Obsessive-Compulsive Data Quality (OCDQ) by Jim Harris is an independent blog offering a vendor-neutral perspective about data quality and its related disciplines, including data governance, master data management, and business intelligence.
Vendor neutrality doesn’t preclude the possibility of vendor-related or vendor-sponsored content. However, if vendor products or services are presented or discussed, it will be done in an objective manner — never promotional — and any vendor sponsorships are always disclosed. The goal of this blog is to foster a marketplace of ideas about data quality in which a diversity of viewpoints is freely shared without bias. Everyone is invited to get involved in the discussions and debates and have an opportunity to hear what others have to offer. This blog’s comments are moderated, however, to prevent personal attacks, inappropriate language, disrespectful behavior, vendor advertisements, or excessive self-promotion, as well as to block spam.
I am often asked about the name of this blog. First, and foremost, I want to make it clear that the name of this blog is in no way intended to trivialize Obsessive-Compulsive Disorder (OCD), which is a serious anxiety disorder. I have personally suffered from chronic low-grade depression most of my adult life, and therefore I would never make fun of any category of mental disorder.
Since many of my colleagues over the years would often describe me by saying “Jim is so obsessive-compulsive about data quality — but in a good way,” when evaluating the list of potential names for this blog, it was not surprising to anyone that I finally chose Obsessive-Compulsive Data Quality (OCDQ).
Furthermore, I do not believe that I am alone in my obsessive-compulsive tendencies regarding data quality and its related enterprise data management disciplines. OCDQ affects millions of people worldwide, and its most common symptoms are:
- Obsessively verifying data used in critical business decisions
- Compulsively seeking an understanding of data in business terms
- Repeatedly checking that data is complete and accurate before sharing it
- Habitually attempting to calculate the cost of poor data quality
- Constantly muttering a mantra that data quality must be taken seriously