Measuring Data Quality in Analytical Projects
Anca Ioana Andreescu (),
Alexandra Florea () and
Vlad Diaconita ()
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Anca Ioana Andreescu: University of Economic Studies, Bucharest, Romania
Anda Belciu: University of Economic Studies, Bucharest, Romania
Alexandra Florea: University of Economic Studies, Bucharest, Romania
Vlad Diaconita: University of Economic Studies, Bucharest, Romania
Database Systems Journal, 2014, vol. 5, issue 1, 15-25
Measuring and assuring data quality in analytical projects are considered very important issues and overseeing their benefits may cause serious consequences for the efficiency of organizations. Data profiling and data cleaning are two essential activities in a data quality process, along with data integration, enrichment and monitoring. Data warehouses require and provide extensive support for data cleaning. These loads and renew continuously huge amounts of data from a variety of sources, so the probability that some of the sources contain "dirty data" is great. Also, analytics tools offer, to some extent, facilities for assessing and assuring data quality as a built in support or by using their proprietary programming languages. This paper emphasizes the scope and relevance of a data quality measurement in analytical projects by the means of two intensively used tools such as Oracle Warehouse Builder and SAS 9.3.
Keywords: : data quality; data profiling; analytical tools; data warehouses (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:aes:dbjour:v:5:y:2014:i:1:p:15-25
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