Data Normalization
Data normalization is standardizing values (names, roles, regions, sectors) into consistent formats for search, filtering, and analytics.
Allocator relevance: Normalization is what makes coverage measurable and filtering reliable—without it, your “mandate fit” filters lie.
Expanded Definition
Normalization converts messy real-world inputs into consistent categories. Examples: mapping “Chief Investment Officer” vs “CIO” vs “Head of Investments,” or standardizing geographies and sectors. In allocator datasets, normalization enables accurate segmentation (SFO vs MFO, region focus, asset class preferences) and reduces duplicates.
Normalization should preserve the raw text (for transparency) while storing standardized forms for product logic.
Decision Authority & Governance
Governance defines taxonomy dictionaries, allowed values, and update rules. Changing a taxonomy is a governance event because it affects historical filtering and analytics.
Common Misconceptions
- Normalization destroys nuance.
- You can normalize once and never revisit it.
- Taxonomies can be changed without impact.
Key Takeaways
- Normalize for product logic; preserve raw for truth.
- Taxonomy changes require governance.
- Normalization improves deduplication and coverage metrics.