The values should first be treated as potential exceptions requiring investigation. Profiling identifies anomalies; it does not automatically prove that every unusual value is incorrect or specify the appropriate remediation.
Negative age and 263 years are highly improbable for a conventional person-age attribute, suggesting a validity or reasonableness defect. However, the Data Quality team should confirm the business definition, unit of measure, derivation logic, source mappings, special-value conventions, and intended population before changing the records.
Blindly replacing values with an average introduces fabricated data and destroys traceability. Deleting the records may also remove legitimate information elsewhere in the record. Increasing the datatype range addresses technical storage rather than semantic correctness.
A disciplined Data Quality process moves from profiling to rule validation, issue assessment, root-cause analysis, remediation, and monitoring. The business rule may ultimately specify an acceptable age range, but that rule should be governed and documented rather than inferred from individual anomalies.
Metadata is essential because it should describe whether Age is stored directly, calculated from Date of Birth, or encoded using another convention.
Reference Topics: DAMA-DMBOK2 Chapter 13 — Profiling; Validity; Reasonableness; Root-Cause Analysis; Data Quality Rules; Metadata.
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