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 As data environments grow more complex, outdated checks quietly pass risk, cost, and accountability back to the business.
Why AI’s future depends on data you can trust, and how CAT makes it real
In 2026, the AI discussion has matured. The question is no longer „should we use AI?, most organizations already are“. The real question has quietly shifted to something much harder: why does AI work well in some companies, and fail completely in others?
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.
