From Chaos to Clean - How 180 Lines of Python Cut 120 Hours from a NetSuite Data Migration
120+
Hours Saved
$12,500
Cost Saved
100%
Trial Balance Accuracy
$0
AR/AP Variance
< 0.3%
Inventory Variance
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.
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