Glossary

What is Canonical data model?

A canonical data model is one consistent schema that all source data is normalized into, so every network looks the same to consumers.

Also known as: normalized data model, canonical schema, data normalization

A canonical data model is a single, authoritative schema that heterogeneous source data is mapped into. Instead of every charging network exposing its own field names, identifiers and status codes, all of them are normalized to one shape — so a consumer writes their integration once and it works across every source.

The alternative — consuming each network’s raw feed directly — means handling dozens of incompatible formats, reconciling different identifier schemes, and re-doing the work every time a new network is added. A canonical model absorbs that complexity in one place.

A strong canonical model is more than field-renaming: it deduplicates entities across sources, preserves provenance (where each value came from and when), and stays the source of truth even when projected to interchange formats like OCPI. That combination is what makes downstream data trustworthy.

In the Data for EV model

The Data for EV canonical.v2 model is exactly this: a four-level Station → EVSE → Connector → Tariff hierarchy that stays the source of truth, with every value source-traced and historized, and projected to OCPI 2.3.0 for interoperability.

See Canonical data model in real data

Data for EV normalizes this across 65 networks into one canonical, OCPI 2.3.0-compatible model.