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Today’s Linkedin Discussion Thread: Enterprise Data Quality April 28, 2009

Posted by Peter Benza in Data Analysis, Data Elements, Data Governance, Data Optimization, Data Processes, Data Profiling, Data Quality, Data Sources, Data Standardization, Data Synchronization, Data Tools, Data Verification.
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Here is my most recent question I just added to my Linkedin discussion group = Enterprise Data Quality.

QUESTION: What master data or existing “traditional” data management processes (or differentiators) have you identified to be useful across the enterprise regarding data quality?

MY INSIGHTS: Recently, I was able to demonstrate (and quantify) the impact of using an NCOA updated address for match/merge accuracy purposes when two or more customer “names and addresses” from three disparate source systems were present. The ultimate test approach warrants consideration especially when talking about the volume of customer records for big companies today number “hundreds” of millions of records. It is ideal to apply this test to the entire file not just a sample set. But, we all know today its about: money, time, value, resources, etc.

For testing purposes, I advised all individual customer address attributes were replaced (where information was available) with NCOA updated addresses and then loaded and processed through the “customer hub” technology. If you are not testing a piece of technology, then constructing your own match key or visually checking sample sets of customer records before and after is an alternative. Either way, inventory matches and non-matches from the two different runs – once with addresses (as-is) and once with addresses that leverage the NCOA information.

My goal was to establish a business process that focused on “pre-processing customer records” using a reliable third party source (in this case NCOA) instead of becoming completely dependent on a current or future piece of technology that may offer the same results, especially when the methodology (matching algorithms) are probalistic. My approach reduces your dependency, as well, and you can focus on “lift” the technology may offer – if your are comparing two or more products.

Where as, inside a deterministic-based matching utility (or off-the-shelf solution) adding extra space or columns of data to the end of your input file to store the NCOA addresses will allow you to accomplish the same results. But, for test purposes, the easier way may be to replace addresses where an NCOA record is available.

Remember, based on the volume of records your client may be dealing with, a pre-process (business process) may be ideal, rather than loading all the customer names and addresses into the third party customer hub technology and processing it. Caution: This all depends on how the business is required (i.e. compliance) to store information from cradle to grave. But, the rule of thumb of the MDM customer hub is to store the “best/master” (single customer view record) with the exception of users with extended search requirements. The data warehouse (vs. MDM solutions) now becomes the next challenge… what to keep where and how much. But, that is another discussion.

The percentage realized in using the updated customer address was substantial (over 10%) on the average based on all the sources factored into the analysis. This means several 10’s of millions of customer records will match/merge more effectively (and efficiently) followed by the incremental lift – based on what the “customer hub” technology enables using its proprietary tools and techniques. This becomes the real differentiator!

Data Quality and Master Data Initiatives March 31, 2009

Posted by Peter Benza in Data Accuracy, Data Integration, Data Integrity, Data Profiling, Data Quality, Data Sources.
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Initiatives related to master data continues to be on the radar of major corporations especially as it relates to data quality and other mission critical business processes across the enterprise that impact or relies on the quality of data being complete, accurate, and up-to-date.

What other MDM initiatives (besides Data Quality) are also paramount as part of centralizing master data for single customer view purposes.

Lets start a list:

1.) Data Profiling

2.) Data Integration

3.) Match Accuracy

4.) MDM Tools

5.) ???

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

Data visualization tool – a must watch video! August 19, 2007

Posted by Peter Benza in Data Sources, Data Tools, Data Visualization.
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Wow, check this data visualization tool out created by a non-for-profit organization.  I think we will all be seeing more of this tool to illustrate global data and other publically available data sources. 

Take the 30 minutes to watch this demo and visually watch these key data trends unfold before your eyes. 

http://sjamthe.wordpress.com/2007/06/14/gapminderorg-data-visualization-techniques/

www.gapminder.org

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.

What is a good approach to start measuring data currency as it relates to my organization? August 13, 2007

Posted by Peter Benza in Data Currency, Data Governance, Data Metrics, Data Sources, Data Synchronization.
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Frequency is a good start.  Compile the lag time between updates representing all the different “content” (data) that accompanies each software application across your organization both internally generated and externally supplied by third parties. 

Compare this to your overall file build process, make adjustments, and update your data synchronization standards to reflect any new data sources.  Warning: Be sure to consider the impact these changes may have on other departments, especially marketing.

Slick website that links you to data sources August 13, 2007

Posted by Peter Benza in Data Sources.
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Check out this website: http://graduateresearch.wordpress.com/tag/data-sources/

Go ahead and post other data sources website you might like to share with other enterprise data quality enthusiasts.