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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!

What tool or service do you license/subscribe to for geocoding? December 27, 2008

Posted by Peter Benza in Data Accuracy, Data Tools.
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Do you use Linkedin ? April 23, 2008

Posted by Peter Benza in Data Governance, Data Hygiene, Data Management, Data Profiling, Data Quality, Data Tools.
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If you are interested in Enterprise Data Quality and want to network with other people that have similar professional interests or skills… Click on the link below and submit your name for review.  A linkedin account is required to join this network group.

http://www.linkedin.com/e/gis/67375/1F86E04EE32D

 

Enterprise Information Management Institute (EIMI) April 23, 2008

Posted by Peter Benza in Data Governance, Data Integration, Data Management, Data Profiling, Data Quality, Data Tools.
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What to learn more about EIM?

http://www.eiminstitute.org/

Here is an extract from the website – ABOUT EIMI

About EIMI

The Enterprise Information Management Institute (EIMI)’s purpose is to provide data management professionals with the most comprehensive knowledge portal and access to the industry’s most respected thought leaders on managing enterprise information assets. EIMI features a monthly electronic magazine, EIMInsight, including regular monthly columns by David Marco, John Zachman, Sid Adelman, Len Silverston, Anne Marie Smith, Larrisa Moss, Mike Jennings, and Richard Wang, with contributions by Bill Inmon.

Cognos data quality rapid assessment service January 17, 2008

Posted by Peter Benza in Data Accuracy, Data Analysis, Data Governance, Data Integration, Data Management, Data Metrics, Data Profiling, Data Quality, Data Standardization, Data Stewardship, Data Tools.
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http://www.cognos.com/performance-management/technology/data-quality/pdfs/fs-cognos-data-quality-rapid-assessment-service.pdf

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

Innovative Systems, Inc – data quality assessment tool January 14, 2008

Posted by Peter Benza in Data Assessment, Data Errors, Data Governance, Data Hygiene, Data Metrics, Data Processes, Data Profiling, Data Tools.
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http://www.innovativesystems.com/services/data_quality_assessment.php

The Innovative Data Quality Assessment provides a quick and economical evaluation of the quality of your customer information. It identifies areas where your information may be enhanced or improved, and quantifies the impact of the defined data quality issues in terms of costs, customer service, lost revenues, etc. It also benchmarks your organization’s data quality against industry standards, showing how your data quality compares to others in your industry.

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

Upcoming information quality and data management tradeshows August 25, 2007

Posted by Peter Benza in Data Integration, Data Management, Data Profiling, Data Quality, Data Research, Data Tools.
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Europe’s Most Authoritative
Data Management and Information Quality Conferences

29 October – 1 November 2007 • London, UK
Victoria Park Plaza Hotel

This year there are three major shows in one: Information Quality, DAMA International, and Meta Data.  (October 29, 2007 – November 1, 2007)

http://www.irmuk.co.uk/dm2007/

White papers on data quality August 25, 2007

Posted by Peter Benza in Data Profiling, Data Quality, Data Tools.
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Here is a link to a variety of white papers on data quality and data profiling from one of the leaders in the industry.  You will need to register, but it’s easy to do.

http://www.trilliumsoftware.com/site/content/resources/library/index.asp

Please continue to add other company links who have white papers on data quality for easy reference in the future. 

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

Data quality and plotting customer address data on a map August 19, 2007

Posted by Peter Benza in Data Analysis, Data Hygiene, Data Integration, Data Metrics, Data Profiling, Data Quality, Data Tools.
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Consider the insights and knowledge your organization will gain about the quality of its customer name/address data prior to centralizing all the desparate data sources into one location.  Here is a actual slide deck I prepared a few years ago using the output from my analysis to illustrate how maps and data profiling can assist in assessing data quality. 

SOA triggers innovation for your enterprise August 18, 2007

Posted by Peter Benza in Data Architecture, Data Processes, Data Tools.
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Say goodbye to those big applications and hello to function-specific services that IT departments can incorporate into their operating architecture with greater ease. 

This flexibility and re-design by vendors to package their solutions into bit-sized services also triggers innovation.  For example, as business requirements change both IT departments and vendors can be more responsive by offering a new service instead of waiting until the next product release.  IT can also win by telling management they are leveraging their current software asset (investment) with vendor XYZ.

How does your data quality tool handle unstructured data? August 17, 2007

Posted by Peter Benza in Data Mining, Data Quality, Data Tools.
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Be first to author an article on this subject.

What data quality dimensions can statisticans impact by using a data profiling tool. August 16, 2007

Posted by Peter Benza in Data Accuracy, Data Completeness, Data Profiling, Data Quality, Data Tools.
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Two data quality dimensions that statisticans can play a role in is data accuracy and data completeness.  A data profiling tool comes in handy to facilitate the actual research required by the organization. 

What data profiling tool does your organization use?

What other data quality dimensions can be analyzed? 

Peter Benza – 1984 graduate of the direct marketing educational foundation – creates enterprise data quality weblog August 13, 2007

Posted by Peter Benza in Data Elements, Data Governance, Data Integrity, Data Management, Data Mining, Data Optimization, Data Profiling, Data Quality, Data Stewardship, Data Strategy, Data Tools, Data Variables, Data Visualization.
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