From Chaos to Clean - How 180 Lines of Python Cut 120 Hours from a NetSuite Data Migration

A client had just six weeks to migrate over 205,000 financial and operational records into NetSuite. The stakes were high:

• Strict go-live date to avoid paying for two ERP systems at once
• 10 years of messy data spread across GL, AR, AP, and Inventory
• Duplicate vendors, customers, and items
• Missing or incorrect item codes
• Legacy custom fields with no clear documentation
• Limited access to their old system before shutdown

Any delay or data mismatch would have jeopardized month-end close and business continuity.

1. Scope & Freeze Data
   Defined exactly what would be migrated and locked data to prevent late changes.
2. Data Dictionary
   Mapped every legacy field to a NetSuite equivalent—or eliminated it if unnecessary.
3. Extract & Stage
   Used Python scripts to pull data into structured staging databases and clean Excel files.
4. Clean & Transform with Python
   • Normalized record formats
   • Merged duplicates in master data
   • Converted balances and transactions into NetSuite-ready formats
5. Validate & Reconcile
   • 100% Trial Balance match
   • AR/AP aging variance = $0
   • Inventory variance within 0.3%
6. Load in Waves
   • Reference data first (COA, Vendors, Items)
   • Transactions in logical order (e.g., Sales Orders before Invoices)
7. Cutover & Hypercare
   Weekend load, Monday go-live, and 45-day stabilization support.

A 180-line Python + Pandas script automated repetitive cleanup tasks like generating accounting periods from transaction dates, removing Excel errors, and enabling instant corrections.
This eliminated 120+ hours of manual work and cut reconciliation time in half.

• Time Saved: 120+ hours
• Cost Saved: $12,500 compared to original migration scope
• Accuracy:
  • 100% Trial Balance tie-out
  • AR/AP variance = $0
  • Inventory variance < 0.3%
• Smooth Transition: Client spent their first month on NetSuite improving processes—not fixing data issues.