What is Data Quality Sense?
Data Quality Sense (DQS) is a Salesforce-native data profiling and quality analysis application. It helps you measure, understand, and improve your data quality to ensure reliable business decisions and AI-ready datasets.
DQS runs entirely within Salesforce. Your data never leaves the platform.
Key Differentiators
100% Salesforce-Native
DQS is built on the Salesforce platform using standard architecture:
| Component | Technology |
|---|---|
| User Interface | Lightning Web Components (LWC) |
| Configuration | Custom Metadata Types (CMT) |
| Processing | Apex Batch Jobs |
| Storage | Custom Objects |
Why native matters:
- Your data stays in Salesforce (no export, no external APIs)
- Uses existing Salesforce security and permissions
- No integration to maintain or troubleshoot
- Familiar interface for Salesforce users
No Code Required
Configure DQS through a point-and-click wizard. No Apex code, no formulas, no technical skills required.
The Definition Builder guides you through:
- Selecting what to measure
- Choosing which fields to analyze
- Setting thresholds and rules
- Reviewing and activating
Salesforce Admins can configure and run scans without developer support.
All Features Included
DQS is a single paid product. Every capability is available to all users:
- All Data Quality dimension variants
- AI Readiness (PII Detection)
- Custom regex patterns
- CSV export
- Scheduling and recurring scans
- Mentor Panel โ contextual, in-context recommendations based on what each scan just measured
The only usage limit is the per-org scan count (configurable, default 10 scans).
What DQS Measures
DQS organizes its capabilities into two dimensions:
Data Quality Dimension
Traditional data quality checks for operational hygiene:
| Capability | What It Measures |
|---|---|
| Completeness | Are required fields populated? |
| Validity | Do values match expected formats? |
| Uniqueness | Are records distinct? |
| Timeliness | Is data current? |
| Consistency | Are values uniform? |
AI Readiness Dimension
Advanced check for Agentforce and AI preparation:
| Capability | What It Measures |
|---|---|
| PII Detection | Is sensitive data protected before AI exposure? |
How DQS Works
The DQS Workflow
- Define what to analyze using the Definition Builder
- Configure thresholds and rules for each capability
- Execute scans that process your records in batches
- Review results with metrics and drill-down to affected records
- Act on findings โ guided by the Mentor Panelโs contextual recommendations โ to improve data quality
- Monitor trends over time with recurring scans
Architecture Overview
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โ DEFINITION BUILDER โ
โ Configure what to analyze, which fields, what thresholds โ
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โ
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โ SCAN EXECUTION โ
โ Batch Apex processes records against selected capabilities โ
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โ RESULTS STORAGE โ
โ Metrics stored in custom objects for analysis and trends โ
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โ RESULTS DASHBOARD โ
โ View scores, drill down to records, export for cleanup โ
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Processing Model
DQS uses Salesforce Batch Apex for processing, which means:
- Scalable: Handles millions of records
- Governed: Respects Salesforce limits automatically
- Background: Runs without blocking users
- Resumable: Continues if interrupted
Processing cost varies by capability:
| Cost Level | Example Capabilities | Notes |
|---|---|---|
| LOW | Completeness, Validity | Simple field checks |
| MEDIUM | Timeliness, Consistency, PII Detection | Pattern analysis |
| HIGH | Uniqueness | Cross-record comparison |
Key Concepts
Definition
A Definition is your configured data quality check. It specifies:
- Which Salesforce object to analyze
- Which fields to include
- Which capabilities to run
- What thresholds and rules to apply
- What filters to narrow the scope
You can have multiple Definitions for different objects or use cases.
Capability
A capability is a specific type of data quality check (Completeness, Validity, etc.). Each capability has:
- Variants: Different implementations of the same check
- Metrics: What it measures (rates, counts, scores)
- Configuration: Thresholds and options you set
Variant
A variant is a specific implementation of a capability. For example, Completeness has:
- Global Fill Rate: Basic completeness percentage
- Contextual Logic: Completeness with conditional rules
Metric
A metric is a specific measurement produced by a scan. Examples:
| Metric | Type | Example |
|---|---|---|
| Completeness Rate | Percentage | 85% of records have Email populated |
| Invalid Count | Integer | 234 records have invalid email format |
| Dominant Values | JSON | Top 5 most common Industry values |
Getting Started
Step 1: Install from AppExchange
- Go to Salesforce AppExchange
- Search for โData Quality Senseโ
- Click โGet It Nowโ and follow the installation wizard
- Assign the DQS permission set to users
Step 2: Create Your First Definition
- Open DQS from the App Launcher
- Click โNew Definitionโ
- Follow the wizard to configure your first scan
For detailed guidance, see the Definition Builder Guide.
Step 3: Run and Review
- Save your Definition
- Click โRun Scanโ
- Wait for processing to complete
- Review your results
See Understanding Results for interpretation guidance.
Next Steps
- Definition Builder Guide: Step-by-step configuration
- Running Scans: Execution and monitoring
- Understanding Results: Interpreting your data