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The Challenges of Data Cleansing with Data Warehouses
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Author(s): Nigel McKelvey (Computer Science Department, Letterkenny Institute of Technology, Ireland), Kevin Curran (Ulster University, UK)and Luke Toland (Letterkenny Institute of Technology, Ireland)
Copyright: 2016
Pages: 6
Source title:
Effective Big Data Management and Opportunities for Implementation
Source Author(s)/Editor(s): Manoj Kumar Singh (Adama Science and Technology University, Ethiopia)and Dileep Kumar G. (Adama Science and Technology University, Ethiopia)
DOI: 10.4018/978-1-5225-0182-4.ch005
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Abstract
Data cleansing is a long standing problem which every organisation that incorporates a form of data processing or data mining must undertake. It is essential in improving the quality and reliability of data. This paper presents the necessary methods needed to process data at a high quality. It also classifies common problems which organisations face when cleansing data from a source or multiple sources while evaluating methods which aid in this process. The different challenges faced at schema-level and instance-level are also outlined and how they can be overcome. Currently there are tools which provide data cleansing, but are limited due to the uniqueness of every data source and data warehouse. Outlined are the limitations of these tools and how human interaction (self-programming) may be needed to ensure vital data is not lost. We also discuss the importance of maintaining and removing data which has been stored for several years and may no longer have any value.
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