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Using external data in a BI solution to optimise waste management
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningsmiljön Virtuell produkt- och produktionsutveckling. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID-id: 0000-0002-8619-3776
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningsmiljön Virtuell produkt- och produktionsutveckling. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID-id: 0000-0003-3973-3394
2020 (engelsk)Inngår i: Journal of Decision Systems, ISSN 1246-0125, E-ISSN 2116-7052, Vol. 29, nr 1, s. 53-68Artikkel i tidsskrift (Fagfellevurdert) Published
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.

sted, utgiver, år, opplag, sider
Taylor & Francis Group, 2020. Vol. 29, nr 1, s. 53-68
Emneord [en]
Business intelligence, external data, decision-support system, waste management
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik
Identifikatorer
URN: urn:nbn:se:his:diva-18334DOI: 10.1080/12460125.2020.1732174ISI: 000517906100001Scopus ID: 2-s2.0-85083907794OAI: oai:DiVA.org:his-18334DiVA, id: diva2:1415634
Tilgjengelig fra: 2020-03-19 Laget: 2020-03-19 Sist oppdatert: 2023-06-20bibliografisk kontrollert

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Strand, MattiasSyberfeldt, Anna

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