Absolutely a pipeline is only as strong as the data flowing through it. I’ve seen models and dashboards that look flawless, but when the underlying pipeline isn’t reliable, insights quickly crumble.

In my projects, I combine unit tests with pytest for transformations, schema validation with tools like Pydantic or Great Expectations to catch anomalies early, and automated checks in CI/CD to ensure broken pipelines never reach production. But beyond tooling, building trust with stakeholders is just as important everyone needs to feel confident that the numbers they’re seeing are accurate and consistent.

The trick is balancing rigor with speed. Over-testing can slow things down, but skipping validation can lead to expensive mistakes. For me, it’s all about smart validation targeted, automated, and visible to the team.