As another year comes to an end it seems like we are confronted with many of the same barriers that we faced years ago when it comes to positioning and achieving the “promise” of data quality. So why has data quality been slow in gaining traction as a valued and integral part of the business operating model? As data quality professionals, what can we do to overcome this inertia and advance the data quality culture?
Before we can attempt to answer these questions, we need to be able to recognize the challenges that are impeding our progress. If you ask any data quality professional to identify the key data quality challenges that they face, the list will invariably include: lack of sponsorship, unclear ownership, environment complexity, high volumes, limited documentation, prohibitive cost, insufficient skills, inadequate tools, etc. These are the “traditional” challenges that most everyone cites and they are certainly real.
However, in my experience over the last several years I have identified eight “non-traditional” challenges that I believe present an even greater barrier to data quality success. My next four postings will discuss these challenges and some techniques for overcoming them. Stay tuned.
Richard Trapp is the founder and Managing Partner of J Baron Group, LLC
