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 a Tony O'BRIEN

Základní údaje

Originální název

Predicting Data Quality Success - The Bullwhip Effect in Data Quality

Autoři

GE, Mouzhi (156 Čína, garant, domácí), Markus HELFERT (276 Německo) a Tony O'BRIEN (826 Velká Británie a Severní Irsko)

Vydání

Copenhagen, Denmark, Proceedings of the 16th International Conference on Perspectives in Business Informatics Research, od s. 157-165, 9 s. 2017

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Dánsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

Springer, CORE B conference, SCOPUS, WoS, DBLP

Kód RIV

RIV/00216224:14330/17:00096703

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-64929-0

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-64930-6_12

Klíčová slova anglicky

Data quality; Bullwhip effect; Data quality success; Supply chain; Data quality improvement

Štítky

firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 26. 2. 2018 14:09, RNDr. Pavel Šmerk, Ph.D.

Anotace

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 a 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, s. 157-165. ISBN 978-3-319-64929-0. Dostupné z: 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 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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