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:
Any delay or data mismatch would have jeopardised month-end close and business continuity.
Defined exactly what would be migrated and locked data to prevent late changes.
Mapped every legacy field to a NetSuite equivalent, or eliminated it if unnecessary.
Used Python scripts to pull data into structured staging databases and clean Excel files.
Weekend load, Monday go-live, and 45-day stabilisation support.
A 180-line Python + Pandas script automated repetitive cleanup tasks such as 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.
import pandas as pd
from datetime import datetime
# Load legacy data (e.g., vendor bills, customers, inventory)
df = pd.read_excel("legacy_data.xlsx")
# 1. Normalize record formats
df['CustomerName'] = df['CustomerName'].str.strip().str.title()
df['ItemCode'] = df['ItemCode'].str.upper()
# 2. Merge duplicates in master data
df = df.drop_duplicates(subset=['CustomerID', 'ItemCode'], keep='first')
# 3. Generate "Period" column from transaction date
df['Period'] = pd.to_datetime(df['TransactionDate']).dt.to_period('M').astype(str)
# 4. Handle missing or incorrect item codes
missing_codes = df[df['ItemCode'].isna()]")120+ hours
$12,500 saved compared to the original migration scope.
The client spent their first month on NetSuite improving processes, not fixing data issues.
Whether you need automation, integrations, data migrations, or custom solutions, we're here to help.
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