The symptom
Two people arrive at a meeting with different revenue figures for the same month. Twenty minutes disappear into reconciling them. Nobody makes the decision the meeting was called for.
The instinct is to buy a better BI tool. The tool is almost never the problem.
Three underlying causes
No agreed definition. "Active user" means one thing to marketing and another to finance, and both definitions are correct within their own context. Neither is written down anywhere the other team would find it.
No test on the transformation. The pipeline runs, so it is assumed to be right. But nothing asserts that revenue is never negative, that the row count is within expected bounds, or that a join has not silently started dropping records.
No freshness signal. A dashboard renders happily on data that stopped updating on Tuesday. Nobody notices until someone recognises a number as stale — usually in front of a board.
What fixes it
A modelled semantic layer: definitions live in version-controlled code, reviewed like any other change, and every report reads from them. When the definition of "active user" changes, it changes in one place and everyone's number moves together.
Tests on the transformations, exactly as you would test application code. Uniqueness, not-null, referential integrity, and business assertions that encode what people believe about the data.
Freshness monitoring that pages someone. If a table has not updated in 24 hours, an engineer should learn that before an executive does.
Start from a decision
Every dashboard we build names the decision it supports and the person who makes it. If we cannot name both, we do not build it — and we have deleted a lot of dashboards on that rule.
The number of dashboards is not a measure of data maturity. The number of decisions people are willing to make from them is.