June 2006 meeting
Articles or Books Selected:
- Total Information Quality Management: A Complete Methodology for IQ Management, by Larry English. [PDF]
- Data Quality 101 (Second Generation Data Quality Systems): For IAIDQ book club, by Tom Redman. [PDF]
- A Product Perspective on Total Data Quality Management Communications of the ACM, February 1998, pp. 58-63, by Richard Wang. [PDF]
These articles by three of the most respected and well-known thought and practice leaders in the field of Information and Data Quality offer an excellent introduction to the information/data quality management and improvement. Understanding their approaches and perspectives will provide a useful framework for future Book Club readings and discussions.
See the June 2006 Reading Guide.
June meeting report:
IAIDQ members from three continents joined via teleconference for the first IAIDQ Book Club meeting on Tuesday June 6th.
Our first meeting was a vibrant discussion on articles from 3 thought leaders in information quality: Mr. Larry English, Dr. Tom Redman and Dr. Richard Y. Wang.
Feedback from participants included:
- “I really enjoyed the phone discussion. This is the sort of thing I hoped for when I joined IAIDQ”, Grant Robinson.
- “The articles were excellent and the discussion was insightful. I learned a couple of things I can apply immediately to my current work. I had been working on developing a modeling style and notation for mapping information supply chains and found Wang's article confirming”, Eric Nielsen .
- “I thoroughly enjoyed the book club session held on 6/6. Looking forward to the next book club meeting”, Diby Malakar.
A few highlights from our discussion include:
The importance of DataQ Councils: Be sure to have business involvement and senior leader sponsorship.
Data Quality with Data Suppliers: Many organizations use purchased data or data that is supplied by suppliers or partners. It is important to measure it and partner with them to improve it.
Data Quality Surveys are an effective way to learn about information customers and information producers views on key data quality criteria. The consensus was 5-8 dimensions of quality questions worked well.
Cost of poor data quality: Measuring non-quality information costs & risks can be a powerful capability. Discussion included: use of COPDQ measures, surveys and key business examples provided by the business have all been effective in identifying and communicating on this topic.
The methodologies are comprehensive and have a basis in proven quality approaches. Implementation of these methodologies in phases and company/organization tailoring is common.