Why You Should Deduplicate Data Before Any Data Migration

Data migrations are high-risk, high-cost projects. And one of the most reliable ways to make them more expensive and more likely to fail is to start the migration without cleaning the source data first.

The Cost of Migrating Dirty Data

Consider the typical migration scenario. An organisation is moving from a legacy CRM to a new platform. The legacy system has 800,000 contact records accumulated over 12 years. Nobody is entirely sure what percentage are duplicates, but it’s known to be significant.

Two approaches are common:

Migrate first, clean later. Import everything into the new system, then run a data quality project to clean it up. The problem is that the new system now has millions of duplicated relationships to navigate, users have already started working with the dirty data, and the cleaning project is far more complex because it has to work around live usage.

Clean first, migrate second. Deduplicate the legacy system before exporting. The migration moves a smaller, cleaner dataset. The new system starts clean on day one. Users never experience the dirty state.

The second approach is almost always cheaper in total — the deduplication work is simpler because the source system is a known, static quantity rather than a live production environment.

What Happens When You Don’t

Post-migration data quality projects fail at a higher rate than pre-migration ones. The reasons:

• Organisational momentum has shifted. The migration is done, the old system is decommissioned, and data quality is treated as a backlog item rather than a project with urgency.
• Users have adapted to the dirty data. After six months working with duplicate accounts, salespeople have developed workarounds. Undoing those workarounds mid-stream creates disruption.
• The migration itself may have amplified duplicates. ETL transformations can introduce new duplicates if field mapping isn’t exact, or if the source had duplicates across multiple tables that collapse into the same target table.

What Pre-Migration Deduplication Involves

The scope depends on the source system and the data volume, but the process is consistent:

1. Connect to the source system — DeDuplica supports SQL Server, PostgreSQL, MySQL, MariaDB, Oracle, Dynamics 365, and local file exports, which covers the majority of legacy CRM and ERP backends.
2. Define matching rules for the key entity tables — contacts, accounts, leads, products, whatever is being migrated.
3. Run Find Duplicates jobs against each table involved.
4. Review and process high-confidence duplicates. Route lower-confidence matches for subject-matter expert review.
5. Repeat until the duplication rate is at an acceptable level.
6. Export the cleaned data for migration.

For very large datasets, this process runs faster than most teams expect. The Standard plan supports up to 100,000 rows per job and 10,000 duplicate resolutions per month — sufficient for most mid-market migrations. The Enterprise plan handles millions rows.

Establishing a Deduplication Baseline Post-Migration

Even if you cleaned before migration, run a deduplication scan in the new system within 60–90 days of go-live. Some duplicates will always slip through. New user entry begins immediately. Integrations start flowing. Establishing a clean baseline early — before the data grows significantly — keeps the remediation scope small.

Scheduling that scan as a recurring job means you’re never again in the position of realising a data quality problem has been silently growing for two years.

Planning a migration? Talk to the DeDuplica team about pre-migration data cleanup support.