Title: Data quality assessment using multi-attribute maintenance perspective

Authors: Mustafa Aljumaili; Ramin Karim; Phillip Tretten

Addresses: Division of Operation, Maintenance and Acoustics Engineering, Luleå University of Technology, Luleå, SE-971 87, Sweden ' Division of Operation, Maintenance and Acoustics Engineering, Luleå University of Technology, Luleå, SE-971 87, Sweden ' Division of Operation, Maintenance and Acoustics Engineering, Luleå University of Technology, Luleå, SE-971 87, Sweden

Abstract: 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.

Keywords: data quality; information; maintenance; eMaintenance; attributes; assessment.

DOI: 10.1504/IJIDS.2018.092423

International Journal of Information and Decision Sciences, 2018 Vol.10 No.2, pp.147 - 161

Available online: 05 Jun 2018 *

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