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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!

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

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

 

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.

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/

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/

SOA Governance At Bea: Essential to your enterprise transformation strategy January 17, 2008

Posted by Peter Benza in 1, Data Analysis, Data Architecture, Data Governance, Data Integration, Data Management, Data Optimization, Data Profiling, Data Security, Data Stewardship.
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Effective SOA governance is an essential element in any enterprise transformation strategy. It can help your organization achieve measurable, sustainable business value.

Read about this and other webcasts, whitepapers, etc… at Bea.

http://www.bea.com/framework.jsp?CNT=index.jsp&FP=/content/solutions/soa_governance/

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]

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

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

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

What is the first piece of advice you would give someone starting a data governance program? September 18, 2007

Posted by Peter Benza in Data Governance, Data Management, Data Metrics, Data Stewardship, Data Strategy.
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The link between Data Quality and Data Governance September 18, 2007

Posted by Dan Power in Data Governance, Data Quality, Data Stewardship.
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I suppose it’s possible to have an enterprise data quality program without having a data governance program.  But I think having an enterprise-wide data governance program which includes data quality as one of its core focus areas makes more sense.

Data quality without data governance means having to correct the same errors over and over again, and not fixing the problem at the source. But data quality with data governance means bringing the data governance council up to speed on the issue, inviting the different business owners to present their views, and then defining a solution that will be funded, supported and enforced.

Data quality without data governance can be a “solution in search of a problem” – where an IT department adopts data quality tools without directly involving the business, and applies them “in the back room” so to speak.  But data quality tools within the context of a corporate data governance program means the business is fully involved from the beginning, from forming the data governance council, to staffing the data management organization with business data stewards, to providing some or all of the funding, etc.

I’ve been encouraging clients lately to start by defining a data governance program, then to build the data governance council and the attending processes, then to start thinking about individual projects underneath the banner of the data governance program.  That could include projects like implementing specific data quality tools or a Master Data Management hub.

The holistic approach that I generally recommend recognizes that for most business problems, there’s going to be:

  • a People aspect (requiring organizational change, with impacts on corporate culture, accompanied by a political dimension)
  • a Process aspect (as the organization realizes that treating information as a true corporate asset means some new work is going to have to be done by someone)
  • a Technology aspect (usually requiring new platforms, applications or tools)
  • an Information aspect (enriching internal data with trusted external content, for example)

This holistic approach to data governance usually involves wholesale improvements in data quality and in integration. If you want to better capture, store, manage and use information, you’re going to need better data entry procedures, better integration between applications and databases, and an efficient, automated way to find & correct incorrent data.

But you’re never going to be able to get away from the People side of things.  People need to be convinced of the importance of data quality as an issue, and of the need for a comprehensive data governance framework as the best long term way to fix data quality problems.

10 Critical factors for successful enterprise data quality September 16, 2007

Posted by Peter Benza in Data Governance, Data Integration, Data Management, Data Profiling, Data Quality, Data Strategy, Data Warehouse.
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These 10 points were written by Len Dubois, VP of Marketing for Trillium Software over five years ago and his guidance still holds true today in September 2007. 

I recommend, if you need a refresher or want to read this article for the first time – its time well spent.  I have met Len in person – be sure to look for him at the next data quality trade show – you will be pleased you did.

http://www.tdwi.org/research/display.aspx?ID=6341

What department should data stewards reside in… IT or business? August 19, 2007

Posted by Peter Benza in Data Governance, Data Stewardship.
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What kind of long-term strategy to data quality can organizations fund? August 17, 2007

Posted by Peter Benza in Data Governance, Data Management, Data Quality, Data Strategy.
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What are some examples of how a data system can become unstable? August 17, 2007

Posted by Peter Benza in Data Errors, Data Governance, Data Management, Data Processes, Data Synchronization.
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How is data governance and compliance being incorporated into your overall data management practices? August 16, 2007

Posted by Peter Benza in Data Governance, Data Management.
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How complete is your data? August 15, 2007

Posted by Peter Benza in Data Completeness, Data Governance, Data Quality, Data Research.
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Data completeness is contingent upon first knowing the target population* relative to the number of missing data elements (bad values) to good values by data element. 

Consider analyzing over time and set up on a scheduled basis missing value reports (better yet aggregate datasets) to study over time data completeness patterns.  These findings might also reveal other data governance processes, policies, and standards in your organization for consideration.

It is advised to include a statistical analyst early on in outlining this process in order to help define data completeness specific to your organization – past, present, and future.

*target population could be anything from your customer name/address customer master database to product-specific datasets and all their associated attributes.

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.

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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What topics comprise of Data Governance? August 13, 2007

Posted by Peter Benza in Data Governance.
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