The Non-Traditional Challenges To Achieving Data Quality Success
It seems like we are continually 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 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 resources, 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 8 “non-traditional” challenges that I believe present an even greater barrier to data quality success.
I have classified these 8 challenges into 4 distinct data quality gap areas: Expectations, Positioning, Perception and Delivery.
This article discusses these non-traditional challenges and some considerations for overcoming them.