Data Migration & Data Management
Migrations fail on reconciliation, not on loading. Moving objects into Teamcenter is mechanical; proving that what arrived matches what left, and that relationships and revisions survived intact, is the work that determines whether anyone trusts the result.
Source analysis and mapping
Before anything moves, we profile the source: how many objects, which types, what is actually populated versus nominally present, and where the data contradicts itself. The mapping document that comes out of this is the thing the whole migration is judged against.
Custom tools for cleanup and mapping
Every migration needs tooling that did not exist before it started: mapping tables encoding hundreds of source-to-target decisions, cleanup routines for whatever the source analysis turned up, de-duplication, and normalisation of free-text fields that were never validated at entry. We build those tools for the migration in front of us rather than pushing the data through something generic — Excel toolbars where the people doing the mapping live in spreadsheets, transformation scripts and database writer services where the volume makes that impractical. A narrow tool written for one job is quicker to build, and far quicker to trust, than a configurable product nobody has used before.
Transformation and loading
Bulk migration using TCXML and PLMXML, with transformation handled in repeatable scripts rather than manual edits. Repeatability matters because no migration runs once — it runs in cycles, each one correcting what the last revealed.
Validation and reconciliation
Automated comparison between source and target: object counts by type, relationship integrity, revision chains, attribute-level differences. The output is a report you can show to the people who have to sign off, not an assurance that it went fine.
Legacy and cross-system data
Extracting from legacy PDM systems, file shares and enterprise databases where the structure was never designed for export. This is usually where the schedule risk sits, and where it is worth spending time early.
What you get
- Source data profile and mapping specification
- Custom cleanup, mapping and de-duplication tools built for your data
- Repeatable transformation and load scripts
- Reconciliation reports comparing source and target
- Dry-run and cutover plans with rollback steps
- Post-migration validation summary for sign-off
Common questions
- How long does a Teamcenter migration take?
- It depends far more on data quality than on volume. A clean source of millions of objects can move faster than a small, inconsistent one. The source analysis gives a realistic estimate.
- Our source data is messy. Is that a problem?
- It is the normal case, and quantifying it is exactly what the source analysis is for. Cleanup can happen before the migration, inside the transformation, or both — which is cheaper depends largely on whether the source system stays in use afterwards. Either way we build the cleanup tooling as part of the work rather than handing it back as something for you to sort out first.
- Can you migrate from a non-Siemens PDM system?
- Yes. The transformation layer changes but the approach does not: profile the source, map it, load it repeatably, reconcile it.
- What happens to revision history?
- Revision chains and relationships are part of what reconciliation checks explicitly. How much history to carry over is a scoping decision worth making deliberately, since it affects both effort and target system size.
Need help with Data Migration & Management?
Tell us about your Teamcenter environment and we will come back to you with how we would approach it.