Data Enrichment
Data enrichment is adding missing fields or context to a record using additional sources and verification workflows.
Allocator relevance: Enrichment turns a directory into an intelligence system—especially for decision chains, mandate signals, and contacts.
Expanded Definition
Enrichment targets high-value gaps: decision-maker roles, routing context, investment preferences, and verified contact channels. The point is not adding “more data,” but improving actionability. Enrichment must be evidence-backed; otherwise it increases noise.
Good enrichment is iterative: start with a base record, then add evidence-weighted fields over refresh cycles, improving confidence over time.
Decision Authority & Governance
Governance defines which fields can be enriched via inference vs only via direct evidence, and what thresholds are required before surfacing enriched fields to users.
Common Misconceptions
- Enrichment means scraping everything possible.
- More fields automatically means better product.
- Inferred data is equivalent to verified data.
Key Takeaways
- Enrichment should prioritize actionability fields.
- Separate inferred vs verified enrichments.
- Enrichment quality depends on governance and evidence.