Data integrity · testing
Quality assurance
Trust the number before you act on it.
Tests for pipelines, models and applications — so a silent wrong figure does not become a public one.
The usual problem
Pipelines fail quietly. A join changes, a late file arrives, a code page shifts — and the board pack is already printed.
How we approach it
We put tests at the points of failure: schema contracts, reconciliation, sampling, and the ugly edge cases. QA here is not a gate at the end. It is how the work is done.
What good looks like
- Failures you hear about before the business does
- A definition of ‘done’ that includes the data
- Fewer heroic firefights on a Monday
Typical work
- Data quality frameworks and ongoing monitoring
- Pipeline, warehouse and report testing
- Application QA for the systems that sit on the data
- Regression packs that survive a release
- Defect triage that names an owner, not a ticket pile
Tell us the problem. We’ll tell you if we can help.
A short conversation is usually enough to know whether this is a two-week discovery or a longer piece of work. No pitch deck.