Data Quality Assurance

Data Quality Assurance - The process of profiling the data to discover inconsistencies and other anomalies, in the data and performing Data Cleansing_ activities to improve data quality. 

Source: Public Schools of North Carolina, 23 April 2010 10:39:28, http://www.ncpublicschools.org/data/management/glossary/

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Data Quality Assurance - Also Known As: Data Cleansing_ or Data Scrubbing. The process of checking the quality of the data being imported into the data warehouse. Data quality assurance is one of the greatest challenges in the process of data warehousing. If the data-based knowledge generated by the data warehouse is to be trusted, the data entered into the warehouse must be complete and accurate - "garbage in, garbage out". Data quality can be a challenge for several reasons: The data is being consolidated from a variety of legacy sources that may have differing definitions of key concepts such as "customer" or "profit". The legacy data was not originally collected for the purpose of decision support so some of the key data might be missing, incomplete, or not as accurate as desired. There might be times when all the data is not received from one of the legacy systems. This could make comparisons between time periods invalid. A significant portion of time in the development process should be set aside for setting up the data quality assurance process and implementing whatever Data Cleansing_ is needed. In a production environment, there should be a data quality report generated after each data warehouse import. There should be provision for rolling back an import if data quality testing indicates that the data is unacceptable.

[Category=Data Warehousing ]

Source: SDG Computing Inc., 06 May 2010 10:03:57, SDG Computing, now offline