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Measuring Data Quality in Analytical Projects

Author

Listed:
  • 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)

Abstract

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.

Suggested Citation

  • Anca Ioana ANDREESCU & Anda BELCIU & Alexandra FLOREA & Vlad DIACONITA, 2014. "Measuring Data Quality in Analytical Projects," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 5(1), pages 15-25, May.
    • Handle: RePEc:aes:dbjour:v:5:y:2014:i:1:p:15-25
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