Data accuracy

Definition

Data accuracy measures how well your records match reality: is the email address deliverable, is the headcount current, does the phone number connect to the person listed. Accuracy is expressed as a percentage — 'we match 92% of email addresses to the right individual at the right company' — and varies by field type (email and phone decay faster than company name). Accuracy is not the same as coverage (how many records have a value for that field). A 95%-accurate dataset with 70% email coverage is usually better than a 99%-accurate dataset with 10% coverage.

Why it matters

Inaccurate data costs more than missing data. A wrong phone number wastes a dial; a wrong email address hurts sender reputation; a wrong seniority tier routes a VP-level prospect to a junior SDR. Accuracy is the single metric that determines whether your enrichment spend generates pipeline or noise.

Example

An enrichment vendor claims 90% data accuracy on direct dials. A RevOps team runs a 100-row audit: 87 dials reach the right person, 8 reach the wrong person, and 5 are disconnected numbers. The real accuracy is 87%, not 90%, and the team recalculates ROI for their enrichment spend against the lower figure.

How Impossible Data helps

Impossible Data verifies attendee records against observed event participation and public sources — so you get conference data built on accuracy guarantees, not inference scores.