# Understanding Results

> Learn to interpret DQS scan results, read dimension scores, drill down to affected records, and export data for cleanup.

Source: https://dataqualitysense.com/resources/using-dqs/understanding-results/
Last updated: 2026-06-03

---

## Results Overview

After a scan completes, DQS presents your results in a dashboard view. The dashboard shows scores at multiple levels:

1. **Overall Score** - Single number representing total data quality
2. **Dimension Scores** - Scores for each capability (Completeness, Validity, etc.)
3. **Field Scores** - Scores for each analyzed field
4. **Record Details** - Drill-down to specific affected records

## The Results Dashboard

### Dashboard Layout

```
┌─────────────────────────────────────────────────────────────────┐
│                     OVERALL QUALITY SCORE                        │
│                           85%                                    │
│                     ▲ +3% from last scan                         │
├──────────────────────┬──────────────────────┬───────────────────┤
│    COMPLETENESS      │      VALIDITY        │    UNIQUENESS     │
│        92%           │        78%           │       95%         │
├──────────────────────┼──────────────────────┼───────────────────┤
│    TIMELINESS        │    CONSISTENCY       │   AI READINESS    │
│        88%           │        82%           │       76%         │
└──────────────────────┴──────────────────────┴───────────────────┘
```

### Accessing Results

1. Open DQS from the App Launcher
2. Find your Definition in the list
3. Click the Definition name
4. Select the **Results** tab
5. Choose a scan date to view

The most recent scan shows by default.

## Overall Quality Score

The overall score is a weighted average of all dimension scores.

### How It's Calculated

DQS uses default weights for each dimension:

| Dimension | Default Weight |
|-----------|---------------|
| Completeness | 25% |
| Validity | 20% |
| Uniqueness | 20% |
| Timeliness | 15% |
| Consistency | 20% |

Formula: Overall = (Completeness x 0.25) + (Validity x 0.20) + (Uniqueness x 0.20) + (Timeliness x 0.15) + (Consistency x 0.20)

AI Readiness scores are shown separately and don't affect the Data Quality overall score.

### Score Interpretation

| Score Range | Quality Level | Action |
|-------------|---------------|--------|
| 90-100% | Excellent | Maintain current practices |
| 80-89% | Good | Address specific weak areas |
| 70-79% | Fair | Prioritize improvement |
| 60-69% | Poor | Immediate attention needed |
| Below 60% | Critical | Major data cleanup required |

### Trend Indicator

Next to your score, you'll see a trend arrow:

- **Green arrow up** - Score improved from last scan
- **Red arrow down** - Score declined from last scan
- **Gray dash** - Score unchanged

The percentage shows the change amount.

## Dimension Scores

Click any dimension card to see detailed metrics.

### Completeness Metrics

| Metric | Type | What It Shows |
|--------|------|---------------|
| Completeness Rate | Percentage | Fields that have values |
| Populated Count | Number | Records with data |
| Incomplete Count | Number | Records missing data |
| Null Rate | Percentage | Fields that are NULL |
| Blank Rate | Percentage | Empty or whitespace only |
| Placeholder Rate | Percentage | N/A, TBD, Unknown values |

**Example interpretation:**
- Completeness Rate: 85% means 15% of records are missing values
- High Placeholder Rate suggests users enter "TBD" instead of real data

### Validity Metrics

| Metric | Type | What It Shows |
|--------|------|---------------|
| Validity Rate | Percentage | Values matching expected format |
| Valid Count | Number | Records with correct format |
| Invalid Rate | Percentage | Values not matching format |
| Invalid Count | Number | Records with format errors |

**Example interpretation:**
- Validity Rate of 78% on Email field means 22% have format issues
- Common issues: missing @, spaces, typos like ".con"

### Uniqueness Metrics

| Metric | Type | What It Shows |
|--------|------|---------------|
| Uniqueness Rate | Percentage | Distinct vs total values |
| Distinct Count | Number | Number of unique values |
| Entropy | Decimal | Value diversity (higher = more diverse) |
| Max Frequency | Number | Most common value occurrence |
| Rarity | Percentage | How rare values are distributed |

**Example interpretation:**
- Uniqueness Rate of 95% means 5% are duplicates
- Low Entropy suggests many records share the same values

### Timeliness Metrics

| Metric | Type | What It Shows |
|--------|------|---------------|
| Freshness Rate | Percentage | Records within freshness window |
| Staleness Rate | Percentage | Records past freshness window |
| Average Age | Days | Mean age of date values |
| Recency Rate | Percentage | Records updated recently |
| Future Rate | Percentage | Records with future dates (errors) |
| Overdue Rate | Percentage | Records past expected update |

**Example interpretation:**
- Staleness Rate of 30% means 30% of records haven't been touched in your freshness window
- Future Rate above 0% indicates data entry errors

### Consistency Metrics

| Metric | Type | What It Shows |
|--------|------|---------------|
| Conformance Rate | Percentage | Values matching expected patterns |
| Conformance Count | Number | Records that conform |
| Non-Conforming Count | Number | Records with variations |
| Variant Count | Number | Different value variations found |
| Dominant Values | JSON | Top values and their counts |

**Example interpretation:**
- Variant Count of 15 on Country field suggests inconsistent entry (USA vs United States vs US)
- Dominant Values shows which variations are most common

### AI Readiness Metrics

**PII Detection:**
| Metric | What It Shows |
|--------|---------------|
| Records with PII | Absolute count of records with pattern matches (for remediation scoping) |
| PII Exposure Rate | Percentage of records containing PII (for compliance reporting) |

## Field-Level Details

Click a dimension to see per-field breakdown.

### Field Score Table

| Field | Score | Issues | Actions |
|-------|-------|--------|---------|
| Email | 92% | 234 invalid | View Records |
| Phone | 78% | 1,456 invalid | View Records |
| MailingCity | 95% | 180 missing | View Records |

### Reading Field Scores

Each field shows:
- **Score** - Performance for this field
- **Issues** - Count of problematic records
- **Actions** - Links to drill-down and export

### Identifying Problem Fields

Sort fields by score (lowest first) to find:
- Fields with most issues
- Fields needing immediate attention
- Patterns across related fields

> **Tip:** Focus on high-impact fields first. A 10% improvement in Email validity has more business value than perfecting a rarely-used field.

## Drill-Down to Records

Click **View Records** to see affected data.

### Record List View

The drill-down shows records with issues:

| Name | Email | Issue | Created Date |
|------|-------|-------|--------------|
| John Smith | `john.smith@example` | Invalid format | 2026-01-15 |
| Jane Doe | `jane.doe@mail,com` | Invalid format | 2026-01-20 |

### Filtering the Record List

Filter by:
- Issue type (missing, invalid, duplicate, etc.)
- Date range
- Owner
- Custom field values

### Direct Record Access

Click any record to open it in Salesforce. Make corrections directly or assign to the appropriate team member.

## Comparing Results Over Time

### Trend Charts

DQS displays trend charts showing:
- Overall score over time
- Dimension scores over time
- Field scores over time

Charts help you:
- Track improvement progress
- Identify declining areas
- Measure impact of cleanup efforts

### Scan Comparison

Compare any two scans:

1. Click **Compare** on the Results tab
2. Select a baseline scan (older)
3. Select a comparison scan (newer)
4. View side-by-side metrics

The comparison highlights:
- Improved metrics (green)
- Declined metrics (red)
- Unchanged metrics (gray)

### Setting Improvement Targets

Use historical data to set realistic targets:

| Current Score | Realistic 90-Day Target |
|---------------|------------------------|
| Below 60% | 70-75% |
| 60-70% | 75-82% |
| 70-80% | 82-88% |
| 80-90% | 90-94% |
| Above 90% | Maintain or 95%+ |

## Exporting Data

You can export results for offline analysis and cleanup workflows.

### CSV Export

Export options:
- **Summary Export** - Scores and metrics only
- **Affected Records Export** - Full list of records with issues
- **Field Detail Export** - Per-field breakdown

### How to Export

1. Open scan results
2. Click **Export** (download icon)
3. Choose export type
4. Select format (CSV)
5. Download the file

### Export Contents

**Affected Records Export includes:**
- Record ID
- Record Name
- Field with issue
- Issue type
- Current value
- Suggested action

**Example row:**
```
0031x00000ABC123,John Smith,Email,INVALID_FORMAT,john.smith@example,Fix email domain
```

### Using Exports for Cleanup

1. Export affected records to CSV
2. Open in Excel or Google Sheets
3. Review and correct values
4. Use Data Loader to update Salesforce
5. Re-run scan to verify improvements

> **Tip:** Create a cleanup assignment workflow. Export records, assign owners based on Account or Region, and track corrections.

## Sharing Results

### Sharing Options

Share results with stakeholders:

1. **Link sharing** - Copy URL to scan results
2. **Screenshot** - Dashboard view for presentations
3. **Export** - CSV for detailed analysis
4. **Email summary** - Automated reports

### Creating Reports for Leadership

For executive presentations, focus on:
- Overall score and trend
- Improvement from previous period
- Top 3 problem areas
- Action plan with timeline

Avoid overwhelming with metric details. Lead with the story.

## Understanding Score Changes

### Why Scores Change

| Change | Common Causes |
|--------|---------------|
| Score improved | Cleanup efforts, better data entry |
| Score declined | New data with issues, changed thresholds |
| Big jump up | Bulk data cleanup completed |
| Big drop down | Data import with quality issues |

### Investigating Changes

When scores change unexpectedly:

1. Compare scans to identify which metrics changed
2. Drill down to field level
3. Review recent data changes (imports, integrations)
4. Check if Definition configuration changed

## Next Steps

- [Definition Builder](/resources/using-dqs/definition-builder/): Adjust thresholds based on findings
- [Running Scans](/resources/using-dqs/running-scans/): Schedule regular scans
- [Measuring Data Quality](/resources/best-practices/measuring-data-quality/): Build a quality scorecard