The Non-Traditional Challenges to Achieving Data Quality Success – Part 4 of 5

February 3, 2012

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.

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.

My next posting will conclude the series on non-traditional data quality challenges.  Until then….


The Non-Traditional Challenges to Achieving Data Quality Success – Part 3 of 5

January 27, 2012

In my last posting, I discussed the Data Quality Expectations Gap and considerations for overcoming it.  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 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.

In my next posting, I will discuss the Data Quality Perception Gap.  Until next time.


The Non-Traditional Challenges to Achieving Data Quality Success – Part 2 of 5

January 20, 2012

In my first posting, I introduced the concept of “non-traditional” challenges to achieving data quality success.  I have classified these 8 challenges into 4 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 marketing and selling 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 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.

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


The Non-Traditional Challenges to Achieving Data Quality Success – Part 1 of 5

January 11, 2012

As Data Quality professionals, 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? 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 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, over the course of the last 10 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. Over then next 4 postings, I will discuss each of these challenges and provide some considerations for overcoming them. Stay tuned!


Nothing Sells Data Quality, Like Data Quality Success

December 23, 2011

Some time ago, I was privy to a very active blog topic regarding CxO’s and why they don’t “get” Data Quality and its value proposition.  It attracted dozens of responses from a multitude of perspectives.  Much of the dialogue was in regards to getting senior leadership’s initial buy-in and support but it got me thinking…. even if you are fortunate enough to get the go ahead, how do you take this one opportunity and parlay it into a string of opportunities?.

At the risk of over simplifying it, I believe it comes down to one word, success.  Success is contagious.  It breeds confidence.  It encourages risk.  It attracts a crowd.  It feeds on itself.  There’s an old saying “you never get a second chance to make a first impression”, and that is especially true when it comes to Data Quality. And if you are a Data Quality professional, failure not only threatens the viability of your program, but your ongoing role in the organization as well.  In short, there’s a lot on the line.  So how do you tilt the board in your favor?  As usual, there are many factors; unambiguous scope, clear governance, appropriate skills, adequate experience, the right tools, etc.  But one factor that is often overlooked is success criteria.  How will I know if I am done and whether or not I have delivered against the stated goals?  More importantly, will I have met my customer’s perception of success? After all, that’s the only opinion that really matters.

So the next time you are undertaking a Data Quality project, in addition to clearly articulating the problem, goals and objectives, take some time to define success in measurable terms that are meaningful to your customer.  This will remove all doubt and uncertainty about the final outcome.  Every hot streak starts by getting one in a row.


For Successful Data Governance – Start Small

December 10, 2011

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.  More times than not, however, it is prudent to err on the small side and here’s why. Most organizations are more comfortable making smaller decisions, the likelihood of success is greater, and small failures are less costly (both in capital and in reputation).  Besides, if you start small and are successful, you can always grow.  But if you go big and fail, you’re odds of a second chance are diminished. So how do you keep the scale down, but still deliver something meaningful?  Follow the money, because it’s all about value.  The quote-to-cash and procure-to-pay lifecycles are rife with opportunities.  Go and speak with business and operational leaders.  Familiarize yourself with the “customer’s” strategies and objectives.  Get your hands on the IT project portfolio.  These channels are excellent sources of information about what problems the business is trying to solve.  Once you’ve captured a handful of opportunities, do some basic analysis and fact gathering.  Then short list it to the 2 or 3 you think provide the most value and the best likelihood of delivery success. Here’s your starting point.  Good luck!


Making Big Data A Little Smaller

December 2, 2011

Big Data. The term has certainly caught on and the phenomenon is real. Every nanosecond of every day, more and more data is being created at ever increasing speeds. And since storage has become so economical(both cost and foot print) there are fewer compelling reasons for the enterprise to manage down the size of its databases. In response, new and emerging technologies such as universal database connectivity, complex event processing, connectivity to social network feeds and in-memory processing have been developed to better manage Big Data’s scale. While this is great news for the enterprise, it comes with some challenges in respect to business analytics.

The natural human tendency is to seek more data points to support or validate decision making. We do it in all aspects of our life. The technologies mentioned above feed this need.  The more information I get, the more information I want – and the cycle repeats. However, without a well thought out strategy and a disciplined approach, the collection and analysis of data becomes the raison d’etre, rather than improved business performance.  The two can be mutually exclusive. The reality is that the vast majority of data that is being created or collected has little or no utility relative to that significant subset of data that drives the enterprise.  Which customers, vendors, competitors, products, assets, etc. account for the majority of revenue, transaction volume, cost, market share, etc? Start here. If you can make quicker, more informed decisions with regards to this critical subset, success will follow.