Data Accuracy
Data accuracy is the degree to which stored information matches reality at the time it is asserted.
Allocator relevance: Directly affects diligence reliability and prevents wasted outreach or decisions based on incorrect facts.
Expanded Definition
Accuracy measures correctness, not completeness or freshness. A record can be complete but wrong, or fresh but still inaccurate if the underlying source is flawed. In allocator intelligence, accuracy is field-dependent: role/title, decision authority, mandate, and contact channels carry far higher decision weight than descriptive fields.
High-quality data systems treat accuracy as measurable and auditable, with explicit distinctions between confirmed facts, inferred attributes, and unverifiable claims.
How It Works in Practice
Accuracy is improved through source triangulation, structured verification workflows, and change detection tied to “last verified” timestamps. Mature systems log the supporting evidence trail (data lineage) and maintain source confidence so users understand reliability.
Decision Authority and Governance
Governance defines what qualifies as “verified,” who can mark fields as confirmed, and how conflicts between sources are resolved. Without governance, accuracy becomes subjective and trust erodes quickly.
Common Misconceptions
- Fresh data is always accurate.
- More sources automatically increases accuracy.
- Accuracy can be measured only at the record level (not per field).
Key Takeaways
- Accuracy is distinct from completeness and freshness.
- Treat high-stakes fields (roles, mandates, contacts) with stricter standards.
- Data lineage and source confidence are essential for trust.