In this article, we break down the five stages of a successful ecommerce data migration: what to check, why it matters, and how to avoid the pitfalls that can derail even the best-planned replatforms.
Audit and Cleanse Before You Move Anything
Every migration starts with an audit. The objective is simple: understand what you’re moving, how it’s structured, and what no longer belongs in the dataset.
Over years, platforms accumulate inconsistencies, duplicate customer records, inactive SKUs, corrupted order data, orphaned assets. These are small issues individually, but together they create friction during migration. Cleaning the data at source dramatically reduces transformation time and post-launch fixes.
A good audit looks beyond just “what exists.” It evaluates dependencies between entities. For example, how product IDs link to images, variant options, or inventory locations. Brands that invest time in this phase often find their go-live QA time halved because they’ve eliminated avoidable mismatches early.
Map Data Models to Shopify’s Schema
Every platform structures data differently. Salesforce Commerce Cloud, Adobe Commerce, BigCommerce, and commercetools all use proprietary data models that rarely align one-to-one with Shopify’s schema. That means mapping — translating every data object from the old system to the equivalent format within Shopify.
For example:
- Products: Product hierarchies, variant SKUs, and attributes need restructuring into Shopify’s product and variant model.
- Customers: Account IDs, order histories, and metafields must be reformatted for Shopify’s customer object.
- Orders: Statuses, payment references, and fulfilment data often require transformation to align with Shopify’s APIs.
Well-defined data mapping documents prevent surprises during import. Teams should define not just what moves, but also how it transforms including handling null values, date formats, and nested relationships.
This is where a technical partner’s experience becomes invaluable. Small schema mismatches can lead to large operational problems once live, particularly when order and customer histories are critical for reporting or loyalty integrations.
Validate and Test in a Controlled Environment
Once mapping rules are established, testing begins. Rather than migrating everything in one go, smart teams validate data integrity in a sandbox or staging environment first.
The process usually involves:
- Running a subset migration (e.g. 1–5% of data).
- Verifying relational consistency: product-to-image links, order-to-customer relationships, and tag inheritance.
- Checking for field truncation or mismatched types after transformation.
Automated QA scripts help, but manual validation by merchandisers and data owners remains essential. They’re the ones who know whether product variants render correctly or customer order histories display as expected.
Brands that rush this step often face downstream costs, from customer service issues to fulfilment errors, that far outweigh the time saved by skipping thorough testing.
Execute in Phases, Not All at Once
A phased migration minimises risk. Rather than a single “big bang” data import, most enterprise migrations follow a sequence:
- Static data (products, collections, content).
- Transactional data (customers, orders).
- Delta sync (the changes made between export and go-live).
This approach ensures that live data, especially new orders or sign-ups captured during the cutover period, isn’t lost. API-driven sync tools or middleware solutions can handle delta updates automatically, keeping systems aligned until cutover is complete.
Running parallel systems briefly can seem inefficient, but it’s safer. It allows teams to confirm that the new Shopify store is transacting correctly before legacy systems are decommissioned.
Post-Migration QA and Reconciliation
Once the new store is live, validation doesn’t stop. Post-migration QA focuses on reconciliation confirming that what landed in Shopify matches what left the old platform.
That includes:
- Record counts across products, customers, and orders.
- Verifying key fields like inventory, pricing, and tax rules.
- Comparing analytics and reporting outputs between systems.
Monitoring tools can automate some of this, flagging anomalies in near real-time. But human oversight remains crucial. Early detection of inconsistencies prevents customer-facing issues and avoids the ripple effect of bad data across connected systems like ERPs or CRMs.
The final validation step is operational, confirming that day-to-day processes (order fulfilment, refunds, stock updates) function correctly on the new data set. Once everything is reconciled, the migration can be declared complete.
Final Thoughts
Data migration is never just a technical task, it’s a translation exercise between two systems, two data philosophies, and often two eras of technology. Done right, it lays the foundation for a more efficient and scalable commerce operation. Done poorly, it introduces friction that persists long after launch.
A meticulous, phased approach with clear mapping, validation, and reconciliation ensures that brands not only protect their operational integrity but also set the stage for long-term performance gains on Shopify.
Our data migration specialists have supported over 150 global brands through complex replatforming projects. If you’re considering a platform migration, contact our expert team, who can support you through the migration process and ensure a seamless transition.
FAQs
Q1. Can historical order data be migrated into Shopify?
Yes but it often requires custom scripts or middleware because Shopify’s native import tools don’t handle historical orders well. Many teams import summaries for reporting while archiving full records externally.
Q2. How do brands ensure data security during migration?
Use encrypted data transfers (SFTP, HTTPS APIs) and restrict credentials to essential personnel. For sensitive customer data, anonymise where possible and comply with GDPR for UK and EU operations.
Q3. What’s the biggest risk during data migration?
Incomplete mapping or poor validation. A single mismatch in IDs or schema can cascade into incorrect reporting, fulfilment errors, or duplicated records.
Q4. Should brands handle migration internally or use a partner?
Internal teams can handle smaller projects, but enterprise migrations usually require partners with cross-platform experience, QA automation, and integration expertise — especially when multiple systems must stay in sync.
