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DataFlux positioned in leaders quadrant for data quality according to Gartner January 17, 2008

Posted by Peter Benza in Data Assessment, Data Consolidation, Data Governance, Data Hygiene, Data Integration, Data Integrity, Data Management, Data Profiling, Data Quality, Data Standardization, Data Templates, Data Tools.
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Compliance, Data Governance, Master Data Management, Data Profiling

http://www.dataflux.com/

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BusinessObjects data quality XI January 17, 2008

Posted by Peter Benza in Data Accuracy, Data Analysis, Data Architecture, Data Assessment, Data Consolidation, Data Hygiene, Data Integrity, Data Profiling, Data Quality, Data References, Data Strategy, Data Templates, Data Tools.
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Standardize, Identify Duplicates, Correct, Improve Match, Append, Consolidate, and more.    

http://www.businessobjects.com/products/dataquality/data_quality_xi.asp

MDM Accelerator® by Zoomix January 9, 2008

Posted by Peter Benza in Data Accuracy, Data Aggregates, Data Analysis, Data Assessment, Data Consolidation, Data Dictionary, Data Formats, Data Governance, Data Hygiene, Data Integration, Data Management, Data Metrics, Data Processes, Data Profiling, Data Quality, Data References, Data Sources, Data Standardization, Data Stewardship, Data Synchronization, Data Templates, Data Tools.
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To learn more about or post your comments about MDM Accelerator®

by Zoomix.

http://www.zoomix.com/mdm.asp

Teradata – Master Data Management January 9, 2008

Posted by Peter Benza in Data Assessment, Data Consolidation, Data Dictionary, Data Governance, Data Hygiene, Data Integration, Data Management, Data Metrics, Data Processes, Data Profiling, Data Quality, Data Standardization, Data Stewardship, Data Strategy, Data Templates, Data Tools, Data Types.
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To learn more about Teradata and their MDM solution offering:

http://www.teradata.com/master-data-management

What other data aggregate functions are useful besides averages and means? September 19, 2007

Posted by Peter Benza in Data Aggregates, Data Consolidation, Data Elements, Data Errors, Data Research.
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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. 

Does data aggregation and data consolidation mean the same thing? August 13, 2007

Posted by Peter Benza in Data Consolidation.
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Does anyone have a point of view on the similarities or differences between data consolidation and data aggregation?