GLOSSARY

The degree to which data is accurate, complete, consistent, timely, valid, unique, and fit for its intended use.

Poor-quality data produces unreliable reporting, failed automation, weak customer experiences, regulatory exposure, and unsafe or ineffective AI decisions.

A revenue team defines required CRM fields, valid lifecycle transitions, uniqueness rules, and freshness targets, then monitors violations and assigns remediation owners.

Quality is purpose-dependent: data suitable for one decision may be inadequate for another. A dashboard score can hide critical field-level failures, and cleansing downstream does not solve defective source processes.

Quality is evaluated against explicit requirements and context. Controls include schema validation, reference checks, reconciliation, deduplication, anomaly detection, data contracts, issue workflows, ownership, and monitoring across sources and transformations.