The Hidden Risk in Data Migration: Duplicate Sources and How to Handle Them
When organizations migrate data from legacy systems to new platforms, the spotlight often falls on mapping fields, transforming values, and ensuring completeness. But one critical challenge lurks in the background: duplicate sources of data. Overlooking duplicates can inflate storage costs, break downstream integrations, and undermine user trust in the new system. That’s why identifying, de-duplicating, and testing for duplicates should be a core part of every migration strategy.
Why Duplicate Sources Matter
Duplicate data sources occur when the same information is stored in multiple systems—or even in multiple tables within the same system. For example:
Customer records stored in both a CRM and a billing system
Employee information duplicated across HR, payroll, and security systems
Product catalogs split across different departmental databases
Migrating these records without addressing duplication leads to:
Conflicting values (e.g., two addresses for the same customer)
Redundant rows that skew reporting and analytics
Increased complexity in reconciling business processes after migration
The result? Stakeholders lose confidence in the new system because it doesn’t feel like a “single source of truth.”
Step 1: Identifying Duplicates
Before migration, run a comprehensive analysis across all data sources. Effective methods include:
Schema comparison: Identify overlap between systems that store similar entities.
Data profiling: Run uniqueness checks, frequency counts, and pattern analysis to detect likely duplicates.
Fuzzy matching: Catch near-duplicates, such as “Robert Smith” vs. “Bob Smith,” using phonetic algorithms or similarity scoring.
Stakeholder input: Confirm with business teams whether records that look similar are actually duplicates or legitimate variations.
This step requires both automation (tools for matching and profiling) and human judgment to avoid false positives.
Step 2: Determining How to De-duplicate
Once duplicates are identified, the harder question is how to resolve them. Options include:
Merging records: Combine attributes from multiple records into a single “golden record.”
Prioritizing systems: Choose one system as the source of truth when conflicts arise.
Rule-based resolution: Apply logic such as “use the most recent update” or “prefer active over inactive records.”
Manual review: Escalate uncertain cases to data stewards for decision-making.
Document these rules clearly—future audits and troubleshooting efforts will depend on knowing exactly how duplicates were handled.
Step 3: Testing the Results
De-duplication is only successful if it holds up in real-world use. Testing should include:
Record counts: Validate that the number of records after de-duplication aligns with expectations.
Spot checks: Manually review samples of merged or removed records to ensure business rules were applied correctly.
Downstream validation: Run reports and business processes (such as invoicing or payroll) in the target system to confirm that duplicates no longer cause issues.
User acceptance: Get business users to validate that the “clean” data meets their operational needs.
Testing isn’t just about catching errors—it builds trust with stakeholders who will rely on the new system.
Conclusion
Duplicates are more than just an annoyance in a migration project—they’re a risk to data quality, business operations, and user adoption. By systematically identifying duplicates, applying clear de-duplication strategies, and rigorously testing outcomes, organizations can ensure their migration delivers on its promise of a reliable, unified data environment.
The next time you plan a migration, ask yourself: Have we really addressed duplicates? If not, it’s time to make it a priority.
