Coverage

Allocator relevance: Determines whether your dataset is reliably usable for targeting, segmentation, and diligence at the specific audience level.

Expanded Definition

Coverage includes two dimensions: (1) entity coverage—whether the relevant organizations and people exist in the dataset, and (2) attribute coverage—whether the key fields (mandate, role, geography, contacts) are populated. Coverage without accuracy or freshness can mislead; coverage should be assessed alongside completeness and verification.

For Altss, coverage is most meaningful when reported by segment: allocator type, geography, AUM band, and mandate category.

How It Works in Practice

Teams define a coverage universe, measure inclusion and field population rates, and track gaps over time. Coverage improvements often come from expanding sources, improving entity resolution, and increasing refresh cadence with verification workflows.

Decision Authority and Governance

Coverage governance requires explicit definitions: what is in-scope, what counts as “covered,” and how often coverage is measured. Without a stable universe definition, coverage claims can’t be trusted.

Common Misconceptions

• More records automatically means better coverage.
• Coverage implies accuracy.
• Coverage can be measured without a defined universe.

Key Takeaways

• Coverage must be defined against a universe.
• Segment-level coverage is more actionable than overall coverage.
• Pair coverage with completeness, accuracy, and freshness.