GE, Mouzhi, Markus HELFERT and Tony O'BRIEN. Predicting Data Quality Success - The Bullwhip Effect in Data Quality. In Björn Johansson. Proceedings of the 16th International Conference on Perspectives in Business Informatics Research. Copenhagen, Denmark: Springer, 2017, p. 157-165. ISBN 978-3-319-64929-0. Available from: https://dx.doi.org/10.1007/978-3-319-64930-6_12.
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Basic information
Original name Predicting Data Quality Success - The Bullwhip Effect in Data Quality
Authors GE, Mouzhi (156 China, guarantor, belonging to the institution), Markus HELFERT (276 Germany) and Tony O'BRIEN (826 United Kingdom of Great Britain and Northern Ireland).
Edition Copenhagen, Denmark, Proceedings of the 16th International Conference on Perspectives in Business Informatics Research, p. 157-165, 9 pp. 2017.
Publisher Springer
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Denmark
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW Springer, CORE B conference, SCOPUS, WoS, DBLP
RIV identification code RIV/00216224:14330/17:00096703
Organization unit Faculty of Informatics
ISBN 978-3-319-64929-0
ISSN 1865-1348
Doi http://dx.doi.org/10.1007/978-3-319-64930-6_12
Keywords in English Data quality; Bullwhip effect; Data quality success; Supply chain; Data quality improvement
Tags firank_B
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 26/2/2018 14:09.
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.
PrintDisplayed: 11/5/2024 00:52