Data Standardization
What is Data Standardization?
Standardization overlaps with normalization but is broader: it includes not just format (phone numbers in E.164, countries in ISO codes) but also taxonomy (titles mapped to a controlled vocabulary of roles, industries mapped to a controlled hierarchy). The work is unglamorous but consequential — a database that uses 60 variants of 'Vice President of Engineering' makes persona reporting useless. Standardization happens at ingest (incoming records are mapped to canonical form before storage), in batch (legacy data normalized to the canonical schema), and through ongoing governance (no new value categories created without a controlled-vocabulary review). It's the disciplined cousin of normalization, applied to taxonomies rather than just formats.
Why it matters
- Makes taxonomic reporting actually work — persona, industry, geography all sum correctly.
- Prevents the slow drift where every new import introduces three new value variants.
- Lets agents act on standardized values without needing to understand variant spellings.
Use cases
- Title standardization. Every job title mapped to a controlled vocabulary of ~50 roles.
- Industry standardization. Every company assigned a SIC/NAICS code rather than a free-text industry.
- Geography standardization. Every country, region, and city stored in canonical form.
How turgo helps
turgo standardizes against published taxonomies (titles, industries, geographies) at ingest — exposing the canonical values for segmentation and reporting while preserving the original raw value.
See turgo in action →
