A cloud retailer relies on a BI dashboard fed by an hourly incremental ETL job from the transactional system. During a walk-through you notice that the dashboard's revenue totals have been identical for the past three hours, yet the ETL job status page shows the most recent run succeeded. As the analyst assigned to validate the report, which action will best determine whether stale data in the underlying dataset is the root cause?
Recalculate the revenue KPI using a different aggregation method to rule out formula errors.
Reduce the dashboard's auto-refresh interval from 15 to 5 minutes in the BI tool settings.
Clear the browser cache and reload the dashboard to eliminate client-side caching.
Query the warehouse table that feeds the dashboard and compare its row count and latest updated_at timestamp with the corresponding source table.
The most direct way to confirm a stale-data problem is to interrogate the dataset that drives the dashboard and compare it with the source system. By checking the record count and, more importantly, the maximum value of an updated_at or ingestion-timestamp column, you learn the exact moment the warehouse table last received new rows. If that timestamp lags the current time by several hours while the source system continues to accumulate new transactions, you have objective evidence that the dataset-and therefore the dashboard-is stale. Simply shortening the dashboard's refresh interval or clearing a browser cache only affects the presentation layer; neither verifies the freshness of the data itself. Rebuilding calculations changes business logic, not data currency, and thus does not test for staleness.
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