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Using external data in a BI solution to optimise waste management
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Produktion och automatiseringsteknik, Production and Automation Engineering)
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-3973-3394
2020 (English)In: Journal of Decision Systems, ISSN 1246-0125, E-ISSN 2116-7052Article in journal (Refereed) Epub ahead of print
Abstract [en]

BI solutions are constantly being developed to support decision-making at various organisational levels. These solutions facilitate the compilation, aggregation and summarisation of large volumes of data. Consequently, the business value created by these systems is increasing as they sustain more and more advanced analytics, ranging from descriptive analytics, to predictive analytics, to prescriptive analytics. However, most organisations work primarily with internal data. Despite many references in the literature to the value hidden in external data, details on how such data can be used are scarce. In this paper, we present the results of an extensive action case study at a public waste management company. The results illustrate how external data from several external data sources, integrated into an up-and-running BI solution, are used jointly to allow for descriptive and predictive analytics, as well as prescriptive analytics. In addition, details of these analytical values are given and related to organisational benefits.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2020.
Keywords [en]
Business intelligence, external data, decision-support system, waste management
National Category
Production Engineering, Human Work Science and Ergonomics Information Systems
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-18334DOI: 10.1080/12460125.2020.1732174ISI: 000517906100001OAI: oai:DiVA.org:his-18334DiVA, id: diva2:1415634
Available from: 2020-03-19 Created: 2020-03-19 Last updated: 2020-03-23Bibliographically approved

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Strand, MattiasSyberfeldt, Anna

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