Data Normalization
What is Data Normalization?
Normalization is the boring, essential work of making 'United States', 'USA', 'U.S.', and 'us' all the same value. Without it, the same logical record appears as ten distinct rows depending on how the field happens to be entered. Common normalization targets: phone numbers (canonical E.164 format), country names (ISO codes), job titles (mapped to a controlled vocabulary), company names (stripped of suffixes like 'Inc' and 'LLC'), email casing (lowercase). Normalization happens at three points: at entry (form fields validate against canonical formats), in batch (legacy data cleaned up to canonical form), and at query time (search abstracts over the underlying variation). Doing it at all three is the gold standard.
Why it matters
- Makes records actually comparable — without it, dedup, matching, and reporting all fail.
- Simplifies downstream queries — search 'USA' and get 'United States' too.
- Reveals real volumes — 'VP Engineering' and 'VP of Engineering' counted separately under-state the persona size.
Use cases
- Phone normalization. Every phone reformatted to E.164 (+15551234567) before storage.
- Title normalization. Raw titles mapped to a controlled vocabulary for persona analysis.
- Country normalization. Every country field stored as ISO-3166 alpha-2 code.
How turgo helps
turgo normalizes every field to a canonical format at ingest — and exposes the canonical schema so customers can rely on consistent values for downstream segmentation and reporting.
See turgo in action →
