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TDWI’s class outline on data quality assessment January 13, 2008

Posted by Peter Benza in Data Aggregates, Data Assessment, Data Consistancy, Data Profiling.
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This course gives comprehensive treatment to the process and practical challenges of data quality assessment. It starts with the systematic treatment of various data quality rules, and proceeds to the results analysis and building of an aggregated data quality scorecard. Special attention is given to the architecture and functionality of the data quality metadata warehouse.

Deciphering between data variables and data elements? August 16, 2007

Posted by Peter Benza in Data Consistancy, Data Consolidation, Data Elements, Data Formats, Data Standardization, Data Templates.
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Here are two data variables that require some special attention or you just might “age” your customers too soon, too late, or not at all. 

Exact age is a data variable and is typically stored as a whole number representing a customer’s age.  In this form it is a very powerful (and predictive) data variable and is used as one of the more commonly used variables to discriminate responders from non-responders. 

Exact age in this case can’t be broken down into any smaller data elements.  Okay, so know you understand the difference, but is this good enough given how you plan to use this data variable for target marketing purposes.

Exact age does have some limitations.  What about maintaining this particular variable in your customer data warehouse.  If left alone in its current format it (exact age) becomes an operational nightmare.  A more common and efficient way is creating a second data variable named (date of birth), and include three data elements month, day, and year of birth.

Remember, some data variables may have specific data elements within them – such as a phone number, street address, zip code, etc.  The more you examine each of the data variables in your database – you will begin to uncover all the potential options.