Knowledge Base
Data Quality
Learn about data quality, explore best practices, and discover how to improve your Salesforce data.
What is Data Quality?
Learn what data quality means, how to measure it, and why it determines the success of your reporting, automation, and AI initiatives.
Read article →What is a Data Quality Score?
A data quality score turns the health of your data into a single number. Learn how it is calculated, what counts as a good score, and how to track it over time.
Read article →The Five Dimensions of Data Quality
Learn the five dimensions DQS measures: Completeness, Validity, Uniqueness, Timeliness, and Consistency.
Read article →Data Quality Examples
Real CRM examples of good vs bad data across completeness, validity, uniqueness, timeliness, consistency, and AI readiness in Salesforce.
Read article →Completeness
All 10 completeness metrics DQS measures, the diagnostic funnel for finding missing data, and how to configure completeness analysis.
Read article →Validity
All 6 validity metrics DQS measures, the diagnostic flow for finding format errors and noise, and how to configure pattern-based validation.
Read article →Uniqueness
All 6 uniqueness metrics DQS measures, the diagnostic flow for finding duplicates and repetitive content, and how to configure uniqueness analysis.
Read article →Timeliness
All 6 timeliness metrics DQS measures, the diagnostic flow for finding stale and anomalous dates, and how to configure freshness analysis.
Read article →Consistency
All 6 consistency metrics DQS measures, the diagnostic flow for finding value fragmentation, and how to configure conformance analysis.
Read article →