Data Migration
PLM Data Migration: From Planning to Go-Live
The nine phases of a PLM data migration, the success criteria to set before you start, and the alternatives worth considering instead of migrating everything.
Dibyak BiswasPLM & 3DEXPERIENCE Presales Head
6 min read
PLM data migration is the process of moving product data (parts, BOMs, CAD files, documents, and change history, together with the relationships between them) from a legacy system into a new Product Lifecycle Management (PLM) system. A successful PLM data migration follows nine phases: planning, data assessment, script development, dry runs, user acceptance testing (UAT), cutover planning, Go-Live execution, post-migration activities, and post-migration support.
Migrating data to a new PLM system is a complex but crucial task, and enterprise system migrations rarely go to plan. In a 2025 Horváth study of 200 companies reported by CIO, only 8% finished their ERP migration on schedule, projects ran 30% longer than planned on average, and more than 60% went over budget. Underestimated testing and data migration phases were among the main causes.
PLM data adds its own complexity. The Infosys white paper on data migration in the PLM ecosystem notes that the volume and complexity of legacy data, especially CAD, is often uncertain, and that data scoping is frequently overlooked. Its scoping scenarios show why scope matters: migrating only work-in-progress and the latest released revisions covers about 35% of the data, against roughly 95% for a near-full migration.
With a structured method, the picture changes: SteepGraph moved 14.76 million PLM records from Windchill into 3DEXPERIENCE in a single five-to-six-hour load window.
Whether you are upgrading from a legacy system or implementing PLM for the first time, the success of your data migration will have a lasting impact on your business operations. In this guide, the PLM specialists at SteepGraph walk you through each phase from planning to Go-Live, the objectives and success criteria to set, and the alternatives to a full migration.
What are the objectives of a PLM data migration?
Clear objectives are crucial to a data migration's success, and they should align with the needs of each department in the organization.
| Area | Data migration objectives |
|---|---|
| Business | Enhance operational efficiency, support business growth, improve data accessibility, and enhance customer experience. |
| Compliance | Ensure data integrity and security, meet regulatory compliance, maintain an audit trail, and align with data retention and deletion policies. |
| IT | Ensure system compatibility, minimize downtime, maintain data quality, and plan for scalability and performance. |
| Finance | Manage cost efficiency, ensure return on investment (ROI), mitigate financial risks, and allocate budget appropriately. |
How do you measure PLM data migration success?
To ensure your data migration project is successful, it’s important to establish clear success criteria:
- Data Accuracy and Integrity: Ensure 100% accurate data transfer with no loss or corruption.
- Minimal Downtime: Plan for minimal disruption to operations.
- Regulatory Compliance: Meet all relevant regulatory requirements and maintain an audit trail.
- User Acceptance and Satisfaction: Achieve successful UAT and ensure high user adoption rates.
- System Performance and Stability: Maintain or exceed performance benchmarks post-migration.
- On-Time and Within Budget Completion: Complete the project on schedule and within budget.
- Effective Data Validation and Testing: Ensure comprehensive testing and validation.
- Post-Migration Support and Monitoring: Provide robust support and continuous monitoring post-migration.
What are the 9 phases of a PLM data migration?
1. Planning: laying the foundation for success
Planning sets the stage for a successful PLM data migration by defining what moves, who is involved, when, and what could go wrong.
- Define the project scope
- Identify stakeholders
- Set timelines
- Assess risks
2. Data assessment: understanding your data
Before you migrate data, assess its quality, relevance and security so the transition is smooth.
- Data quality assessment
- Data mapping from source to target PLM
- Data security considerations
3. Data migration script development: preparing for migration
Develop the scripts that extract, transform and load (ETL) data into the new PLM system, with initial validation to confirm the migration will work.
- Data extraction
- Data transformation
- Data loading
- Data validation
4. Data migration dry run cycles: testing the process
Dry run cycles test the full migration process before the actual Go-Live.
- Execute dry runs
- Identify and resolve issues
- Run a final dry run
5. User acceptance testing (UAT): ensuring user satisfaction
In UAT, end users validate that the migrated data meets their requirements and expectations.
- Data verification
- Feedback collection
- Final adjustments
6. Cutover planning: preparing for Go-Live
Cutover planning details the Go-Live window: timing, resources and contingencies.
- Schedule Go-Live
- Allocate resources
- Plan contingencies
7. Migration execution on Go-Live: the moment of truth
With everything in place, execute the migration during the planned Go-Live window.
- Execute the migration
- Resolve issues
- Monitor Go-Live
8. Post-migration activities: ensuring a smooth transition
After migration, confirm the new system runs smoothly and users are comfortable with the change.
- Post-migration validation
- End-user training
- Support and monitoring
9. Post-migration support: continuous improvement
Post-migration support addresses issues found after Go-Live promptly and helps users adapt to the new system.
- Support desk
- Ongoing monitoring
- Continuous improvement
What are the alternatives to a full PLM data migration?
A full data migration is not always the best approach. Consider these seven alternatives:
| Approach | How it works | Best when |
|---|---|---|
| Phased migration | Migrate data in stages | You want lower risk and continuous testing |
| Parallel run | Run old and new systems side by side | The new system needs full validation first |
| Data archiving | Migrate only critical data, archive the rest | Much legacy data is rarely used |
| Data integration | Connect old and new systems with integration tools | You need access without moving all the data |
| Data federation | Combine data from several sources virtually | Data lives in many systems |
| Selective data migration | Migrate only high-value data sets | Value is concentrated in a few data sets |
| Legacy system extension | Keep the legacy system for some functions | Specific functions or new data move first |
Conclusion
Successfully migrating data to a new PLM system requires careful planning, execution, and monitoring. Set objectives across business, compliance, IT, and finance, agree on success criteria early, test through dry runs and UAT, and choose a full or alternative migration approach based on your data and risk.
Plan your PLM data migration with SteepGraph. Moving records is the easy part; keeping parts, BOMs, CAD, documents and change history connected is the real work. SteepGraph's PLM data migration services have delivered 30+ migration programmes and 2B+ records, with structures and relationships kept intact and every load reconciled against the source. We migrate into and between 3DEXPERIENCE, Aras Innovator, Teamcenter and Windchill, from sources including Oracle Agile PLM, ENOVIA, SmarTeam and SOLIDWORKS PDM, using full, selective or phased approaches. Repeated migration cycles run until everyone reconciles, so issues surface in rehearsal, not at Go-Live.
Running Oracle Agile PLM? Premier Support ends in December 2027, so now is the time to plan your move. Talk to a migration expert to define your scope, approach, and validation strategy.
FAQs
PLM data migration moves product data such as parts, BOMs, documents, and change history from a legacy system into a new Product Lifecycle Management system. It covers extracting, transforming, loading, and validating that data.
The nine phases are planning, data assessment, migration script development, dry run cycles, user acceptance testing, cutover planning, Go-Live execution, post-migration activities and post-migration support.
Dry runs test the full migration process before Go-Live, so issues are found and fixed early. A final dry run confirms the migration is ready.
Cutover planning is the detailed plan for the Go-Live window: its timing, the resources needed, and contingencies if something goes wrong.
Measure it on data accuracy and integrity, minimal downtime, regulatory compliance, user acceptance, system performance, on-time and on-budget delivery, thorough validation, and post-migration support.
Duration depends on data volume, data quality, the number of source systems, and how many dry-run cycles are needed. Define the timeline in the planning phase and refine it after the data assessment.
Filed under
- PLM Strategy
- Implementation
About the author
Dibyak Biswas
PLM & 3DEXPERIENCE Presales Head, SteepGraph
