If a data cleanup silently removes useful exceptions, start by preserving one representative example. It gives the investigation a concrete reference and makes the eventual correction easier to judge.
Find the likely cause
Outliers are deleted without understanding their meaning.
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
Review unusual records before excluding them.
Clean inputs make analysis and personalization more dependable by reducing ambiguity before generation begins.
Check that the repair worked
Document why each exclusion is appropriate for the intended analysis.
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 analysis counts duplicate customers as separate people. Define a deduplication rule using appropriate reliable keys.
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.