# 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.

Source: https://dataqualitysense.com/resources/salesforce/salesforce-data-quality-dashboard/
Last updated: 2026-06-03

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A data quality dashboard turns dozens of scattered checks into a single view you can monitor at a glance. In Salesforce, the right dashboard tells you — in seconds — how trustworthy your data is, where the problems concentrate, and whether things are getting better or worse. This guide covers the metrics that matter and how to read them.

## What a Data Quality Dashboard Is For

A dashboard answers three questions on a recurring basis:

- **Can I trust this data today?** A single headline number for an at-a-glance read.
- **Where are the problems?** A breakdown that turns the headline into specific, ownable tasks.
- **Are we improving?** A trend that shows whether your fixes are working and catches new issues early.

If a dashboard cannot answer all three, it is a report, not a monitoring tool.

## The Metrics That Matter

A useful Salesforce data quality dashboard tracks a small set of complementary metrics rather than a wall of numbers:

| Metric | What it tells you | Why it matters |
|---|---|---|
| **Data Quality Score** | A single weighted 0–100 figure across all dimensions | The headline. One number leaders can track over time. |
| **Dimension breakdown** | Score per dimension (completeness, validity, uniqueness, consistency, timeliness) | Shows *what kind* of problem dominates |
| **Field health** | Pass/fail rate per field | Shows *where exactly* the problem lives — the actionable layer |
| **Trend over time** | The score across successive scans | Shows whether you are improving and surfaces new issues fast |
| **PII exposure** | Records and fields containing sensitive data | Critical before any Agentforce or AI project |
| **Worst offenders** | The objects and fields driving the most failures | Tells you where to start |

Together these move you from "how healthy is the data?" down to "which field, on which object, do we fix first?" in three clicks.

## How to Read the Dashboard

Read it top-down, from headline to action:

1. **Headline.** Glance at the Data Quality Score. Up from last scan? Down? Flat?
2. **Dimension.** Open the dimension breakdown to see which type of problem is pulling the score down — a completeness problem and a uniqueness problem call for very different fixes.
3. **Field.** Drill into the weakest dimension's field health to find the specific fields driving failures. This is the layer someone can own and fix.
4. **Trend.** Check the trend line. A sudden dip usually means a new integration or process started writing bad data — catch it here, not in a broken report three months from now.

## Why Trends Beat Snapshots

A single measurement is obsolete the day after you take it, because Salesforce data changes constantly. The real value of a dashboard is the **trend**. A score of 82 means little on its own; 82 and falling for three weeks is an alarm, while 82 and climbing is proof your program works. Scheduled scans are what turn a one-time audit into a trend you can manage — and what let you set a target and watch the line move toward it.

## What "Good" Looks Like

There is no universal passing score; it depends on how the data is used. A practical way to set targets is to tier them by stakes:

| Data | Target |
|---|---|
| Regulatory / compliance fields | 99%+ |
| Customer-facing and revenue data | 95%+ |
| Operational data | 85%+ |
| Historical / archival data | 70%+ |

Set the target per dimension and per object, then let the dashboard tell you how far each one has to go.

## Building It in DQS

Data Quality Sense provides this dashboard inside Salesforce through Insight Studio. After you run a scan from the Definition Builder, Insight Studio shows the weighted Data Quality Score, the per-dimension breakdown, field health, and the trend across scans — plus PII exposure for AI-readiness work. Because scans run natively and on a schedule, the dashboard always reflects live data in your org, with no exports and no external pipeline to maintain.

## Next Steps

- [How to Measure Data Quality in Salesforce](/resources/salesforce/measure-data-quality-in-salesforce/): the Data Quality Score in depth
- [Data Quality in Salesforce](/resources/salesforce/data-quality-in-salesforce/): the complete guide
- [How to Improve Data Quality in Salesforce](/resources/salesforce/improve-data-quality-in-salesforce/): from dashboard to action
- [Measuring Data Quality](/resources/best-practices/measuring-data-quality/): KPIs and scorecards in depth