Summary
Data quality, more than ever before, is a pressing issue for organizations. With business intelligence and data mining currently featuring so prominently on the corporate decision-support wish list, data quality underpins useful and accurate decisions.
While we have examined a purposely trivial example here, it serves to illustrate just how powerful DQS can be in an environment with tables that have millions of rows of data and hundreds of tables. With the addition of business procedures and naming conventions, a business data expert can examine the data, create rules to flag data that is invalid, and extract data that is to be changed in order to standardize it across the system.
Once corrections have been identified, the changes to the ...
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