What’s your favorite wrong question?
Most data projects are still approved on trust instead of evidence. Someone says the system was tested, the invoice gets signed, and nobody asks to see what actually ran, against which data, and with what outcome. This article looks at why that question is so often missing, why that silence creates risk at board level, and why documented test results should be treated as a standard project deliverable, not a technical extra.
Old habits die hard, but your Data Testing habits shouldn’t
Many data teams still rely on testing habits they inherited years ago, not because they are effective today, but because building something better used to be too difficult. That excuse no longer holds. As data environments grow more complex, outdated checks quietly pass risk, cost, and accountability back to the business.
You Paid for the Project. Where Is the Proof It Was Tested?
When a vendor delivers a data warehouse, migration, or integration project, one question should come before final payment: where is the proof it was tested? Not verbal reassurance, not assumptions, but documented evidence showing which checks were run, against which data, and with what results. Because without that proof, you are not signing off a reliable delivery — you are accepting hidden risk that may only surface months later in reporting, AI outputs, or regulatory submissions.
The Hero’s Path in Data
After years of projects and migrations, I’ve learned that the loudest tools rarely solve the hardest problems. We keep hearing about AI, automation, and “next-gen” platforms, but when someone asks, “Is the data behind this actually correct?” — the room often goes silent.
That silence exposes the real issue: not lack of technology, but lack of verification.
Because trust isn’t a by-product. It’s a decision — earned by testing what we believe to be true.
Data Testing in the Age of AI: What’s New and What Stays the Same
AI is changing how we work with data every day. It can write code, generate reports, and analyze patterns faster than ever. That’s exciting – but it also means small mistakes can spread more quickly. Some things, however, never change: if your data is broken, your results will be broken too. Garbage in, garbage out still applies.
You might be managing BI dashboards, feeding data into AI models, or handling big data pipelines. And you’ve probably felt that sinking feeling when a small data issue throws off a report or model. AI won’t magically fix bad data – it just makes the problem more obvious. So how do we handle this new reality? Let’s look at what’s changed, what hasn’t, and how modern tools like CAT help.
CAT vs. NBi: Lessons from a Real Project with a Swiss Customer
Testing data isn’t optional anymore. When models grow, teams scale, and releases get faster, having the right testing tool becomes critical.
When engineers don’t test: Key lessons from our Data Quality meetup
At our recent meetup “How to measure Data Quality in engineering” we went deeper: technically, practically, and without fluff. This time, we focused on the role of data engineers, developers, and teams at the beginning of the data lifecycle. And there was plenty to discuss.
The discussion revealed a few recurring mistakes that unnecessarily increase risk, lower trust, and introduce chaos into companies. Here’s a summary of the most important takeaways.
Are you demonstrating your commitment to data security to clients and stakeholders?
Are you struggling to ensure that your company’s data access and security protocols are up to par? Almost everyone faces the constant challenge of managing who can see and modify data across various systems—whether on-premises or in the cloud. Automated tests with CAT are a game-changer in this arena. Not only do they streamline the […]
Top 3 challenges in data migration
Data migration is a significant task that’s essential for maintaining business operations and promoting growth. Think of it like moving to a new house: necessary, but often stressful. Automated data tests can be incredibly helpful, they make sure your data transfer goes smoothly and successfully. Let’s explore why automated data tests are so important for […]
