How to Score Contact Data Quality Without Overclaiming
Design an explainable contact-quality review that separates missing data, conflicts and message approval.
A data-quality score is useful only when its components and limits are visible. Define what the number measures: completeness, recency, source reliability or consistency. Do not call it an identity score if it only checks whether required fields are filled.
A practical way to apply it
A transparent rubric could assign positive evidence to a recently confirmed business domain and subtract confidence when company names conflict. Mark a field unknown when no source exists; do not invent a neutral value. Show the reasons beside any score and let a reviewer correct stale data.
Before using the pattern
If a score is useful, show its components rather than only a number: source reliability, recency, completeness and contradictions. Define what missing evidence means before applying weights. A complete record is not automatically a likely buyer, and a high score must never override an invalid address or an unsubscribe.
A worked example
Keep contact confidence separate from message quality. A well-documented contact can still receive a misleading draft, while a sound message may rely on incomplete contact details. Separate outcomes might be “ready for human review,” “needs data check” and “blocked by unsubscribe,” instead of one green light.
Common mistakes to avoid
Never let a high score override an unsubscribe, invalid address or purpose restriction. Document the rubric, test it against edge cases and review outcomes for systematic bias. Cadensy’s planned scoring model is not currently available or a guarantee of identity or delivery.