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.
The most common mistakes in engineering approaches to Data Quality
- Missing automation
Many teams still rely on manual checks or just a gut feeling that things are working. A developer deploys code and hopes for the best. But without automated tests, trust is just an illusion.
- Testing too late
If tests exist, they often come in QA or right before production. But by then, it is often too late. Fixing issues costs significantly more time and money.
- Shifting responsibility
“The testers will catch it” or “Business will verify it.” This mindset is a trap. QA should not clean up after developers. The code should be tested and functional when delivered. Business users are not backup validators.
- Invisible change does not equal flawless change
Even small edits in the ETL pipeline, data warehouse, or BI model can break results. Without tests that catch these changes, no one notices until a frustrated user reports it. And by then, it is too late.
- Lack of quality culture
Testing is often seen as extra work. It is not baked into processes or planned in timeboxes. The result is pressure, overload, and quality compromises.
What helps: automation, ownership, and common sense
We discussed practical alternatives and heard real-life benefits from teams who test from the beginning:
- Automated tests integrated into the development workflow
- Validating data changes right after commit
- Daily regression checks running hundreds of tests
- Understanding that tests mean greater trust in both team and results
- Faster releases thanks to fewer bugs
And the biggest benefit? Trust, both within the team and across the business.
What to use? Try CAT
CAT was built exactly for this purpose. It helps teams test data easily, consistently, and without manual effort.
- Works with SQL Server, Power BI, Snowflake, DataBricks, MS Fabric, ORACLE, file-based data and many others
- Data testers define tests that are then run automatically
- Requires no complicated setup neither architecture changes
- Catches issues before they hurt your business
Thinking about where to start with testing?
Get in touch. We will show you CAT in action and explain how you can start testing your data within a week. Write to us or book a demo. CAT might be the thing that finally kicks off your data quality culture.
