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Improving data quality in data warehousing applications

Li, Lin; Peng, Taoxin; Kennedy, Jessie

Authors

Lin Li



Contributors

Joaquim Filipe
Editor

Jos� Cordeiro
Editor

Abstract

There is a growing awareness that high quality of data is a key to today’s business success and dirty data that exits within data sources is one of the reasons that cause poor data quality. To ensure high quality, enterprises need to have a process, methodologies and resources to monitor and analyze the quality of data, methodologies for preventing and/or detecting and repairing dirty data. However in practice, detecting and cleaning all the dirty data that exists in all data sources is quite expensive and unrealistic. The cost of cleaning dirty data needs to be considered for most of enterprises. Therefore conflicts may arise if an organization intends to clean their data warehouses in that how do they select the most important data to clean based on their business requirements. In this paper, business rules are used to classify dirty data types based on data quality dimensions. The proposed method will be able to help to solve this problem by allowing users to select the appropriate group of dirty data types based on the priority of their business requirements. It also provides guidelines for measuring the data quality with respect to different data quality dimensions and also will be helpful for the development of data cleaning tools.

Presentation Conference Type Conference Paper (Published)
Conference Name Proceedings of the 12th International Conference on Enterprise Information Systems
Start Date Jun 8, 2010
End Date Jun 12, 2010
Publication Date 2010
Deposit Date Feb 4, 2011
Publicly Available Date May 16, 2017
Peer Reviewed Peer Reviewed
Volume 1
Pages 379-382
Book Title Proceedings of the 12th International Conference on Enterprise Information Systems
ISBN 9789898425041; 9789898425058; 9789898425065; 9789898425072; 9789898425089
DOI https://doi.org/10.5220/0002903903790382
Keywords Data quality; dirty data; data cleaning tools; data warehousing;
Public URL http://researchrepository.napier.ac.uk/id/eprint/3886
Contract Date May 16, 2017

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