Data quality assessment using multi-attribute maintenance perspective

Mustafa Aljumaili, Ramin Karim and Phillip Tretten

International Journal of Information and Decision Sciences, 2018, vol. 10, issue 2, 147-161

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. (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=92423 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijidsc:v:10:y:2018:i:2:p:147-161

Access Statistics for this article

More articles in International Journal of Information and Decision Sciences from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().