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May 15, 2008

Identity Systems Acquisition – the Next Evolution of Data Quality

Posted by Chris Cingrani in: Data Quality > Benefits

Chris Cingrani
On May 15th, Informatica completed the acquisition of Identity Systems, a pioneer in identity resolution technology allowing customers to search and match identification data across multiple systems and more than 60 languages. As someone who has been in the data quality space since 2001, I remember encountering Identity Systems in various sales cycles when they were known as Search Software America (SSA). In each instance, I recall that the level of sophistication around matching they offered was something that was difficult to compete against. If we could expand the data quality discussion to include other aspects, such as cleansing and validation, we had a much better chance in the proof of concept or sales cycle.

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May 09, 2008

Data Quality Maturity Model – How Does Your Organization Rate?

Posted by Chris Cingrani in: Data Quality > Best Practices

Chris Cingrani

Recently I spoke at a User Group Meeting on the topic “Align for Success: The critical part Data Quality plays in complex Business and IT Initiatives.” I began the discussion by polling the group to find out how many of the organizations represented had a data quality solution in place. The response to the question was mixed, with approximately half the audience indicating they either had a solution or were considering one, while the other half indicated they weren’t currently considering data quality (or the person was unaware of any data quality initiatives). Although this was a very unscientific survey, it set the tone for my presentation, as I attempted to explain the concept of a data quality maturity model. By understanding where an organization is today from the standpoint of the model, management can begin to develop plans as to where they want to end up both in the short and long term.

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April 08, 2008

Profile Early, Profile Often

Posted by Informatica in: Data Quality

Dr. Claudia Imhoff, President & Founder, Intelligent Solutions and Ed Lindsey, National Product Specialist, Informatica answer some questions that were raised during our recent web seminar. If you missed the web seminar, you can listen to it by clicking the following link:

An Eye-opener for Your Business: How Data Profiling Can Build Support for Data Quality within Data Management Projects


Q: Who in the organization should be responsible for data quality? And who should sign off on the scope document?

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March 12, 2008

Seeing is Believing

Posted by Informatica in: Data Quality

Claudia Imhoff, PhDi
You know the expression "Seeing is believing"? Well, it is even more true for data management projects. Getting sponsorship much less funding for data management projects like data quality, CRM, MDM, data warehousing, or other such enterprise-encompassing initiatives is very difficult at best. What should you do?

Today I am doing a webcast with Informatica on just this topic. The answer lies in your ability to demonstrate the true state of the data -- "seeing" the problems by performing data profiling on your data. Nothing says "Houston, we have a problem" more than viewing the profiling results. It will make believers out of the most difficult people!

I hope you enjoy the presentation.

Update: Questions and answers from this event can be in the following post - Profile Early, Profile Often

February 15, 2008

Rome Wasn’t Built in a Day and Neither is a Data Governance Initiative

Posted by Chris Cingrani in: Data Quality > Governance / Stewardship

Chris Cingrani
In my previous posts, I have discussed building the business case for data quality as well as the role that a data quality dashboard plays in supporting this case. As previously noted, these efforts will directly impact your ability to articulate the need to pursue a data quality initiative. The reason for returning to this topic is that I have recently participated in multiple discussions with a variety of companies that were either in the process of forming a data governance council or in the process of building the internal business case to support exploring a data governance initiative. In these discussions two common threads were present – the role of data quality in the data governance initiative and the need to change the culture within the organization if data governance is going to succeed. Although these are only two aspects to consider when pursuing a data governance initiative, they are directly tied to the underlying success or failure of the program.

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February 01, 2008

You can’t have CDI without Data Quality

Posted by Tom Golden in: Data Quality > Benefits ; Data Quality > Best Practices ; Data Quality ; Data Quality > Technology

Tom Golden
Looking in Webopedia.com recently I came across a definition for CDI. Yes webopedia.com - it bills itself as the #1 online encyclopaedia dedicated to computer technology. You might wonder what I was doing surfing this font of knowledge – well I had time on my hands between delayed flights coming back to Europe from the US. You know what they say “time to spare, travel by air.”

The Webopedia.com CDI definition went: “Short for Customer Data Integration, it is the combination of the technology, processes, and services needed to create and maintain an accurate, timely and complete view of the customer across multiple channels, business lines, and, potentially, enterprises, where there are multiple sources of customer data in multiple application systems and databases.”

A bit long winded perhaps, but the three words that shone out at me through the glare of the florescent lights in San Francisco airport were “accurate, timely and complete”; all data quality issues. Despite this, few if any of the Customer Data Integration (CDI) vendors in the market today have truly addressed the data quality issues in their CDI solutions. And anyone who has gone down the route of developing their own custom-built CDI application will be all too familiar with the data quality demands involved.

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January 20, 2008

IQ and Information Product Specifications Quality

Posted by Larry English in: Data Quality ; Data Quality > Vertical Solutions

Larry English
One of the root causes of poor quality information is defects in the data definition, specifically the “information product specifications.” Because information is a product of our business, manufacturing and service processes, the analogy of an “information product” is real, and the requirement for quality in “information product specifications” is a critical requirement for Information Quality.

This blog is the first of a series of three blogs on the critical quality characteristics (or measures) of information quality required to achieve Total Information Quality Management.

1. Information Product Specification data quality
2. Data content quality
3. Information presentation quality

What constitutes the “Information Product Specifications” data?

• Information standards
• Data names
• Data definitions
• Attribute valid value set or range of values
• Value format for structured attributes (VIN, SSN, Product Codes)
• Business rule specifications of constraints on data
• Information Steward accountable for data definition quality

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