Understanding Results

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:

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

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

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

Example interpretation:

• Validity Rate of 78% on Email field means 22% have format issues
• Common issues: missing @, spaces, typos like “.con”

Uniqueness Metrics

Example interpretation:

• Uniqueness Rate of 95% means 5% are duplicates
• Low Entropy suggests many records share the same values

Timeliness Metrics

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

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:

Field-Level Details

Click a dimension to see per-field breakdown.

Field Score Table

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:

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:

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

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: Adjust thresholds based on findings
• Running Scans: Schedule regular scans
• Measuring Data Quality: Build a quality scorecard