The
Non-Traditional Challenges
to Achieving Data Quality Success
April 2010: Originally published in IDQ Newsletter Vol 6 Issue 2
Richard Trapp
Introduction
The 8 Non-Traditional Challenges to Achieving DQ Success
DQ Expectations Gap
- Customers are not excited about DQ
- Customers purchase business success, not DQ
DQ Positioning Gap
- DQ is incorrectly positioned as an end, rather than the means
- DQ is primarily positioned as a technology and not a business solution
DQ Perception Gap
- DQ solutions are perceived as theoretical or impractical
- DQ solutions are perceived as creative ways not to address the problem
DQ Delivery Gap
- Successful DQ projects cannot be delivered with generalists
- Not all DQ professionals are equipped with the proper mindset
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.
The Data Quality Expectations Gap
As with marketing and selling any product, service, or solution, we must start 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 applied meaning 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-data quality professionals to appreciate data quality, and why do we insist on "selling" it? Shouldn't we define 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. We need to start communicating in terms that resonate with our customers. Until we close the Expectations Gap, data quality will continue to be sold, not bought. Trust me, there's a big difference.
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 the 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 the previous section on the Expectations Gap. As a result, we erroneously conclude that the customer is looking for data quality and we further perpetuate the mismatch between expectation and message.
However, if we have done a good job understanding our customers and their expectations and even realize that data quality is an enabler and not the real business goal, we still have to overcome the fourth non-traditional challenge… data quality is primarily positioned as a technology and not a business solution.
Often times I see data quality professionals leading with technical features and functions instead of business benefits. For example, terms like entity resolution, standardization, normalization, enrichment, and domain integrity all ring hollow if they are not positioned relative to the business problem our customers are attempting to solve. Let's be honest, a VP of Sales and Marketing wouldn't recognize domain integrity in product data if it jumped up and bit her, nor should she.
The Data Quality Perception Gap
Assuming we have properly met the challenges associated with customer expectations and solution positioning, chances are that our customers 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.
Even if we are successful in proposing a practical and actionable solution, we need to be mindful of the sixth non-traditional challenge… data quality solutions are perceived as creative ways not to address the problem.
If the customer's data quality problem can be solved by targeted data cleansing in the source system, then propose a solution that does just that. If the customer is unsure of the degree and impact of their data quality gaps, then propose a data quality solution to help them quantify and qualify their data quality issues. It is never a one size fits all and there's no quicker way to lose credibility than to propose a solution that doesn't address the customer's needs.
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 cannot 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.
But when it comes to effective data quality delivery, having the right skills is only part of the human resource equation. Which brings us to the 8th and final non-traditional challenge… not all data quality professionals are equipped with the proper mindset.
In a nut shell, data quality success should not be measured by the amount of data defects cleansed, but rather the degree of business improvement achieved. Having data quality professionals who understand and embrace this perspective is integral to any meaningful data quality success.
If data quality is to claim its position as a valued business discipline, we need to recognize that there is more to it than just getting a few smart people in a room. Doing otherwise devalues the proposition and diminishes our profession.
Conclusion
By focusing on these data quality gap areas, a better appreciation for data quality’s value proposition will start to take hold in your organization and the old, traditional challenges will seem… less challenging.
Copyright © 2010 Richard Trapp
About the Author
Richard Trapp is the founder and Managing Partner of J Baron Group, LLC, a provider of strategic data quality consulting services.He is a highly accomplished Data Quality practitioner and proven Business Leader with more than 17 years experience in business development and business optimization.
Prior to joining J Baron Group, he was director of global data quality for a Fortune 500 provider of business communications solutions and services, where he designed and implemented an Enterprise Data Quality Program including organizational design and governance, operating framework and detailed process designs for full suite Data Quality service offerings. He has successfully planned and led numerous Data Quality strategies and initiatives with dozens of core team members, hundreds of extended team members and nearly 500 thousand Data Quality service hours.
Richard regularly presents at industry events on the business benefits of Data Quality and is a guest blogger on Informatica Perspectives. He leverages his unique business perspective and Data Quality operations experience to develop and deliver complex, value-driven solutions.
Richard is a charter member of the International Association for Information and Data Quality (IAIDQ) and an active member of the Integration Consortium. He has received a certificate from MIT in “Principles and Foundations of Information Quality.”