Data Quality’s Image Problem

By on

Click to learn more about author Kevin W. McCarthy.

Let’s be honest, some of us have a negative opinion around Data Quality. It is something that takes a lot of time and effort, and may not be as exciting as data projects like analytics, personalization, or Data Science. Data Quality is typically one of those tasks we put off until the last possible minute or a problem arises. 

With that in mind, I’ve got a brief story for you. Last Thursday, I asked my wife what was going on for the weekend (of course, I am a stereotypically oblivious husband who had no idea), and her one-word response was, “Yardwork”. With that one word, my balloon of optimism about a fun and exciting weekend immediately deflated. Yardwork is not fun. Yardwork is something I try to avoid, or get my teenage boys to take care of, or “outsource” to a lawncare service (when the household budget can accommodate!).


Learn about the key responsibilities you’ll have and the skills and education you’ll need with our online training program.

When Saturday morning came around, I begrudgingly started the yardwork process by mowing the lawn. Soon after, my wife asked me to break out some lawn chairs, and to go to the supermarket to get some burgers and beverages. Later in the afternoon, a cascade of our collective friends showed up at our house, and we proceeded to have a fantastic afternoon and evening eating, drinking and laughing with a great group of people. In the end, it was the kind of weekend I wanted the whole time!

So, what does my impromptu party weekend have to do with Data Quality? Data Quality is the business equivalent of “yardwork”. Where my wife should have told me, “we’re having a great party on Saturday!”, she only mentioned the foundational activity that would lead to that party – the yardwork.

In today’s corporate environments, even the mere mention of Data Quality can take the business enthusiasm for a data project (Data Governance, Master Data Management, Business Intelligence, marketing optimization, etc.) and “deflate” it in a very similar way that the word “yardwork” did for me We ask: can I get someone else to do it (like IT), or maybe we can outsource it? 

It is important to remember that Data Quality is not the end goal, but simply a preparation and/or construction activity to provide businesses with superior results for their data projects. Therefore, we shouldn’t think of it as a “Data Quality” project. It is a single customer view project providing a comprehensive reflection of our customer base to enable a superior customer experience. Or it’s a Data Governance project to ensure data consistency and secure usage in full compliance with relevant regulations. Data Quality is the enabler for your data projects and a key activity that has a significant impact on the overall success of your project. 

The struggle is, how do you convince stakeholders that Data Quality is not an “IT-only” bane of record-level tedium, but a business imperative and facilitator for a wide variety of impactful data projects throughout the organization. Is it time to change the vernacular around Data Quality? 

In some ways, it’s already been happening. Twenty years ago, there wasn’t data preparation – it was Data Quality, and there wasn’t data wrangling – it was Data Quality. Just about every “data-something” term has been taken at this point, although I’m still a holdout for “data anthropomorphizing” to emphasize the humanization of data. I know it sounds ridiculous, but just wait and see what crazy terms get added to the Data Management lexicon over the coming years!

Data Quality has an image problem. The typical conversation is around how poor the level of Data Quality is in the organization, the problems this causes with customer experience and reporting, and the cost and effort that will need to be invested to fix it. Rarely is there a conversation around how great the level of Data Quality is, and how it’s having such a positive impact on ROI and risk. It also has the disadvantage of being the foundational “second-fiddle” to the more glamorous Data Management projects listed above. How many job titles today directly reflect Data Governance? I’ve seen quite a few. Have you seen a VP of Data Quality? Rarely, and really, does anyone want that title?…

In addition, much like with yardwork, Data Quality can become easier with the right tools. From personal experience, mowing my lawn became exponentially easier when I switched from a push mower to a riding lawnmower.   Similarly, Data Quality tools that are built for the wider business with machine learning and intuitive workflows make the job much easier for a larger group of stakeholders.

The time has come for Data Quality to be recognized as the success-instilling companion to the leading data management activities. Data Quality is the Robin to the MDM Batman. Data Quality is the Chewbacca to the data governance Han Solo. Data Quality is the Spock to the single customer view Captain Kirk. Data Quality may not get the glory, but there is no denying that it has a dramatic, positive impact on your data management projects. In some cases, Data Quality is doing most of the transformation heavy lifting to bring these projects to a successful conclusion.

Leave a Reply

We use technologies such as cookies to understand how you use our site and to provide a better user experience. This includes personalizing content, using analytics and improving site operations. We may share your information about your use of our site with third parties in accordance with our Privacy Policy. You can change your cookie settings as described here at any time, but parts of our site may not function correctly without them. By continuing to use our site, you agree that we can save cookies on your device, unless you have disabled cookies.
I Accept