Building a Data Mapping Strategy: From Legacy Systems to Odoo

The Problem: Data Chaos Collides with Odoo's Order

You have product data scattered across five systems. Your customer info is split between Shopify and QuickBooks. Your inventory lives in a spreadsheet. Nobody knows which is the "source of truth."

Why Data Mapping Breaks 80% of Implementations

Classic Disaster: The SKU Mismatch

Result: 60+ hours creating SKU lookup tables to reconcile the differences.

PHASE 1: Discovery & Analysis

Step 1: List All Data Sources

Step 2: Understand Odoo's Structure

PHASE 2: Field-Level Mapping

PHASE 3: Transformation Logic

IF QB.AccountName IS NOT BLANK:
    Odoo.partner_name = QB.AccountName
ELSE IF QB.ContactName IS NOT BLANK:
    Odoo.partner_name = QB.ContactName
ELSE:
    Odoo.partner_name = "Unknown - " + QB.CustomerID

import re

def normalize_phone(phone):
    digits = re.sub(r'\D', '', phone)
    if len(digits) == 10:
        return f"({digits[:3]}) {digits[3:6]}-{digits[6:]}"
    return phone

PHASE 4: Lookup Tables

TAX_LOOKUP = {
    'TAX': 15,   # Sales Tax 8.25%
    'NON': 1,    # Tax Exempt
    'OUT': 1,    # Out of State
    'GST': 22,   # GST Canada
}

def get_odoo_tax_id(qb_tax_code):
    return TAX_LOOKUP.get(qb_tax_code, 1)

PHASE 5: Dependency Chain

Real-World Example: $2M Fashion Brand

Common Mapping Mistakes

Tools for Mapping

Frequently Asked Questions