E-commerce Product Data Migration: How to Move Your Catalog Without Losing Data

E-commerce product data migration: why most projects fail

E-commerce product data migration is one of the highest-risk projects an online retailer can undertake. Research shows that approximately 83% of data migrations exceed budget, exceed timeline, or fail to deliver intended outcomes. The consequences of failure are severe: businesses can experience up to 60% traffic drops post-launch, and downtime during peak periods can cost $100,000 or more per hour.

Yet migration is often unavoidable. In 2025-2026, 88% of enterprises plan to modernize their commerce platforms within 12 months. The "Great Replatforming" is accelerating, with significant migrations from legacy platforms like Magento, PrestaShop, and Volusion to modern solutions like Shopify, BigCommerce, and composable commerce architectures.

The critical insight is that most migration failures are planning failures, not technical failures. The technology to move data between platforms exists and works. What breaks projects is missing documentation, incomplete logic mapping, incorrect assumptions about how the new platform behaves, and unclear ownership of decisions.

This guide covers the strategies that successful migrations use to avoid these pitfalls and move product data cleanly between platforms.

Platform migration challenges you must plan for

Before diving into tactics, understand the specific challenges that derail e-commerce migrations:

The hidden complexity problem

Legacy e-commerce systems accumulate years of technical debt that does not appear in simple data exports. This includes:

• Deprecated apps and plugins that modified data in ways no one remembers
• Orphaned metafields containing data that was once critical but is now undocumented
• Outdated schemas with fields that no longer match current business processes
• Inconsistent SKU formats from multiple rounds of catalog expansion
• Spaghetti code workarounds that compensated for old platform limitations

When you export your catalog, you get the data but not the context. Understanding what each field actually means and how it is used requires investigation before migration begins.

The fragile dataset problem

Certain data types consistently cause problems because platforms handle them fundamentally differently:

Gift cards and store credits rank as the most fragile dataset in e-commerce migrations. Different platforms treat them as first-class objects, products, coupons, or payment methods. The behavioral differences are subtle but critical—a gift card that works as a product on one platform may need to become a discount code on another, with different redemption rules and reporting implications.

Loyalty points present similar challenges. Mapping errors often only surface post-launch when customers try to redeem points and discover their balances are wrong or missing entirely.

Order history seems straightforward until you realize that returns, exchanges, partial refunds, and subscription modifications all have platform-specific implementations. Support teams discover missing history when customers call about orders that no longer appear in their accounts.

The integration dependency problem

Enterprise e-commerce does not exist in isolation. Your platform connects to:

• ERP systems (NetSuite, SAP, Microsoft Dynamics) for inventory and financial data
• 3PL and WMS for fulfillment and warehouse management
• PIM systems for product information management
• Marketing platforms for email, SMS, and advertising
• Analytics tools for reporting and business intelligence

Changing your e-commerce platform's data schema can break every one of these integrations. A field that was called product_weight on your old platform might be shipping_weight on the new one—and your 3PL integration will fail silently until someone notices orders are not shipping correctly.

For guidance on handling multi-source data challenges, see our article on merging supplier catalogs into a clean structure.

Data mapping strategies for clean migration

The fundamental best practice for e-commerce migration is using ETL (Extract, Transform, Load) architecture rather than a simple "lift and shift" approach.

Why ETL beats lift-and-shift

Lift-and-shift migration copies data from your old platform to your new one with minimal transformation. This approach is faster initially but perpetuates every problem in your legacy data:

• Duplicate SKUs remain duplicated
• Inconsistent attribute values stay inconsistent
• Missing fields stay missing
• Outdated information transfers unchanged

ETL treats migration as an opportunity to improve your data:

1. Extract data from your current system with full documentation of what each field contains
2. Transform the data by cleaning, restructuring, and enriching it for your new platform's requirements
3. Load optimized data that fits the new platform's schema and takes advantage of its capabilities

Creating effective field maps

Before moving any data, create detailed field maps that document:

• Source field name and location in your current system
• Target field name and location in the new platform
• Transformation rules for converting values (e.g., "XL" becomes "Extra Large")
• Default values for fields that do not exist in the source
• Validation rules for ensuring data integrity after transformation

Test your mappings thoroughly:

1. Start with 5 sample records per entity type (products, customers, orders)
2. Verify that transformed data appears correctly in the new platform
3. Test edge cases: products with maximum variants, customers with complex order histories, orders with partial refunds
4. Document any issues and refine mappings before full migration

Deciding what deserves migration

Not all legacy data should transfer to your new platform. Migration is an opportunity to eliminate:

• Obsolete product attributes that are no longer used or relevant
• Cold customer data that creates GDPR/CCPA compliance risks without business value
• Historical orders beyond your retention requirements
• Deprecated category structures that do not match your current taxonomy
• Test data and internal records that should never have been in production

Be deliberate about what you migrate. Every piece of unnecessary data increases complexity and risk.

Preventing data loss: the migration checklist

Use this checklist to ensure comprehensive coverage during your migration:

Pre-migration audit

• Inventory all data sources: Document every system that contains product, customer, or order data
• Map all integrations: List every system that reads from or writes to your e-commerce platform
• Document custom fields: Identify all metafields, custom attributes, and non-standard data
• Audit data quality: Run quality checks to understand your starting point (use our product data quality checklist)
• Identify data owners: Assign clear responsibility for each data domain

Product data preparation

• Audit product images: Fix broken links, missing images, and incorrect associations
• Standardize variants: Ensure consistent structure across all products for the new platform
• Clean titles and descriptions: Remove outdated information and align with SEO best practices
• Validate identifiers: Verify GTINs, SKUs, and MPNs are accurate and properly formatted
• Review categories: Map your taxonomy to the new platform's category structure

URL and SEO preservation

• Freeze URL decisions: Lock down your URL structure well before cutover
• Build redirect table: Create 301 redirects from every legacy URL to its new equivalent
• Preserve canonical tags: Ensure canonical URLs transfer correctly
• Migrate meta data: Transfer SEO titles, descriptions, and structured data
• Plan for crawl budget: Submit updated sitemaps immediately after launch

Customer and order data

• Hash passwords appropriately: Ensure customer credentials transfer securely or plan for password resets
• Preserve order history: Verify historical orders display correctly in customer accounts
• Migrate loyalty data: Test point balances and redemption functionality
• Transfer subscriptions: Ensure recurring orders continue without interruption
• Handle gift cards: Map gift card balances and redemption rules to new platform

Technical preparation

• Create full backups: Secure complete backups of all data before any transfer
• Set up staging environment: Test migration in a non-production environment first
• Plan rollback procedure: Document how to revert if critical issues emerge
• Schedule maintenance window: Communicate downtime to customers and stakeholders
• Prepare monitoring: Set up alerts for errors, performance issues, and data discrepancies

For teams dealing with supplier data specifically, our guide on standardizing supplier product data with AI covers the transformation challenges you will encounter.

Post-migration data quality assurance

Migration does not end when data reaches the new platform. Post-migration QA catches issues before they impact customers:

Immediate validation (Day 1)

Run automated checks immediately after migration:

• Record counts: Verify product, customer, and order counts match expectations
• Sample verification: Spot-check 50-100 records across each entity type
• Image validation: Confirm product images load correctly
• Price verification: Ensure prices display accurately
• Inventory sync: Verify stock levels match source system

Functional testing (Days 1-3)

Test critical user journeys:

• Search functionality: Verify products appear in search results with correct attributes
• Category navigation: Confirm products display in correct categories
• Cart and checkout: Test purchasing flow with various product types
• Customer accounts: Verify login, order history, and saved information
• Integration checks: Confirm ERP, fulfillment, and marketing integrations function correctly

Monitoring period (Weeks 1-4)

Establish ongoing monitoring:

• Error tracking: Monitor for 404 errors, broken images, and failed transactions
• Search Console: Watch for crawl errors and indexing issues
• Customer feedback: Track support tickets related to missing data or incorrect information
• Analytics comparison: Compare key metrics to pre-migration baselines
• Integration logs: Review sync logs for failures or discrepancies

How Lasso ensures data quality after migration

Platform migration often reveals data quality issues that were hidden in your legacy system. Products that "worked" on your old platform may fail validation on the new one due to stricter requirements or different schema expectations.

Lasso helps teams address post-migration data quality challenges:

• Gap analysis: Identify missing attributes, incomplete descriptions, and validation failures across your migrated catalog
• Bulk enrichment: Fill missing product information using AI-powered data extraction and generation
• Standardization: Normalize attribute values, units, and formats to match new platform requirements
• Validation: Verify data meets channel requirements before publishing to marketplaces and advertising platforms

Migration is also an opportunity to improve your data operations. Rather than recreating the same manual processes on a new platform, teams can implement automated enrichment and validation workflows that prevent quality issues from accumulating again.

For teams planning a migration, explore Lasso's use cases to see how automated data quality fits into your new platform architecture. Ready to discuss your migration? Book a consultation to review your data quality requirements.