Data-normalization field guide

A compact checklist for turning inconsistent source content into a maintainable structured dataset.

By GT · template

Maintained · Updated Aug 2026

  • Data quality
  • LLM extraction
  • Schemas

Why I use it

It separates extraction, normalization and validation so model output is treated as a candidate record rather than unquestioned truth.

Boundaries

The field definitions and confidence thresholds must be adapted to the source domain.

Before extraction

Define the entity, its stable identifier and the smallest useful schema before selecting a model or writing a prompt.

  • Keep source URLs
  • Record raw evidence
  • Name required and optional fields
  • Define acceptable unknowns

During normalization

Map vocabulary into controlled values without erasing meaningful differences or manufacturing precision.

  • Preserve raw values
  • Version mappings
  • Attach confidence
  • Separate inference from extraction

At the exception layer

Group failures by type, route ambiguous records for review and use recurring exceptions to improve the system.