Don’t Move Until You’ve Run This Checklist 

Enterprise migrations break when people rush into execution without cleaning up what’s underneath. 

You’ve got multiple systems, overlapping owners, undocumented reports, and years of patchwork logic. Moving that without prep doesn’t fix anything — it just copies the mess. 

This checklist is built to help you avoid exactly that. 
If you’re migrating CRMs, ERPs, financial systems, or internal BI stacks — and the data under it — this is what your team should walk through before a single line gets moved. 

Let’s get into it. 

1. Plan It Right: Know What You’re Migrating 

Plan-It-Right-Know-What-Youre-Migrating

Start with a full inventory: 

  • What systems are in scope? 
  • What’s actually used vs what’s been sitting untouched? 
  • Which reports, dashboards, or APIs are depending on this data? 

Then get into schema mapping. Line up fields, formats, logic, and relationships. Make sure everything you move fits where it’s going — structurally and operationally. 

Also: 

  • Tag sensitive fields early 
  • Identify compliance implications (GDPR, HIPAA, etc.) 
  • Document system interdependencies and data refresh cycles 

Finally, get clear on ownership. 
Every dataset should have a name next to it. If it doesn’t, it’s not ready to move. 

2. Clean the Data Before You Move It 

Clean-the-Data-Before-You-Move-It

Don’t migrate clutter. Don’t migrate junk. 

Before the transfer: 

  • Remove unused tables and fields 
  • Fix nulls, broken joins, mismatched types 
  • Standardize formats (dates, currencies, text casing, etc.) 
  • Clean duplicated records and redundant tables 
  • Mask or encrypt sensitive fields 
  • Back everything up — fully, not partially 

Validate each dataset against business expectations. This ensures the cleanup is meaningful — not just technical. 

3. Execute with Structure 

Execute-with-Structure

No manual patchwork. Use scripts or pipelines. 

Your transfer should be: 

  • Logged 
  • Automated 
  • Monitored 
  • Reversible 

Plan for: 

  • Batch control (don’t overload systems) 
  • Rollback in case of failure 
  • Coordination with ops for cutover timing 
  • Downtime planning and comms with affected teams 
  • Retry scenarios + version tagging of data chunks 

Use dry runs with test volumes to find cracks before they happen at scale. 

4. Validate Like a Team That Owns the Outcome 

Data-validation

Validation isn’t a checklist item. It’s a full-phase. 

Run: 

  • Row count and schema reconciliations 
  • Field-level spot checks across high-value tables 
  • Referential integrity validation across tables 
  • Pipeline runs on migrated data 
  • Dashboard and report re-validation 
  • User acceptance testing with real scenarios 

Set up alerting and data quality monitors early — not after go-live. 

5. Post-Migration: Tighten What You Built 

Once the move is done, make it cleaner: 

  • Archive what’s not needed 
  • Optimize heavy queries and scheduled jobs 
  • Tune indices and data storage formats 
  • Set up long-term monitoring and alerting 
  • Finalize and centralize updated documentation 
  • Add ownership and SLAs for new pipelines 

Migrations should improve systems. If you’ve carried over bottlenecks, fix them now. 

Your Pre-Migration Enterprise Checklist 

Phase  What to Cover 
Plan  ✅ Inventory systems ✅ Schema mapping ✅ Identify data owners ✅ Tag sensitive fields ✅ Align stakeholders 
Prep  ✅ Clean bad data ✅ Remove stale tables ✅ Standardize formats ✅ Mask/encrypt sensitive fields ✅ Backup everything 
Execute  ✅ Script and automate ✅ Monitor batch loads ✅ Handle rollback scenarios ✅ Coordinate downtime ✅ Test small volumes first 
Validate  ✅ Reconcile records ✅ Confirm schema alignment ✅ Run UAT ✅ Check dashboards + alerts ✅ QA pipelines with business input 
Tighten  ✅ Optimize performance ✅ Archive noise ✅ Set SLAs ✅ Monitor quality ✅ Document ownership 

Final Word 

This checklist isn’t a nice-to-have. It’s what keeps migrations from turning into damage control. 

You already know what’s at stake — reports breaking, data out of sync, users losing trust. 

This is how you avoid that. 
Use it with your team. Use it with your vendors. Make it part of every migration kickoff. 

Need help implementing it end-to-end? Let’s talk. 
Data Semantics works with teams handling complex migrations across cloud, hybrid, and legacy stacks — with zero room for error.