When dates are interpreted in the wrong format, another broad rewrite or another batch of output may leave the underlying problem intact. Diagnose the relevant failure before changing the whole process.
Find the likely cause
The dataset mixes regional conventions.
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
Convert dates to an unambiguous documented format.
Clean inputs make analysis and personalization more dependable by reducing ambiguity before generation begins.
Check that the repair worked
Test records where day and month could be confused.
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: missing values become fabricated facts. Preserve unknown values and define safe fallback behavior.
Monitor the useful outcome
Track invalid records, unresolved fields, and downstream errors attributable to input quality.
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.