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Normalization and lineage

One data model, with every original value kept and every transformation traced.

The common learning data model

Every source field is mapped to a shared model covering learners, courses, versions, enrollments, attempts, completions, scores, certifications, requirements and content. The model is documented, versioned and extensible, so a field that only exists in one system still has a home rather than being dropped.

Raw values are never discarded

Normalization creates a clean, comparable value beside the original one. A completion date recorded as text in one system and a timestamp in another becomes one comparable field, while the raw strings remain queryable. Nothing is silently rewritten.

What lineage records

  • Source system, connector and extraction run
  • Original record identifier and original field values
  • Every mapping and transformation applied, with the mapping version
  • Identity match decisions that touched the record
  • Who approved promotion into the governed layer and when

Lineage is attached to the record itself, so an output can always be traced back to its origin without a separate investigation.

Why this matters

Auditors ask where a number came from. Migration teams need to prove nothing changed in transit. BI teams need to know which source a field reflects. Lineage answers all three from the same place.

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.