We’re running into a recurring problem with our BI dashboards where metrics look correct at first glance but turn out to be fundamentally wrong when we dig into the logic. The latest incident involved a regional performance scorecard in Power BI that was supposed to roll up store-level metrics to area and then to region. At the store level everything checked out, but when executives filtered by area, the numbers were wildly inflated because the aggregation was summing across all stores company-wide instead of just the stores in that area.
What made it worse is that the dashboard displayed clean percentages and the visual formatting looked professional, so leadership was making resource allocation decisions based on these numbers for almost two weeks before someone noticed the discrepancy. We’ve also seen issues in Looker Studio where pre-calculated ratios like click-through rate get averaged instead of recalculated from base metrics, which completely distorts campaign performance.
We’re piloting some LLM-powered query tools and I’m worried this problem is going to get worse, not better. How are other teams validating that aggregations are logically sound before dashboards go live? Are you using semantic layers, validation pipelines, or some kind of reconciliation process against known benchmarks?