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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/

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

Summit 2008 – San Francisco January 10, 2008

Posted by Peter Benza in Data Accuracy, Data Governance, Data Integrity, Data Metrics, Data Processes, Data Quality, Data Stewardship, Data Strategy, Data Templates, Data Verification, Data Warehouse.
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If you have not attended a Summit then mark your calendars for:

CDI-MDM Summit Spring 2008

Please post and share your comments about this upcoming summit or if you have not attended and want to learn more then link using the above reference.

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

Online data gathering – great resource for surveys and business forms August 25, 2007

Posted by Peter Benza in Data Analysis, Data References, Data Templates, Data Tools, Data Warehouse.
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I came across this website and after reading what it does I just had to share it.  The responses can be exported to a excel or word file – even add your response and form data into a data warehouse – an Access file will be downloaded that will allow you to do further analysis, if you desire.  

Visit www.askget.com to learn more about this online data gathering tool. 

Malcolm Chisholm, President, Askget.com, Holmdel, NJ

What kinds of international data templates exist today – out of the box? August 17, 2007

Posted by Peter Benza in Data Templates.
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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. 

How do you prevent data errors in your database today? August 16, 2007

Posted by Peter Benza in Data Errors, Data Hygiene, Data Processes, Data Sources, Data Templates, Data Verification.
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Data errors can be reduced but not totally eliminated, so be realistic.  First consideration must be given at point of entry and depending of the size of your organization this could be many.  Once your data is consumed, a number of other places should be considered to monitor data errors, such as: data convertion, data preparation, data migration, data integration, data reporting, data analysis, and finally when it is consumed and displayed for use in a dashboard.

Collectively, once you document where most of these errors are orginating from – then and only then will you be able to classify data errors given the entire end to end process from point of entry to using the data in its original or transformed state in a report, analysis, or dashboard.

Now, that you have compiled all these data errors (specific to your organization) you can begin to feed some/most of these findings back into your data quality, data governance, and data management frameworks.