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A Data Quality Metrics Hierarchy for Reliability Data

Gitzel, Ralf and Turrin, Simone and Maczey, Sylvia and Wu, Shaomin and Schmitz, Björn (2016) A Data Quality Metrics Hierarchy for Reliability Data. In: Proceedings of the 9thIMA International Conference on Modellingin Industrial Maintenance and Reliability. Institute of Mathematics and its Applications. ISBN 978-0-905091-31-0. (KAR id:56313)


In this paper, we describe an approach to understanding data quality issues in field data used for the calculation of reliability metrics such as availability, reliability over time, or MTBF. The focus lies on data from sources such as maintenance management systems or warranty databases which contain information on failure times, failure modes for all units. We propose a hierarchy of data quality metrics which identify and assess key problems in the input data. The metrics are organized in such a way that they guide the data analyst to those problems with the most impact on the calculation and provide a prioritised action plan for the improvement of data quality. The metrics cover issues such as missing, wrong, implausible and inaccurate data. We use examples with real-world data to showcase our software prototype and to illustrate how the metrics have helped with data preparation. Using this way, analysts can reduce the amount of wrong conclusions drawn from the data to mistakes in the input values.

Item Type: Book section
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Shaomin Wu
Date Deposited: 15 Jul 2016 09:35 UTC
Last Modified: 10 Dec 2022 06:08 UTC
Resource URI: (The current URI for this page, for reference purposes)

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