Most data quality tools describe what the error is. None of them describes why. Last year, I found that out the hard way — I opened a notebook, ran some queries, dug through pipeline logs, and eventually traced it back to a test account that had been deleted without cleaning up its associated orders. The fix took ten minutes. Finding the cause took three hours. What bothered me wasn't that the tool missed it — it caught it. What bothered me was that the tool handed me a one-line error and expected me to do all the detective work myself. Every DQ tool has the same blind spot I've used Great Expectations, Soda Core, and dbt tests across different teams. They're all good tools. But they all answer the same question: did this check pass or fail? That's genuinely useful. You know something is wrong. But knowing something is wrong is only the first step, and in my experience it's the easy step. The hard part is what comes after the alert fires. You open a notebook and query the failing table.…