Two of the more common questions that arise when trying to effectively deploy Data Governance are; “Where do I begin?” and “What business areas should I include?”. If you start too narrowly, the value and credibility of the effort is questioned. Be too aggressive, and delivery risk and scalability become a problem. As usual, success comes down to defining and managing scope. However, more times than not it is prudent to err on the small side, and here’s why… (more…)
This is the last posting in a 5 part series on the non-traditional challenges to achieving data quality. In Part 4, I reviewed the Data Quality Perception Gap. In this post, I will conclude with the Delivery Gap.
The Data Quality Delivery Gap
Once we have successfully marketed, positioned and sold our Data Quality solution, we must shift our focus to delivery. The surest way to secure additional business is to gain customer confidence and there is no better way to do this than through demonstrated competence. While there are many variables that can impact delivery effectiveness, of those that we can control, skills are the most critical. This brings us to the seventh non-traditional challenge…….successful data quality projects can be delivered with generalists. If the business needs an experienced product manager, they don’t hire a payroll specialist. Then why staff a data quality role with an accountant, or a sales operations manager, or an SQL developer? Yet, this is often what happens, and when the effort fails it is at the expense of data quality’s reputation. (more…)
In my last posting, I introduced the 4 Data Quality Gap Areas that are associated with the non-traditional challenges to achieving data quality success and discussed in some detail the Data Quality Expectations Gap. In this post, I will cover the Positioning Gap.
The Data Quality Positioning Gap
Once we have identified our customers, determined what motivates them and defined the offer, we need to market or “position” our solution. And it goes without saying that we need to do this within the context of what problem we are trying to solve. Enter the third non-traditional challenge…….data quality is incorrectly positioned as an end, rather than the means. More times than not, this is the direct result of not understanding customer motivators as outlined in my previous post on the Expectations Gap. We erroneously conclude that the customer is looking for data quality and we further perpetuate the mismatch between expectation and message. (more…)
If you haven’t been following along, in my previous posting I reviewed the Data Quality Positioning Gap as a non-traditional challenge to achieving data quality success. In this post, I will discuss the Perception Gap.
The Data Quality Perception Gap
Assuming we have properly met the challenges associated with customer expectations and solution positioning, chances are that our customer’s are still not “buying” because of the fifth non-traditional challenge…….data quality solutions are perceived as theoretical or impractical. Often times, data quality solutions appear to boil the ocean and our customers become overwhelmed with the scope and complexity or rightfully dubious of the likelihood of success. While this may not be readily apparent from the customer’s objections or from their rationale for why not to proceed, it is a leading reason why data quality solutions never see the light of day. In order to win our customers’ confidence and their business, we need to be viewed as a data quality expert. Proposing solutions that strain credulity calls this expertise into question. (more…)
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. (more…)
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