 |
Les DOIs | |
Etat de la publication | Publié - 2022 |
Série de publications
Nom | Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE |
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ISSN (imprimé) | 2325-9000 |
ISSN (Electronique) | 2325-9086 |
Empreinte digitale
Examiner les sujets de recherche de « Supporting Data Selection for Decision Support Systems: Towards a Decision-Making Data Value Taxonomy ». Ensemble, ils forment une empreinte digitale unique.Projets
- 1 Terminé
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MP: Recherche universitaire sur l’exploitation des données des pouvoirs locaux
Burnay, C. (Responsable du Projet), Castiaux, A. (Responsable du Projet), Dodeigne, J. (Responsable du Projet), Linden, I. (Responsable du Projet) & Jacquet, V. (Chercheur)
1/01/21 → 31/12/22
Projet: Recherche
Thèses de l'étudiant
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Reducing the Effects of Information Overload on Governments Decision-Making: Empirical Frameworks to Support Data Selection
Lega, M. (Auteur), Burnay, C. (Promoteur), Linden, I. (Copromoteur), Gnabo, J.-Y. (Président), Kolp, M. (Jury), Simonofski, A. (Jury) & CRUSOE, J. (Jury), 24 avr. 2024Student thesis: Doc types › Docteur en Sciences