Tool migrations are where product history goes to die. You switch from Jira to Linear. Somewhere in the transition, three years of product decisions become unsearchable. Nobody planned for it. Nobody noticed until someone asked why.
Last year I helped a team migrate from ProductPlan to Productboard. They had 400 feature cards, 12,000 customer insights, and two years of prioritization history. The migration took three months. They thought it would take two weeks. The hard part was not the export. The hard part was agreeing, upfront, on what had to survive the move.
Here is the thesis: tool migrations succeed or fail based on data planning, not tool features. The new system might be better, but if you lose institutional knowledge in the transition, you pay for that loss forever. (Is this about tool features? No, it is about data planning.) (Better tool, but what about institutional knowledge? It is the part you cannot afford to lose.)
Why Migrations Are Harder Than They Appear
Every PM and design tool has its own data model. A "feature" in Productboard is not the same as a "feature" in Aha!. A "component" in one design system does not map cleanly to another. That difference is not just semantics. It becomes the shape of what you can search, filter, report, and trust after the move.
This is what I mean by schema mismatch. The basic gist is this: tools organize information differently, and translating between schemas loses nuance that seemed unimportant until it was gone. You usually notice the missing nuance later, when you are trying to understand why something was prioritized, when a dependency was introduced, or when a decision changed. The record is still there, but the context is thinner. (Does nuance really matter? Yes, you notice when it is gone.)
In practice, those boxes hide a lot of decisions. "Data Export" might mean multiple exports, partial exports, and exports taken at different times. "Schema Translation" often becomes a running document that keeps changing as you learn what matters. "Validation" is rarely a single pass. It is a loop, because you only learn what to validate when you see what broke. Even the cleanest migrations involve tradeoffs, and tradeoffs are easier to live with when they are chosen, not discovered.
Planning the Migration
Start with a data inventory. What data exists in the current system? Features, tickets, comments, attachments, integrations, user permissions, custom fields. Write it down in plain terms. Include the obvious things and the annoying little things. The annoying little things are often what people miss, and missing them is what makes the new system feel incomplete.
Map to the target system. For each data type, where does it live in the new system? What is the equivalent field? What has no equivalent? Do not assume the default fields will cover you. Many teams rely on custom fields, conventions, and naming patterns that are not formalized anywhere. If the migration is the first time you are forced to formalize them, that is normal.
Identify what cannot migrate. Some data will not transfer cleanly. Historical comments might become flat text. Custom fields might require manual recreation. Know this upfront. Decide what "good enough" looks like before you hit the import button, not after. (What does good enough look like? Decide before import.)
Plan for history. Do you need to preserve historical states (what the roadmap looked like six months ago)? Or is current state sufficient? History preservation dramatically increases complexity. History also changes the testing burden, because you are validating not just records, but sequences. If you need history, be explicit about which history matters and why.
Migration Approaches
Big bang: Export everything, import everything, switch over in one day. Fast but risky. If something goes wrong, you have to fix it under pressure. This approach can work when the data model is simple, the team is small, and the cost of being wrong is limited. But it also concentrates risk into a single cutover moment.
Parallel operation: Run both systems simultaneously during transition. Import data progressively. Validate before cutting over. Safer but more expensive (time and subscription costs). Parallel operation is less about duplicating work forever and more about buying time to validate. You are paying for overlap so you can catch mismatches early, while the original source is still active. (Why is it more expensive? Time and subscription costs.)
Fresh start: Accept that historical data will not migrate cleanly. Export reference copies to archive storage. Start fresh in the new system. Fresh start is not the same as forgetting. It is choosing to treat the old system as a reference archive rather than an active workspace. (Start fresh, but can you still look back? Yes, via archive storage.)
The right approach depends on how critical historical data is to your workflow. It also depends on how often people need to answer "what changed" versus "what is current." If your workflow is built around learning from the past, you will feel history loss immediately.
Migrating PM Tool Data
Features and requirements: Export to CSV or via API. Map fields. Import. Verify counts match. Then verify representative records match, not just totals. Counts are a baseline. They do not prove that titles, descriptions, owners, and states landed in the right place.
Customer feedback: Often the hardest to migrate. Feedback links to features, has sentiment tags, has source information. Document what will translate and what will be flattened. Decide whether flattening is acceptable, and for which types of feedback. If the feedback is used primarily for discovery, search quality matters more than perfect structure. If it is used for reporting, structure matters more.
Roadmaps and timelines: Visual roadmaps may not export meaningfully. Screenshots might be the only viable archive. If screenshots are the archive, label them clearly, keep them organized, and make sure people know where they live. A hidden folder is not an archive in practice.
Prioritization scores: If you used custom scoring (RICE, value/effort), recreate the framework in the new system before importing. This is less about math and more about interpretation. If the new system labels or calculates differently, you can accidentally change how teams read the same score.
User permissions and workflows: These often cannot migrate. Plan to recreate manually. Permissions are not just settings. They are a promise about who can change what, and they shape how confident people feel using the tool. (Can user permissions and workflows migrate? These often cannot migrate, so plan to recreate manually.)
Migrating Design Tool Data
Figma file migration: Generally straightforward within Figma versions. Moving from Sketch to Figma or vice versa requires design file conversion that loses some fidelity. Even when conversion "works," you still have to inspect what matters: layout consistency, text styles, symbols or components, and anything that affects handoff.
Design system migration: Components, styles, and tokens need careful mapping. Inconsistent naming between systems causes import failures. Naming also affects discoverability. If teams cannot find the right component quickly, they will duplicate it, and duplication is how design systems quietly break. (Is naming really the problem? Inconsistent naming between systems causes import failures.)
Prototype interactions: Complex prototypes may not survive migration. Plan to recreate high-fidelity interactions. Prioritize the interactions that teams rely on to make decisions, not every interaction that exists. Some prototypes are essentially documentation. Some are active collaboration artifacts.
Comments and history: Version history often does not migrate. Export key comments before switching. Export does not have to mean perfect export. It can mean capturing decisions and rationale in a durable place. The point is to keep the why, not just the what.
AI design tools like Figr can help rebuild design assets during migration. If recreating components is necessary, Figr can generate them following your design system, speeding reconstruction.
Testing the Migration
Never migrate directly to production. Create a test instance of the target system. Run the migration. Validate. Treat the test instance like a rehearsal, not a formality. A rehearsal surfaces the missing steps, the missing permissions, and the missing fields.
Check record counts. Did all features transfer? All tickets? All comments? Then check the edge cases that people complain about in real life, because edge cases are where migrations hurt. That might be oversized attachments, unusual custom fields, or records that were created by integrations.
Check data fidelity. Open random records. Is the information complete? Are links preserved? If links are not preserved, note which types of links break, and whether you can repair them with mapping. Links are often the connective tissue between product decisions.
Check usability. Can team members find what they need? Is search working? Search is not a nice-to-have. It is how institutional knowledge stays alive inside the tool. (Is search part of testing? Yes, check usability and search working.)
Fix issues and remigrate until validation passes. Remigrating is normal. The first run is rarely the last run.
Common Migration Failures
The first failure is inadequate inventory. You discover critical data existed in the old system after you have already shut it down. This failure feels like a small oversight at first, then becomes a long-term trust problem.
The second failure is underestimating effort. Migrations always take longer than expected. Build buffer. The buffer is not pessimism. It is realism about mapping, cleaning, testing, and iteration.
The third failure is losing relationships. Individual records migrate, but links between records break. Relationships are not just convenience. They are how teams trace a line from feedback to feature to decision to release.
The fourth failure is no rollback plan. If the migration fails catastrophically, can you revert? Know before you start. A rollback plan does not have to be elegant. It has to be possible.
The fifth failure is user abandonment. People stop using the old system before the new system is ready. Work happens in neither place. User abandonment is rarely malicious. It is usually a sign the transition was not paced and supported.
Post-Migration Cleanup
After migration, expect cleanup work. Broken links need repair. Missing data needs manual addition. Custom configurations need recreation. Cleanup is where you restore confidence. It is also where you learn what your inventory missed.
Plan a stabilization period. For the first month, expect questions and issues. Staff accordingly. Stabilization is not a separate project. It is part of the migration cost.
Archive the old system before deletion. Even if you never access it, keeping an export provides insurance. If the export is large, keep it indexed or at least clearly labeled. An export that nobody can find is not insurance.
Gather feedback. What did users expect that did not work? Use this to improve future migrations. Feedback is also how you identify training gaps, because sometimes "did not work" means "works differently."
In short, migration is not complete at cutover. It is complete when the new system is fully productive.
The Takeaway
Data migration between PM and design tools requires careful planning: inventory current data, map to target schemas, choose a migration approach, test thoroughly, and plan for cleanup. Do not underestimate complexity or timeline. The cost of lost institutional knowledge exceeds the cost of careful migration.