Predicting Data Quality Success - The Bullwhip Effect in Data Quality

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00096703" target="_blank" >RIV/00216224:14330/17:00096703 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-64930-6_12" target="_blank" >http://dx.doi.org/10.1007/978-3-319-64930-6_12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-64930-6_12" target="_blank" >10.1007/978-3-319-64930-6_12</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting Data Quality Success - The Bullwhip Effect in Data Quality

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Predicting Data Quality Success - The Bullwhip Effect in Data Quality

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 16th International Conference on Perspectives in Business Informatics Research

ISBN

9783319649290

ISSN

1865-1348

e-ISSN

—

Počet stran výsledku

9

Strana od-do

157-165

Název nakladatele

Springer

Místo vydání

Copenhagen, Denmark

Místo konání akce

Copenhagen, Denmark

Datum konání akce

1. 1. 2017

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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