Data Migration Planning Guide

Why Data Migration Is Hard

Data migration is often the most challenging part of a software implementation. It's not just copying files—it's transforming data structures, cleaning up years of inconsistencies, and ensuring nothing gets lost.

Migration Planning Steps

1. Data Discovery

• Inventory all data sources
• Document data volumes
• Identify data owners
• Understand data relationships
• Assess data quality

2. Data Mapping

• Map source fields to destination fields
• Identify transformation rules
• Document data type conversions
• Plan for missing or new fields
• Handle lookup/reference data

3. Data Cleansing

• Remove duplicate records
• Standardize formats (addresses, phone numbers)
• Fix data entry errors
• Resolve incomplete records
• Archive obsolete data

4. Migration Development

• Build extraction scripts
• Develop transformation logic
• Create loading procedures
• Build validation checks
• Document all transformations

5. Testing

• Test with sample data first
• Validate record counts
• Verify data accuracy
• Test edge cases
• Run full migration rehearsal

6. Cutover

• Freeze source data changes
• Run final migration
• Validate results
• Activate new system
• Monitor for issues

Common Pitfalls

Underestimating Data Quality Issues

Legacy systems often contain decades of data inconsistencies. Allow time for discovery and cleaning.

Missing Historical Data Requirements

Decide early: Do you need all history, or just recent/active data? Migrating everything isn't always necessary.

Insufficient Testing

Never run a migration for the first time on go-live day. Rehearse multiple times.

No Rollback Plan

Have a plan if the migration fails. Can you restore the old system quickly?

Forgetting About Users

Users need to validate that their data looks correct. Build in time for user acceptance testing.

Migration Strategies

Big Bang

Migrate everything at once over a weekend/downtime window.

• Pros: Clean cutover, no synchronization needed
• Cons: Higher risk, longer downtime

Phased/Incremental

Migrate data in stages (by department, by data type, etc.).

• Pros: Lower risk per phase, can learn and adjust
• Cons: Longer timeline, synchronization complexity

Parallel Run

Run old and new systems simultaneously for a period.

• Pros: Fallback available, can compare results
• Cons: Double data entry, extended effort

Validation Checklist

• ☐ Record counts match expectations
• ☐ Key fields populated correctly
• ☐ Relationships preserved (parent-child, foreign keys)
• ☐ Calculations and totals match
• ☐ Date formats correct
• ☐ Special characters handled properly
• ☐ File attachments/documents accessible
• ☐ User acceptance sign-off obtained