Knowledge Base
Resources & Documentation
Learn about data quality, explore best practices, and discover how to improve your Salesforce data.
All Articles
Getting Started
Introduction to Data Quality
Get started with data quality fundamentals and learn how DQS helps you measure and improve your Salesforce data.
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Getting Started
Why Data Quality Matters
Understand the business impact of poor data quality and why organizations are investing in data quality now.
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Getting Started
Quick Start Guide
Get from zero to your first data quality insights in 10 minutes. Step-by-step guide to getting started.
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Data Quality
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.
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Data Quality
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.
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Data Quality
The Five Dimensions of Data Quality
Learn the five dimensions DQS measures: Completeness, Validity, Uniqueness, Timeliness, and Consistency.
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Data Quality
Data Quality Examples
Real CRM examples of good vs bad data across completeness, validity, uniqueness, timeliness, consistency, and AI readiness in Salesforce.
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Data Quality
Completeness
All 10 completeness metrics DQS measures, the diagnostic funnel for finding missing data, and how to configure completeness analysis.
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Data Quality
Validity
All 6 validity metrics DQS measures, the diagnostic flow for finding format errors and noise, and how to configure pattern-based validation.
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Data Quality
Uniqueness
All 6 uniqueness metrics DQS measures, the diagnostic flow for finding duplicates and repetitive content, and how to configure uniqueness analysis.
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Data Quality
Timeliness
All 6 timeliness metrics DQS measures, the diagnostic flow for finding stale and anomalous dates, and how to configure freshness analysis.
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Data Quality
Consistency
All 6 consistency metrics DQS measures, the diagnostic flow for finding value fragmentation, and how to configure conformance analysis.
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AI Readiness
PII Detection
The 8 PII detection patterns DQS uses, three presets for common scanning scenarios, and how to configure pattern-based detection.
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AI Readiness
Agentforce Data Quality: Preparing Salesforce Data for AI
Improve Agentforce data quality and data readiness. A practical guide to preparing your Salesforce data for AI agents with DQS — completeness, consistency, and PII detection.
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AI Readiness
Agentforce Data Readiness Checklist
A practical Agentforce data readiness checklist. Assess whether your Salesforce data is ready for AI agents across completeness, consistency, PII, and more.
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AI Readiness
Why Agentforce Agents Give Wrong Answers: Data Quality Root Causes
Why Agentforce gives wrong answers: 6 data quality root causes behind inaccurate responses and hallucinations, the DQS metric that diagnoses each, and the fix.
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AI Readiness
Agentforce and PII: Protecting Sensitive Data Before AI Deployment
Agentforce reads PII hidden in Salesforce text fields. Learn how to find and remediate sensitive data before deployment to reduce compliance risk.
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AI Readiness
Salesforce Data Cleanup for Agentforce: Object-by-Object Guide
A field-level Salesforce data cleanup playbook for Agentforce. Which objects and fields to clean first for service and sales agents, mapped to DQS.
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AI Readiness
Agentforce Data Quality: Frequently Asked Questions
Answers to common Agentforce data quality questions: does Agentforce need clean data, what data it reads, readiness thresholds, PII risk, and how to prepare.
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Best Practices
Building a Data Governance Framework
Establish governance roles, policies, and standards to manage data quality across your organization.
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Best Practices
Measuring Data Quality
Define KPIs, build scorecards, and benchmark your data quality to drive continuous improvement.
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Best Practices
Data Quality KPIs and Metrics
A catalog of 18 data quality KPIs with formulas, example targets, worked calculations, and a weighted scorecard template you can copy.
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Best Practices
Building a Data Quality Culture
Drive adoption and sustainability through change management, training, and organizational alignment.
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Best Practices
10 Common Data Quality Pitfalls
Avoid the mistakes that derail data quality initiatives and learn recovery strategies.
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Using DQS
DQS Overview
Learn what Data Quality Sense is, how it works, and why Salesforce-native architecture matters for your data.
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Using DQS
Definition Builder Guide
Step-by-step guide to creating DQS Definitions using the 5-step wizard. Configure objects, fields, thresholds, and dimension weights.
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Using DQS
Running Scans
Learn how to execute DQS scans, monitor progress, handle large datasets, and schedule recurring scans.
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Using DQS
Understanding Results
Learn to interpret DQS scan results, read dimension scores, drill down to affected records, and export data for cleanup.
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Salesforce
Data Quality in Salesforce
What data quality means inside Salesforce, why CRM data degrades, the six dimensions that matter, and how to measure and improve it natively.
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Use Cases
Completeness: Configuration Scenarios
Three practical walkthroughs showing how to configure DQS completeness analysis for different business needs.
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Salesforce
How to Measure Data Quality in Salesforce
How a Data Quality Score (data reliability score) works in Salesforce: weighted dimensions, field-level breakdowns, and tracking quality over time.
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Use Cases
Consistency: Configuration Scenarios
Three practical walkthroughs showing how to configure DQS consistency analysis for different business needs.
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Salesforce
How to Improve Data Quality in Salesforce
A practical, repeatable workflow to improve and maintain data quality in Salesforce: detect, prioritize, fix, prevent, and monitor.
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Use Cases
Validity: Configuration Scenarios
Three practical walkthroughs showing how to configure DQS validity analysis for different business needs.
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Salesforce
Salesforce Data Quality Dashboard: Metrics That Matter
What a Salesforce data quality dashboard should track: the Data Quality Score, dimension breakdowns, field health, trends, and PII exposure.
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Use Cases
Timeliness: Configuration Scenarios
Three practical walkthroughs showing how to configure DQS timeliness analysis for different business needs.
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Salesforce
Salesforce Data Quality Tools
Salesforce data quality tools fall into three layers: native prevention, measurement and monitoring, and specialized third-party. Match the tool to the job.
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Use Cases
Uniqueness: Configuration Scenarios
Three practical walkthroughs showing how to configure DQS uniqueness analysis for different business needs.
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Salesforce
Who Owns Data Quality in Salesforce?
Nobody owns Salesforce data quality by default. A practical ownership model: one accountable owner, named stewards per object, and producers held to entry standards.
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Use Cases
PII Detection: Configuration Scenarios
Three practical walkthroughs showing how to configure DQS PII detection for different scanning needs.
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The Real Cost of Bad Salesforce Data
Bad CRM data has a price: rep hours lost to record archaeology, wasted marketing spend, missed forecasts, and AI that answers from fiction. Here is the math.
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Salesforce Data Cleansing: A Complete Guide
How to clean Salesforce data end to end: audit first, then deduplicate, standardize, complete, refresh, and secure — and the prevention that keeps it clean.
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