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.
Použití CAT pro testování datových agentů
V posledních měsících je jedním z častých scénářů využití AI tvorba agenta, který odpovídá na otázky uživatele na základě vašich dat. Může jít o SQL datový zdroj nebo například Power BI sémantický model. V tomto článku se nebudu zabývat tím, jak takového agenta postavit. Chci se zaměřit na jeden důležitý aspekt datového agenta – spolehlivost.
Using CAT for Testing of Data Agents
We built a data agent for one customer on top of Power BI model. However, one of the unpleasant aspects of using LLM for creating queries (in our case it was a DAX) is it can produce different DAX for same user’s question. If you repeat same question three times, it’s quite probable you will get 3 more or less different DAX queries. There are methods to increase consistency in Agent implementation however you cannot eliminate creativity of LLM model.
Regression Testing: DAX User Defined Functions
User Defined Functions is a new feature in PowerBI currently in public preview. There is a lot of buzz in the air regarding this because it opens new scenarios with modularity and reusability of the code.
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.
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.
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 […]
Automate tests to compare entities across different systems with CAT
Automating tests to compare entities across different systems can save you a lot of headaches. Imagine handling data from your ERP, CRM, DWH, and accounting systems manually. It’s a tedious and error-prone process. You’ve got customers, employees, and products scattered across all these platforms. You need to ensure the names, addresses, dates of birth, and […]
Your very first Power BI test
Power BI testing has never been simpler Do you know you can automate tests of your data in Power BI semantic models (formerly called datasets)? Let’s do it, now! Why should you care Bugs can be everywhere. It is great you tested the data in the previous stages, but if you omit testing of the “final destination”, […]
