If you’ve been through a platform migration—or talked to anyone who has—you’ve heard the story. The timeline that seemed reasonable at kickoff stretched. The “simple” data transfer revealed unexpected complexity. The launch date moved once, then again.
This pattern is so consistent that it’s worth understanding why. Migration timelines don’t slip because of incompetence. They slip because of the inherent nature of moving from one complex system to another.
The Discovery Problem
Migrations begin with incomplete information. You know your current store from the outside—how it looks, how it works. But you don’t fully know its internals until you start pulling it apart. Legacy customizations, undocumented integrations, data quirks, and hidden dependencies reveal themselves during the work, not before.
Every discovery creates decisions. Some are quick; others require significant discussion. Each one takes time that wasn’t in the original estimate because it couldn’t have been.
Data Is Messier Than It Looks
Data migration seems straightforward: move customer records, order history, product information. But data is never clean. Fields don’t map one-to-one. Historical records have inconsistencies. Custom fields in the old system have no equivalent in the new one.
Decisions about data—what to bring, what to leave, how to transform it—take time. Testing data integrity takes more time. Fixing issues found in testing takes even more.
The Integration Cascade
Your Shopify store doesn’t exist in isolation. It connects to fulfillment, marketing, finance, and support systems. Each integration needs to be re-established on the new platform, and each one has its own setup, testing, and debugging cycle.
Worse, integrations often depend on each other. You can’t fully test the fulfillment integration until the product data is right. You can’t validate the marketing integration until orders are flowing correctly. These dependencies create cascading timelines.
Planning for Reality
The solution isn’t better estimation—it’s better planning. Build buffers into timelines. Plan for a discovery phase before committing to a launch date. Create decision-making frameworks so unexpected issues don’t stall progress.
Expect the migration to surface problems with your current operation. These aren’t failures of the migration; they’re opportunities to fix issues that would have caused problems eventually anyway. A migration that takes longer but results in a cleaner, more solid foundation is worth the extra time.