A number in a board deck feels authoritative. But that number is only as good as the pipeline that produced it, and a broken join or a late-arriving record upstream surfaces as a confident, wrong conclusion downstream — usually long after anyone could trace it back.
The fix is to move quality upstream. We instrument pipelines with freshness, volume, and schema checks that fail loudly and page the dataset owner before bad data reaches a report. Transformations are tested at each boundary so a metric resolves to the same value across every team that reads it.
Trustworthy data isn't a cleanup project you schedule later. It's a foundation you build first, because everything — analytics, forecasting, AI — inherits its quality whether you invested in it or not.