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How can data profiling and a meta data repository be leveraged together? August 17, 2007

Posted by Peter Benza in Data Modeling, Data Processes, Data Profiling.
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Many times the same term used by one department means something totally different to another department.  This can prove to be a challenge as organizations continue to centralize all their customer data into one location.  You may not be able to resolve all the different name variations used by each department, but assembling all the pieces and documenting them in one place is a must.  It may become necessary to follow-up with the appropriate decision makers to resolve any discreptencies.

So, it becomes mission-critical to compile “data about your data” and store it in a meta data repository, plus include some other key attributes about the data source, about each variable, its range of values, record length, and so on.  Ultimately, the data elements need to be analyzed and merged into a single classification system based on all relevant data sources from across the enterprise.

This (meta data) information will also become valuable guide to validate other data-specific activities, such as: customer data modeling, match/merge logic, and even for QC purposes during the integration/execution phase of storing the resulting customer information in one location.

Meta Data: http://en.wikipedia.org/wiki/Metadata

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