Open DQ Repository
“What Success Looks Like With Open DQ Repository”
- Standardized DQ measurement across all systems
- Faster onboarding of DQ processes
- Enterprise-wide visibility of DQ issues
- Standarized DQ reporting/dashboards
“What Success Looks Like With Open DQ Repository”
For years those of us in the data management community argued that poor data quality has a substantial strategic and operational impact on corporations (and non-profit organizations). Unfortunately, due to the intangible nature of data and poor record keeping within organizations this argument has been difficult to support.
Case Studies using the Conformed Dimensions of Data Quality (CDDQ)
The CDDQ offers a framework for organizing training, data quality projects, programs and data quality benchmarks. On this page we'll provide these examples over time.
For a number of years now, every time I play Battleship with my son, I've thought about how that game offers so many examples of Integrity relating to data quality. As is the objective of this blog, we'll use fun everyday topics to learn about data quality. We'll use a very common table-game and explain how it illustrates the importance of connectedness and for that matter how you can ensure you have an argument-free game between your kids.
Recently, when flying Southwest, I needed to reset my password. I wouldn’t have thought Data Quality applied to such a simple task, but of course they rejected my new password. I always use a password generator to create strong passwords. By default, I create all new passwords with a decent length including all three categories (letters with mixed case, numbers and Special Characters). So when I submitted my password I was prompted with the following error.
While mailing a package the other day, I bumped into a fellow USPS (United States Postal Service) customer who said her packages had been sent back to her by the USPS. She said that the Smiley face sticker(s) on the envelope scanned by the USPS sorting machines were mistaken for a QR or barcode. I found this very interesting and humorous at the same time. I thought I’d share it with this data quality audience because it highlights data quality from a machine’s (non-human) perspective.
Need more info? See results of the last survey.