Data Migration Checklist: 10 Essential Steps
1. Clearly Define the Scope
Start with clarity.
Ask upfront:
What systems are involved?
What data is in scope and what is not?
Is this a one-off migration or recurring?
Unclear scope is one of the biggest causes of scope creep and delays.
2. Identify Data Owners and Stakeholders
Every dataset should have a business owner.
You need clear answers to:
Who understands this data best?
Who signs off on accuracy?
Who decides what gets fixed or excluded?
Without ownership, decisions stall quickly.
3. Profile Your Source Data Early
Never assume your data is clean.
Before migrating anything, profile your source systems to understand:
Null values
Duplicates
Inconsistent formats
Unexpected values
With Alteryx, data profiling takes minutes and immediately highlights risk areas.
4. Clean and Standardise Before You Migrate
Migrating bad data just moves problems from one system to another.
Best practice is to:
Standardise formats
Fix data types
Remove duplicates
Apply consistent business rules
Alteryx allows you to build reusable data cleansing workflows so this work is not repeated manually.
5. Define Mapping and Transformation Rules
Source and target systems rarely match perfectly.
You need documented rules for:
Field mappings
Data type conversions
Calculated fields
Deprecated or merged fields
Visual workflows make these rules easier to review and audit than spreadsheets or scripts.
6. Automate the Migration Process
Manual migration steps increase risk.
Automation ensures:
Consistency
Repeatability
Faster re-runs during testing
Alteryx is particularly effective here, especially when migrations need to be repeated multiple times before go-live.
7. Validate and Reconcile the Data
Never skip validation.
Always compare:
Record counts
Totals and balances
Key business metrics
Alteryx makes reconciliation transparent by allowing validation logic to sit alongside the migration workflow.
8. Test with Realistic Data Volumes
Small test samples rarely reveal performance issues.
Where possible:
Test with full or near-full datasets
Include edge cases and historical data
Measure runtime and resource usage
This avoids unpleasant surprises during final cutover.
9. Document Everything
Documentation is not optional.
You should capture:
Business rules
Assumptions
Known limitations
Validation outcomes
Well-documented workflows reduce dependency on individuals and simplify future migrations.
10. Plan for Ongoing Data Quality
Migration is rarely the end of the story.
Ask:
How will data quality be monitored post-migration?
Will the process need to run again?
Who maintains it?
Many organisations use Alteryx not just for migration, but for ongoing data preparation and quality checks.