How it works
The same pipeline serves a one-time migration and an always-on integration. Here is each stage in detail.
Discover
Inventory every system, owner, record class, format and volume. Agree what must be preserved exactly, what can be summarized and what falls outside scope. Output: a documented data landscape.
Connect
Set up connectors by file, API, database, xAPI, SFTP or cloud storage. Credentials are scoped and encrypted. A first extraction runs into staging. Connectors →
Normalize
Map source fields to the common model. Keep raw values alongside clean ones. Record every transformation as lineage. Normalization and lineage →
Reconcile
Match identities across systems with deterministic and probabilistic rules, route uncertain matches to review, and log every decision. Identity reconciliation →
Validate
Run quality checks, review the evidence with your team, and gate promotion into the governed layer on thresholds you set. Data quality →
Govern
Assign owners, permissions, privacy classification and retention to every dataset. Log every access. Governance →
Use
Dashboards, BI exports, audit pulls, integrations, controlled data movement, skills analysis and AI context, all from the same store. Outputs →
Ready to see your learning data in one place?
Tell us which systems you run and we will show how the platform connects, normalizes and preserves their records.