Archive migrations rarely fail because of tooling. They struggle because of data quality.
Years of organic growth, PST imports, mergers and policy changes leave most legacy archives in less-than-perfect shape. Understanding common data-quality issues before migration reduces cost, delay and compliance risk.
Here are ten we see repeatedly:
1. Duplicate messages
Multiple ingestion paths create an inflated archive size.
Solution: Deduplicate before or during migration.
2. Orphaned mailboxes
Historic data belonging to users who no longer exist.
Solution: Establish ownership and mapping rules early.
3. Inconsistent retention policies
Different rules applied across time periods.
Solution: Audit and rationalise before migration.
4. Corrupt PST files
Legacy PSTs often contain broken indexing or partial data.
Solution: Validate and repair prior to ingestion.
5. Incomplete journal data
Journal archives may have gaps due to historic misconfiguration.
Solution: Conduct integrity checks before extraction.
6. Oversized attachments
Large legacy files inflate storage and slow ingestion.
Solution: Assess attachment policies and consider optimisation.
7. Inconsistent folder structures
User-driven archiving creates unpredictable hierarchies.
Solution: Normalise where appropriate.
8. Timezone inconsistencies
Older systems may not preserve timestamps consistently.
Solution: Validate metadata preservation strategy.
9. Broken indexing
Legacy archives sometimes rely on outdated indexing engines.
Solution: Reindex on ingestion into the target platform.
10. Legacy encryption
Older encryption schemes can complicate extraction.
Solution: Identify encryption handling requirements upfront.
Why does all this matter?
Ignoring data-quality issues leads to:
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Longer migration timelines
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Higher ingestion costs
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Frustrated users
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Legal defensibility concerns
Addressing them early enables smoother transitions and stronger governance in the target environment.
Migration success is rarely about speed alone. It’s about integrity, accuracy and defensibility.
The cleaner your data, the stronger your outcome.
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