Informatica Data Quality Blog

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?

Continue reading "Profile Early, Profile Often" »

Posted by Informatica at 08:12 AM | Permalink | Comments (0) | TrackBacks (0)

Posted by Informatica in: Data Quality

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

Posted by Informatica at 08:40 AM | Permalink | Comments (0) | TrackBacks (0)

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

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.

Continue reading "Rome Wasn’t Built in a Day and Neither is a Data Governance Initiative" »

Posted by Chris Cingrani at 09:44 AM | Permalink | Comments (0) | TrackBacks (0)

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

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.

Continue reading "You can’t have CDI without Data Quality" »

Posted by Tom Golden at 02:13 AM | Permalink | Comments (0) | TrackBacks (0)

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

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

Continue reading "IQ and Information Product Specifications Quality" »

Posted by Larry English at 04:15 AM | Permalink | Comments (0) | TrackBacks (0)

Posted by Chris Cingrani in: Data Quality > Monitoring

In my last blog post, I discussed the topic of building the business case for data quality. As such, one of the points I mentioned was the need to highlight resulting issues. Since my last post, I have had a number of discussions with clients and prospects on this topic. At the core of these discussions is the same fundamental question – what is the best way to package up the issues we uncover? In answering this, I often discuss the six dimensions of data quality (Completeness, Conformity, Consistency, Accuracy, Integrity, and Duplicates) and how to use a data quality scorecard to present the information in a meaningful way that it can be shared with key stakeholders within the organization. Although my response to this question remains the same, a conversation I overheard at the airport a couple of weeks ago made me look at the need for a DQ scorecard a little differently.

While grabbing a bite to eat prior to a flight, I overheard two gentlemen who were both retired from the newspaper business discussing how people don’t really take the time to read a newspaper like they used to. They were lamenting that people today preferred quick sound bites of information – whether it be from television or from reading one of the various news sites on the Internet.

Continue reading "Data Quality Dashboard – Capture Your Audience’s Attention" »

Posted by Chris Cingrani at 05:11 PM | Permalink | Comments (0) | TrackBacks (0)

Posted by Ivan Chong in: Data Quality > Best Practices ; Data Quality ; Data Quality > Monitoring > Metrics ; Data Quality > Monitoring

I recently asked a customer of ours why they invested so much in monitoring and publishing key performance indicators for their data quality. “Believe it or not, the biggest reason we measure data quality is not to correct bad data” came the reply. “The reason we monitor data quality is to detect problems with our business processes.”

Indeed, as I mentioned in my last blog post, business users look to investments in people and processes in addition to technology in order to address poor data quality. For example, if a bank branch manager received a report showing that customer data originating from his branch office had much higher incidents of duplicate entries and was putting the entire bank at risk of massive regulatory fines, he is not going to throw technology at the problem. His response might be mandatory training for tellers or better hiring practices to screen for adequate computer skills.

Experts in quality control methodology refer to this as addressing “root cause.” Common starting points of measurement involve completeness, accuracy, consistency, conformity, duplication, and integrity. Eventually, as the business culture matures its data quality practices, timeliness and data lineage (origination) are used to evaluate quality of data. Of course, software technology that automates the process of parsing, standardizing, matching and consolidating data is of immense value and is an absolute requirement in any data integration project. However, the issue of data quality goes beyond these IT projects. Ongoing measurement and monitoring of data quality provides value directly to the business because it helps them to better manage their people and processes.

Posted by Ivan Chong at 03:57 AM | Permalink | Comments (0) | TrackBacks (0)