Predicting Data Quality Success - The Bullwhip Effect in Data Quality

GE, Mouzhi, O’BRIEN, Tony and HELFERT, Markus (2017). Predicting Data Quality Success - The Bullwhip Effect in Data Quality. Perspectives in Business Informatics Research, 157-165.

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Official URL: https://link.springer.com/chapter/10.1007/978-3-31...
Link to published version:: https://doi.org/10.1007/978-3-319-64930-6_12

Abstract

Over the last years many data quality initiatives and suggestions report how to improve and sustain data quality. However, almost all data quality projects and suggestions focus on the assessment and one-time quality improvement, especially, suggestions rarely include how to sustain the continuous data quality improvement. Inspired by the work related to variability in supply chains, also known as the Bullwhip effect, this paper aims to suggest how to sustain data quality improvements and investigate the effects of delays in reporting data quality indicators. Furthermore, we propose that a data quality prediction model can be used as one of countermeasures to reduce the Data Quality Bullwhip Effect. Based on a real-world case study, this paper makes an attempt to show how to reduce this effect. Our results indicate that data quality success is a critical practice, and predicting data quality improvements can be used to decrease the variability of the data quality index in a long run.

Item Type: Article
Additional Information: 16th International Conference, BIR 2017, Copenhagen, Denmark, August 28–30, 2017, Proceedings
Identification Number: https://doi.org/10.1007/978-3-319-64930-6_12
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 05 Mar 2019 14:30
Last Modified: 05 Mar 2019 14:30
URI: http://shura.shu.ac.uk/id/eprint/23817

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