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@inproceedings{1380779,
author = {Ge, Mouzhi and Helfert, Markus and O'Brien, Tony},
address = {Copenhagen, Denmark},
booktitle = {Proceedings of the 16th International Conference on Perspectives in Business Informatics Research},
doi = {http://dx.doi.org/10.1007/978-3-319-64930-6_12},
editor = {Björn Johansson},
keywords = {Data quality; Bullwhip effect; Data quality success; Supply chain; Data quality improvement},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Copenhagen, Denmark},
isbn = {978-3-319-64929-0},
pages = {157-165},
publisher = {Springer},
title = {Predicting Data Quality Success - The Bullwhip Effect in Data Quality},
url = {http://www.bir2017.aau.dk/},
year = {2017}
}
TY - JOUR
ID - 1380779
AU - Ge, Mouzhi - Helfert, Markus - O'Brien, Tony
PY - 2017
TI - Predicting Data Quality Success - The Bullwhip Effect in Data Quality
PB - Springer
CY - Copenhagen, Denmark
SN - 9783319649290
KW - Data quality
KW - Bullwhip effect
KW - Data quality success
KW - Supply chain
KW - Data quality improvement
UR - http://www.bir2017.aau.dk/
N2 - 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.
ER -
GE, Mouzhi, Markus HELFERT a Tony O'BRIEN. Predicting Data Quality Success - The Bullwhip Effect in Data Quality. In Björn Johansson. \textit{Proceedings of the 16th International Conference on Perspectives in Business Informatics Research}. Copenhagen, Denmark: Springer, 2017, s.~157-165. ISBN~978-3-319-64929-0. Dostupné z: https://dx.doi.org/10.1007/978-3-319-64930-6\_{}12.
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