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Data quality assessment using multi-attribute: maintenance perspective
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-6135-3008
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-0055-2740
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0003-3827-0295
2018 (English)In: International Journal of Information and Decision Sciences, ISSN 1756-7017, E-ISSN 1756-7025, Vol. 10, no 2, p. 147-161Article in journal (Refereed) Published
Abstract [en]

The paper proposes a model for data quality (DQ) assessment in maintenance. Data has become an increasingly important since most of the maintenance planning and implementations are based on data analysis. Poor DQ reduces customer satisfaction, leading to poor decision making, and has negative impacts on strategy execution. To improve DQ as well as to evaluate the current status, DQ needs to be measured. A measure for DQ could be an important support for decision makers. Multi-criteria decision-making (MCDM) methods can provide a framework for DQ assessment, however, they are not used in literature for DQ assessment. In order to assess DQ, the attributes or KPIs need to be defined, their hierarchy should be designed and the assessment model is proposed to evaluate these attributes. A case study is also presented in this paper. The study shows that MCDM methods could provide qualitative estimation for the quality of DQ attributes.

Place, publisher, year, edition, pages
InderScience Publishers, 2018. Vol. 10, no 2, p. 147-161
Keywords [en]
Data quality, data science, eMaintenance
National Category
Engineering and Technology Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-69215DOI: 10.1504/IJIDS.2018.092423Scopus ID: 2-s2.0-85048767333OAI: oai:DiVA.org:ltu-69215DiVA, id: diva2:1215277
Note

Validerad;2018;Nivå 1;2018-06-21 (svasva)

Available from: 2018-06-08 Created: 2018-06-08 Last updated: 2018-06-29Bibliographically approved

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Al-Jumaili, MustafaKarim, RaminTretten, Phillip

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