Data Normalization

Normalization is the work of making data from different sources speak the same language: one schema, consistent units, a single time zone, and a shared set of identifiers. Every source names fields differently, formats dates its own way, and reports in its own units, and until that is reconciled, two datasets cannot be joined or compared without silent errors.

It is tedious, low-glamour engineering that carries enormous leverage, because everything downstream - joins, aggregations, signals - inherits whatever inconsistencies survive it. Canonicalizing to a well-designed common form is what turns a pile of heterogeneous sources into a coherent dataset. It sits alongside deduplication and entity resolution as the cleaning layer that makes raw collection usable.