Battleship Game as Integrity Illustration

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.

Special Characters in Passwords

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.

Smiley Face Corrupted USPS Scanner Data

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.

Dr. Christiana Klingenberg

With the Conformed Dimensions of Data Quality, Dan Myers has created a practicable basis for establishing data quality in companies. The concept goes far beyond the DQ dimensions. In addition, Underlying Concepts are described and named and some of the most relevant scenarios that occur in companies are described. The Conformed Dimensions thus represent a practical approach that makes dealing with data quality in companies manageable.

Danette McGilvray, Granite Falls Consulting

For the data industry, standardized definitions of data quality dimensions are needed, just as other professions, such as accounting have agreed-upon concepts and terms like “chart of accounts”. It is then up to each organization to gain competitive advantage by the way the dimensions are applied (through assessments, root cause analysis, improvements, metrics, etc.) to manage and increase the quality of the information and data on which the organization depends.

Dealing with Domain Precision and Granularity

While teaching several classes in Brisbane, Australia recently, I discussed when it is best to start thinking about each of the Conformed Dimensions of Data Quality (CDDQ) within the context of a typical waterfall Software Development Lifecycle (SDLC). We will not cover each dimension per phase here in this blog, but I thought I'd just cover Precision as an example and provide my thoughts on the other dimensions relating to each of the phases as a separate document.