Predicting Data Quality Success - The Bullwhip Effect in Data
Quality

D 2017

Predicting Data Quality Success - The Bullwhip Effect in Data Quality

GE, Mouzhi, Markus HELFERT and Tony O'BRIEN

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

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Denmark

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

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

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

Abstract

V originále

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.

Citovat

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.

@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 and 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, p.~157-165. ISBN~978-3-319-64929-0. Available from: https://dx.doi.org/10.1007/978-3-319-64930-6\_{}12.

Detailed Information on Publication Record