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What is the major difference between structured and unstructured data? December 27, 2008

Posted by Peter Benza in Data Dictionary, Data Elements, Data Formats, Data Types.
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A good rule of thumb is structured “tabular” data fits into rows and columns and unstructured data are things like web pages, presentations, survey’s, and images.

[Add more examples here.]

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

Spatial data layers and conflation September 18, 2007

Posted by Peter Benza in Data Accuracy, Data Elements, Data Formats, Data Types, Data Visualization.
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Conflation is more than matching features from different spatial sources.  A good spatial-matching technology that includes conflation as a parameter should also be defined by location, the shapes attributes, and its relationships to other objects. 

A good example of this is when two or more road networks have conflicting views – how do you proceed, if you end up only wanting to display one of the sources? 

What geometrical matching techniques or advice do you have on this topic?

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.