You cannot improve what you do not measure. In Salesforce, measuring data quality means turning a vague sense that “the data is messy” into a number you can track, a breakdown you can act on, and a trend you can watch. That number is a Data Quality Score — sometimes called a data reliability score — and this guide explains how it works, how to read it, and how to act on it.
What a Data Quality Score Is
A Data Quality Score is a single figure, on a 0–100 scale, that summarizes how well a set of Salesforce records meets the quality rules you define. A score of 100 means every record in scope passed every check; a lower score tells you both how much work remains and, when broken down, exactly where it concentrates.
The score is not a vanity metric. Its value comes from three properties:
- It is composite. The score rolls up multiple data quality dimensions — completeness, validity, uniqueness, consistency, timeliness — into one comparable number.
- It is weighted. Not every problem matters equally, so the score reflects business priority rather than raw issue counts.
- It is repeatable. Run on a schedule, the same calculation turns a one-time audit into a trend line you can manage.
“Data reliability score” and “data quality score” describe the same idea: a quantified, trustworthy measure of whether your data is fit for use.
How the Score Is Calculated
A meaningful score in Salesforce is built bottom-up, from fields to dimensions to an overall number:
- Field-level checks. Each rule runs against the fields in scope. Is Account Industry populated? Does Contact Email match a valid format? Is this Opportunity a duplicate? Every check produces a pass/fail at the record level.
- Dimension scores. Field results roll up into a score for each dimension. If 92% of in-scope records pass every completeness check, completeness scores 92.
- Weighted overall score. Dimension scores combine into one figure, weighted by how much each dimension matters to you. A missing Opportunity Amount can count for more than a missing secondary phone number.
This bottom-up structure is what makes the score actionable. A single “78” is a starting point. The breakdown behind it — completeness 65 on Accounts, driven by a blank Industry field from one integration — is what you actually fix.
Why Weighting Matters
Two orgs can both score 80 and be in completely different shape. One has minor formatting issues spread across low-stakes fields. The other has 20% of its Opportunity Amounts missing. An unweighted count of failures would treat these the same.
Weighting fixes that. By assigning higher weight to the fields and dimensions that drive revenue, reporting, and automation, the score tracks business impact rather than issue volume. When you tune weights to your priorities, the number starts to mean something a leader can trust.
Reading the Score
A score is only useful if you can move from the headline number to a decision. Read it in three passes:
| Pass | Question | What you look at |
|---|---|---|
| 1. Headline | How healthy is this data overall? | The single weighted Data Quality Score |
| 2. By dimension | What kind of problem dominates? | Per-dimension scores (e.g. completeness vs. uniqueness) |
| 3. By field | Where exactly is the problem? | Field-level breakdown within the weakest dimension |
By the third pass you are no longer looking at “data quality” in the abstract. You are looking at a specific field, on a specific object, with a specific failure rate — which is a task someone can own.
From Score to Action
A score turns measurement into a prioritized to-do list:
- Baseline. Run the first scan to establish where you stand.
- Prioritize. Sort issues by business impact (weight) against effort to fix. The highest-weight, lowest-effort problems come first.
- Fix. Clean up existing records, add validation rules to stop new bad data, and adjust intake processes.
- Re-measure. Run the scan again and watch the score move. Improvement you cannot see is improvement you cannot defend.
Tracking Quality Over Time
A single measurement is obsolete the day after you take it, because Salesforce data degrades continuously. The point of a score is the trend, not the snapshot. Scheduled scans — daily, weekly, or monthly — turn the score into a line you can monitor, so a new integration that starts writing bad data shows up as a dip you catch in days, not a problem you discover months later in a broken report.
How DQS Measures It
Data Quality Sense produces a weighted Data Quality Score entirely inside Salesforce — no records are exported. You define what good looks like in the Definition Builder (select dimensions, scope objects and fields, set thresholds and weights), run the scan on demand or on a schedule, and explore the result in Insight Studio: the overall score, the per-dimension breakdown, field health, and the trend over time. Because it runs natively, the score always reflects live data in your org.
Next Steps
- Data Quality in Salesforce: the complete guide
- The Five Dimensions of Data Quality: what the score measures
- Measuring Data Quality: KPIs and scorecards in depth
- Agentforce Preparation: getting your score AI-ready