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Keynote for MDM SUMMIT Fall 2008 in NYC April 25, 2009

Posted by Peter Benza in 1, Data Governance, Data Hygiene, Data Integration, Data Management, Data Stewardship, Linkedin.
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As part of some ongoing research I have been conducting, here is a slide deck I came across from 2008 – the keynote for the CDI-MDM Summit this past fall in NYC. Re: Key Trends

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Dots On A Map Improve Data Quality April 18, 2009

Posted by Peter Benza in Data Accuracy, Data Hygiene, Data Integrity, Data Management, Data Mining, Data Profiling, Data Quality, Data Standardization, Data Stewardship, Data Types, Data Visualization, Linkedin.
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This was a presentation I originally prepared back in 2005, but is probably even more applicable in 2009 given the impact using a GIS tool can have on visualizing data quality – customer addresses on  a map! The next time you conduct a customer “data” assessment – try this!

Where Is Cognizant Showing This Year? October 23, 2008

Posted by Peter Benza in 1, Data Governance, Data Hygiene, Data Integration, Data Management, Data Profiling, Data Quality.
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Here are some of tradeshows Cognizant has been attending:

http://www.cognizant.com/html/news/events.asp

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

 

Human Inference – an international data quality solution provider February 11, 2008

Posted by Peter Benza in Data Governance, Data Hygiene, Data Integrity, Data Management, Data Metrics, Data Processes, Data Profiling, Data Quality.
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From the website:

Human Inference discovered that to reach the desired results, mathematical logic is not sufficient. The knowledge about the language and culture of a country was necessary as well. Human Inference proved to be right, since today the largest companies of the world are using our knowledge-based software to improve the quality of their data.

http://humaninference.com/services/professional-services/data-quality–assessment/

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.

What types of common data problems are found in your master data? January 13, 2008

Posted by Peter Benza in Data Analysis, Data Assessment, Data Governance, Data Hygiene, Data Metrics, Data Profiling, Data Quality.
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Master Data exists across your entire enterprise.  Companies today are assessing what is the best way to consolidate all their information assets (data sources) into a “single customer view”.

What types of data problems exist in your organization today or the future with the move towards managing data at the enterprise level?

[Be first to answer this question]

What type of data quality reports does your organization publish on a regular basis? January 13, 2008

Posted by Peter Benza in Data Assessment, Data Hygiene, Data Management, Data Profiling, Data Quality.
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[be first to answer this question]

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

International association of information and data quality website September 19, 2007

Posted by Peter Benza in Data Governance, Data Hygiene, Data Management, Data Quality, Data Stewardship.
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www.iaidq.org

Firstlogic’s data quality blog launches in 2005 September 19, 2007

Posted by Peter Benza in Data Hygiene, Data Management, Data Quality.
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http://findarticles.com/p/articles/mi_pwwi/is_200507/ai_n14753212

Purisma delivers breakthrough solutions for MDM September 19, 2007

Posted by Peter Benza in Data Governance, Data Hygiene, Data Integration, Data Management, Data Quality, Data Variables, Data Warehouse.
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Here is an partial extract from this announcement:  

(see link below to read the entire article)

Bob Hagenau, Cofounder and VP of Products and Corporate Development for Purisma, recently participated in an exclusive interview with Claudia Imhoff and the Business Intelligence Network ( http://www.BeyeNETWORK.com ). In this interview, Hagenau discusses Purisma’s solutions-driven approach to master data management (MDM), new features of Purisma Data Hub 3.0 and Purisma Business Data Appliances — preconfigured MDM applications that allow businesses to solve specific point problems.

http://findarticles.com/p/articles/mi_pwwi/is_200709/ai_n19509669

Data quality connector for Siebel by Group 1 Software September 16, 2007

Posted by Peter Benza in Data Hygiene, Data Integration, Data Management, Data Profiling, Data Quality, Data Standardization.
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See this podcast immediately by linking to the demo below:

http://www.g1.com/Resources/Demos/DQC/index.html

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. 

Great definition: about data integrity August 18, 2007

Posted by Peter Benza in Data Hygiene, Data Integrity.
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I came across this definition from the Data Lever website about data integrity.

“Data integrity involves more than just data cleansing, deduplication, householding, CASS certification, and geocoding. You need information that is consistent, complete, accurate and relevant. To get there, you need an enterprise-class data quality solution that can handle correction, validation, and enhancement of all your data—no matter where is comes from. You need it to be easy to implement, efficient to operate, at a price you can afford.”

www.datalever.com 

What non-for-profit associations are there to learn more about global data quality issues? August 17, 2007

Posted by Peter Benza in Data Hygiene, Data Quality, Data References.
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Be first to author an article in this area!

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.

Do you know any data hygiene solution providers that include non-postal address ranges with their reference data? August 13, 2007

Posted by Peter Benza in Data Hygiene.
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Group 1 Software is a good example of one company who licenses a advanced geo-coding solution offering that goes beyond using just the USPS supplied (Zip+4) street ranges. 

A good example of a non-postal street address are those streets in rural areas across America that have not yet been converted to meet the 911-compliant standards.  In order words, not all addresses in rural areas have a house number associated with their address.  Also, note a PO Box styled address is typically used in small communities where individuals/businesses will go to pick up their mail daily.

Other companies also providing non-postal street addresses are: Trillium Software, First Logic, and Proxix.