In my first posting, I introduced the concept of “non-traditional” challenges to achieving data quality success.  I have classified these eight challenges into four distinct Data Quality Gap areas: Expectations, Positioning, Perception and Delivery. I will explore the Expectations Gap in this posting.

The Data Quality Expectations Gap

As with any product, service or solution, it starts with understanding our “customers” and what motivates them. We then have to match our message to their expectations. This seems pretty straight forward, right? However, that brings us to the first non-traditional challenge……customers are not excited about data quality. In fact, I have yet to speak with a customer or business person who was even looking for data quality. Yet, very often that is what data quality professionals are proposing or selling. As a result, there is a mismatch between message and motivator.

Data quality is an abstract concept and its application and business value are difficult to understand and convey, even by those of us who call it our profession. So why do we expect non-DQ professionals to appreciate data quality, and why do we insist on “selling” it? Shouldn’t we design and present solutions that address our customer’s needs and expectations? This brings us to the second non-traditional challenge…….customers purchase business success, not data quality. Our customers care about increased inventory turns, reduction in days sales outstanding, improved operating margin, reduced write-offs to bad debt, cycle time reductions, reduced systems implementation risk, etc. By and large, business people care about solutions to business challenges and we need to start communicating to them in terms that they resonate with.

Until we close the Expectations Gap, data quality will continue to be sold, not bought. Trust me, there’s a big difference.

In my next posting I will discuss the Data Quality Positioning Gap. Stay tuned.