When I ask data teams how they approach data quality, the answers are almost always personal:
“We built something in-house years ago, or our senior analyst set up these checks back in 2018, or we inherited the ruleset from the previous team and never really questioned it.”
Data Quality habits can be learned, but they can also be inherited from the people and systems that came before us. I’ve seen this play out repeatedly: a team adopts a set of validation rules that made perfect sense when their data volumes were small and pipelines simple. The rules stick. They become ritual. Nobody asks why anymore.
Fast forward to today. The data landscape has changed beyond recognition. AI initiatives, real-time pipelines, regulatory pressure, third-party data feeds and yet, many organizations are still running the same spot checks they always have. Not because the checks are good enough. But because there was no better tool available. Building something more robust meant engineering resources, months of development, and ongoing maintenance. The barrier was technological, not philosophical.
That barrier is gone now.
But someone always pays the bill
Think of it like a restaurant that serves you a dish you didn’t order. You complain. The waiter shrugs and says “that’s just how the kitchen works.” And then, somehow, the mistake ends up on your bill anyway.
That’s exactly what happens with bad data. Decisions get made on flawed inputs. Reports mislead. AI models train on garbage. And at the end of the day, it’s the business, the one that asked for a working solution, that absorbs the cost. In lost revenue. In failed projects. In regulatory fines. In reputational damage.
Why should the customer pay for a problem they never caused?
The answer, of course, is that they shouldn’t. But without visibility into data quality, without continuous testing, without accountability baked into the pipeline, they do. Every single time. Silently.
CAT removes much of the technological barrier that used to hold teams back. What once required a custom-built solution or an army of SQL scripts can now be done continuously, automatically, and at scale. The technological excuse is gone. What remains is a choice: keep inheriting broken habits, or finally draw a line and say: “this stops here, and we know who’s responsible.”
Data Quality is no longer just a technical problem. It’s an accountability problem. And the first step toward solving it is deciding that the bill stops landing on the wrong desk.
Ready to move beyond inherited data testing habits? CAT Studio helps teams start testing data in a more practical, automated way.
About the Author
I help organizations build trust in their data by making automated data testing a natural part of every project. At CAT, I work closely with corporate data leaders and our partners to ensure strategies, platforms, and teams rely on reliable, validated data – whether in banking, insurance, e-commerce, AI initiatives or F1 team. With more than 15 years of experience in the data field, I combine technical expertise with business insight to guide clients, mentor teams, and contribute actively to the data quality community.
