Data Quality: Are You and Your Team Prepared?

Can you imagine …

… claiming to be an engineer just because you were good at building bridges with Legos when you were a child?

… hiring anyone who knows how to use an online tax program to be a Chief Financial Officer?

… opening a medical practice because you read one medical book or were really good at growing healthy plants?

Yet that is what happens when an organization expects those with other business or technology expertise to fill in for a data quality professional role. “Just get a good data entry person and our data quality will be fine” is a phrase often heard. But data quality requires much more than data entry.

The “perfect” data quality professional can talk to the CEO about strategy and business value as easily as digging into details in a data model or information life cycle, discussing the latest technology trends and writing code. No one person has that breadth and depth and there is no such thing as a “perfect” data quality professional.

Of course, data quality efforts will benefit from any experience in business, data, and technology fields. Data is a bridge between individuals, departments, functions, and other disciplines. Data quality professionals also deal with the human element of the work, so the ability to communicate, present, listen, negotiate, work as a team (the list goes on) are also important.

Unfortunately, when someone has been put in charge of data, too often they don’t realize that a profession exists with depth of experience and knowledge. And the manager that put them in charge might not know it either.

Everyone can increase their skills. As an individual, take the initiative to improve. Take advantage of learning opportunities. Read books, sign up for courses, in person or online. Attend conferences, webinars, and industry chapter meetings. Look for university programs in data quality and data management. Find resources to help gain the foundational knowledge necessary for data quality work. Get training on and use a proven data quality methodology like my Ten Steps™ **, which gives everyone a jumpstart, so the bulk of their efforts are spent applying the methodology to fit their particular needs, not reinventing the basics.

Proactively approach management to help pay for courses and/or time off work. Show how this benefits your organization. If you still don’t get support, don’t let it stop you from improving your skills on your own. As a manager, look for the same opportunities to increase the skills of your team members.

Let me end here with a concern I have had since AI gained general popularity, particularly generative AI such as ChatGPT. Too many times, AI answers to my questions have resulted in something that is almost – but not quite – right. Maybe the answer is only half right. Looks good, sounds good – but it is wrong. What is going to happen when those of us who can tell the difference are gone? Who is going to ensure the quality of the data that underlies everything we do – as individuals, organizations, and societies? We need more data quality professionals and more data professionals generally. We need as many university programs focused on data as there are engineering, computer science, and MBA programs.

I have devoted over 30 years to my data profession. I won’t be around forever, but data will be. And I hope many of you will have the knowledge, skills, and experience to continue leading the way. Are you prepared?

** See Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™, 2^nd Ed (Elsevier/Academic Press, 2021) by Danette McGilvray.