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Incorporating external data into a BI solution at a public waste management organization
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and Automation Engineering)
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID-id: 0000-0003-3973-3394
2019 (engelsk)Inngår i: International Journal of Business Intelligence Research, ISSN 1947-3591, E-ISSN 1947-3605, Vol. 10, nr 2, s. 36-56Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Organizations are showing an increasing interest in incorporating external data into their business intelligence solutions. Such data allows for advanced analytics and enables more comprehensive and inclusive decision-making. However, external data incorporation is relatively unexplored in the literature, and scientifically published details on up-and-running BI solutions are very sparse. In addition, published literature concerning the incorporation of external data into BI solutions is often rather synoptic or rather old (originating from data warehouse related literature). Therefore, the authors present the results of an action case study at a public waste management organization, illustrating detailed aspects of external data incorporation related to the back-end of the solution such as data selection, source characteristics, acquisition technologies and frequencies, and integration approaches. Given that the external origin of the data poses specific problems that must be overcome in order to allow for successful incorporation initiatives, special attention was paid to such problems. Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

sted, utgiver, år, opplag, sider
IGI Global, 2019. Vol. 10, nr 2, s. 36-56
Emneord [en]
Business Intelligence, Case Study, External Data, Waste Management
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik
Identifikatorer
URN: urn:nbn:se:his:diva-17479DOI: 10.4018/IJBIR.2019070104Scopus ID: 2-s2.0-85068688761OAI: oai:DiVA.org:his-17479DiVA, id: diva2:1339125
Merknad

EISBN13: 9781522566939

Tilgjengelig fra: 2019-07-25 Laget: 2019-07-25 Sist oppdatert: 2019-11-18bibliografisk kontrollert

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