Why nobody tests their data
Ask almost any data leader whether data quality matters and the answer usually comes quickly. Of course it does. Bad data leads to bad decisions, broken reports, failed AI initiatives, and loss of trust across the business, and the list could easily go on… And yet, in practice, very few organizations actively test their data in a systematic or ongoing way.
This is rarely because teams are careless or unaware of the risks. Much more often, it’s because data fails in ways that are easy to overlook, hard to assign to a single owner, and difficult to define with absolute certainty. That’s why it’s worth pausing for a moment and asking why data testing so often never really gets started.
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?
From Chaos to Clarity – Automating Data Warehouse Load Troubleshooting
When a critical data warehouse (DWH) load fails, how much time do you lose? For one of our clients in the financial services sector, this wasn’t just an occasional hiccup—it was a recurring challenge that disrupted operations and delayed critical reports. The Challenge: Lost Time, Lost Insights Every time a data warehouse (DWH) load failed, […]
Why you shouldn’t “code” data tests in Python
When you are coding data solutions, sooner or later you should come to a decision point – how to test that what you did is actually doing what your stakeholders expect it to. You may get tempted to start “coding” data tests in XYZ coding language… you name it. For instance, Python, Scala, Java or […]
How to enforce rules for your data architecture?
In our data-driven age, keeping data accurate and following rules are key tasks for any data solution architect. CAT is revolutionizing data architecture by simplifying rule enforcement and testing,ensuring ongoing compliance, and preserving the integrity of diverse data systems. Understanding Data Architecture and Its Challenges Data architecture encompasses the models, policies, rules, and standards that […]
