Different charts use inconsistent date ranges. The useful response is a targeted correction with an observable acceptance check. That keeps the repair tied to the problem instead of turning it into an open-ended redesign.
Find the likely cause
Components were configured independently.
Treat this as an explanation to verify against the actual work. Look at the input, the relevant decision, and the final result together. If the evidence does not support this diagnosis, investigate the mismatch before applying a convenient but unrelated fix.
Make the targeted correction
Apply explicit, consistent periods or label exceptions clearly.
A dashboard should help a specific audience identify what changed, why it matters, and which question deserves attention next.
Check that the repair worked
Compare the filters behind each chart.
Repeat the check on the final version that the reader or customer will encounter. An approved draft, a preview, and a published result can differ; the acceptance decision should concern the version people actually use.
Prevent the next related failure
A separate issue to watch for is this: aI dashboard summaries invent explanations. Separate observed changes from hypotheses and suggested checks.
Monitor the useful outcome
Track whether dashboard users can identify an appropriate next action without a separate explanation.
Keep a short record of the original symptom, the evidence behind the diagnosis, and the result of the acceptance check. That record makes the solution reusable when the same condition appears again, without assuming that every superficially similar problem has the same cause.