TL;DR: A data quality KPI is a single measurable indicator, such as fill rate or duplicate count, that you track against a target over time. This guide catalogs 18 KPIs across completeness, validity, uniqueness, timeliness, consistency, and AI readiness, each with a formula and an example target. It then shows how to roll them into a weighted scorecard using the same formula DQS applies inside Salesforce.
What Is a Data Quality KPI?
A data quality KPI is a single, measurable indicator that tracks one aspect of how fit your data is for use. Each KPI produces one number, usually a rate or a count, that you compare against a target and watch over time.
A KPI is not the same as a data quality score. A KPI measures one thing: the phone fill rate, the email validity rate, the duplicate count. A data quality score is a weighted composite that rolls many KPIs across several dimensions into one 0-100 number. The KPI tells you where the problem is. The score tells you the overall health.
Use both. Executives read the score. Data stewards act on the individual KPIs behind it.
Tracking KPIs is also how you put a price on the problem. Gartner estimates poor data quality costs organizations an average of $12.9 million per year, and in Salesforce’s State of Data and Analytics research data leaders rated 26% of their own data as untrustworthy. A KPI baseline turns those industry averages into your org’s actual numbers.
The Data Quality KPI Catalog
The KPIs below are organized by dimension. Each one maps to a metric you can compute directly, and each includes a formula and an example target. Set your own targets from the cost of being wrong: a field that feeds billing needs a higher bar than one used for occasional lookups. For the concepts behind each dimension, see The Five Dimensions.
Completeness KPIs
Completeness measures whether required data is present, counting NULL, blank, empty, and placeholder values as missing.
| KPI | Formula | Example Target |
|---|---|---|
| Fill Rate | Populated records / Total records | 95%+ for critical fields |
| Null Rate | Null or blank records / Total records | Below 5% |
| Placeholder Rate | Placeholder values / Total records | Below 2% |
Worked example: You have 10,000 Contact records. 8,700 have the Phone field populated. The phone fill rate is 8,700 / 10,000 = 87%. Against a 95% target, that is 800 records short.
Validity KPIs
Validity measures whether populated values match the expected format for their field type, such as email, phone, or URL. Validity depends on completeness: only populated values are checked.
| KPI | Formula | Example Target |
|---|---|---|
| Format Validity Rate | Correctly formatted values / Populated values | 98%+ for email, 90%+ for phone |
| Invalid Count | Populated values failing format checks | Trend toward zero |
| URL Validity Rate | Valid URLs / Populated URL values | 95%+ |
Worked example: Of the 8,700 populated phone numbers, 7,830 match a valid phone format. The format validity rate is 7,830 / 8,700 = 90%. The other 870 are populated but malformed.
Uniqueness KPIs
Uniqueness measures whether the same real-world entity appears more than once. DQS reports uniqueness rate, distinct count, and duplicate count.
| KPI | Formula | Example Target |
|---|---|---|
| Uniqueness Rate | Distinct values / Total populated values | 95%+ for identifier fields |
| Duplicate Count | Records sharing a value with another record | Trend toward zero |
| Distinct Count | Number of unique values | Context-dependent |
Worked example: Across 10,000 Contacts, 350 records share an email address with at least one other record. The uniqueness rate is 9,650 / 10,000 = 96.5%, and the duplicate count is 350. DQS flags these records so you can review them. It does not merge them for you; remediation stays under human control.
Timeliness KPIs
Timeliness, presented in DQS as Data Freshness, measures record age against a date field and a threshold you configure.
| KPI | Formula | Example Target |
|---|---|---|
| Freshness Rate | Records updated within threshold / Total records | 80%+ |
| Stale Record Count | Records exceeding the freshness threshold | Trend toward zero |
| Average Record Age | Mean days since the tracked date field | Varies by object |
Worked example: You set a 180-day freshness threshold on Accounts. Of 10,000 Accounts, 7,200 were updated within 180 days. The freshness rate is 7,200 / 10,000 = 72%, and 2,800 records are stale.
Consistency KPIs
Consistency, checked in DQS as a Conformance Check, measures whether values match the dominant or expected standard for a field.
| KPI | Formula | Example Target |
|---|---|---|
| Conformance Rate | Records matching the standard / Total records | 90%+ |
| Variant Count | Number of distinct value variations | Minimize |
| Dominant Value Coverage | Most frequent value / Total records | Context-dependent |
Worked example: Your Country field stores “United States” as the standard, but variants like “US”, “USA”, and “U.S.” exist. 9,400 of 10,000 records use the standard. The conformance rate is 94%, with 600 records to normalize.
AI Readiness KPIs
AI readiness KPIs measure whether text data is safe and dense enough to ground an assistant like Agentforce. DQS computes PII detection counts, content density, and language consistency.
| KPI | Formula | Example Target |
|---|---|---|
| PII Exposure Count | Records with detected SSN, card, email, or phone in text | Zero in fields used for AI grounding |
| Content Density | Average token count per text field | Above your model’s minimum useful length |
| Language Consistency Rate | Single-language records / Total text records | 95%+ for a single-language corpus |
Worked example: DQS scans the Description field on 10,000 Cases and detects unredacted SSN or credit-card patterns in 128 of them. The PII exposure count is 128. Those 128 records are candidates to redact before you expose the field to an AI model. In CSV exports, DQS masks the detected values as [REDACTED] so the report itself does not leak PII.
Leading vs Lagging Indicators
Data quality KPIs are leading indicators. They move before the outcomes they predict.
- A falling email validity rate is a leading indicator of a lagging outcome: campaign bounce rates and lost reach.
- A rising duplicate count is a leading indicator of inflated pipeline reports and double-counted revenue.
- A low PII exposure count and high content density are leading indicators of an AI assistant that answers safely and accurately.
Lagging indicators, such as bounce rate, forecast error, or a failed AI response, tell you the damage is already done. Track the KPI so you can act before the lagging metric moves.
Building a KPI Scorecard
A scorecard collects your headline KPIs into one view and rolls them into a single score. Keep it to one or two KPIs per dimension so it stays readable.
Example Scorecard
| Dimension | Headline KPI | Current | Target | Trend |
|---|---|---|---|---|
| Completeness | Fill Rate | 87% | 95% | Up |
| Validity | Format Validity Rate | 91% | 98% | Flat |
| Uniqueness | Uniqueness Rate | 96% | 98% | Up |
| Timeliness | Freshness Rate | 72% | 80% | Down |
| Consistency | Conformance Rate | 84% | 90% | Flat |
| AI Content | Language Consistency Rate | 80% | 95% | Up |
| PII Safety | PII Exposure (inverse) | 95% | 100% | Flat |
Rolling KPIs Into One Score
DQS combines dimension scores with a weighted average. The formula is:
Overall Score = SUM(dimension score × weight) / SUM(weight)
The default weights reflect how much each dimension affects reporting and AI outcomes. Adjust them to match your own business priorities.
| Dimension | Score | Weight | Weighted (Score × Weight) |
|---|---|---|---|
| Completeness | 87 | 25 | 2,175 |
| Validity | 91 | 20 | 1,820 |
| Uniqueness | 96 | 15 | 1,440 |
| Timeliness | 72 | 15 | 1,080 |
| Consistency | 84 | 15 | 1,260 |
| AI Content | 80 | 10 | 800 |
| PII Detection | 95 | 10 | 950 |
| Total | 110 | 9,525 |
The overall score is 9,525 / 110 = 86.6 out of 100.
Two details matter in that calculation. First, the denominator is the sum of the weights of the dimensions you actually scanned, not a fixed 100. If you skip the AI Content and PII dimensions, the denominator drops to 90 and the score reflects only the operational dimensions. Unscanned dimensions are excluded, so they never drag the score down. Second, the weights encode what “good” means for you. If AI readiness is a priority, raise the weight on PII and content density.
Common Mistakes
Avoid the failures that make a KPI program stall:
- Too many KPIs. A scorecard with 30 metrics gets ignored. Track one or two per dimension at the executive level and keep the detail in steward reports.
- KPIs with no target. A number without a target is trivia. Every KPI needs a target and an owner.
- Measuring once. A single scan is a snapshot. The value is in the trend, so schedule re-measurement and compare periods.
- Treating empty datasets as perfect. An unscanned or empty field is not 100% complete. DQS deliberately scores a dimension with no data to measure as 0, not 100, so gaps stay visible instead of inflating your score.
Tracking KPIs in Salesforce
These KPIs map directly onto how DQS works inside Salesforce. DQS scans any SObject, including Accounts, Contacts, Leads, and custom objects, with field selection and scope filters. It computes fill rate, format validity rate, uniqueness rate, freshness rate, conformance rate, and PII detection counts per field, then rolls them into a weighted score. Scheduled scans keep the numbers current, score trend charts show whether each KPI is improving, scan comparison surfaces the delta between two periods, and CSV export moves the results into your BI tool for reporting. Uniqueness violations are the one exception: they are not exported to CSV.
Ready to see your own KPIs? Take the AI Readiness Assessment to get your baseline scores in a few minutes, or contact us to walk through the scorecard on your data.
Frequently Asked Questions
What are data quality KPIs?
Data quality KPIs are single, measurable indicators that track one aspect of how fit your data is for use. Each KPI is a rate or a count, such as fill rate, format validity rate, or duplicate count. You measure each one repeatedly against a target so you can see whether your data is improving or slipping.
What is an example of a data quality KPI?
Fill rate is the most common example. If 8,700 of 10,000 Contact records have a phone number, the phone fill rate is 87%. Other examples include validity rate, uniqueness rate, freshness rate, and PII exposure count.
How many KPIs should a scorecard have?
Track one or two headline KPIs per dimension, which gives you five to eight metrics for a typical operational scorecard. Fewer KPIs keep the scorecard readable and make trends easy to communicate. Add detailed field-level KPIs only in the working reports your data stewards use.
What is the difference between a data quality KPI and a data quality score?
A data quality KPI is a single measurable indicator, such as an 87% fill rate for one field. A data quality score is a weighted composite that rolls many KPIs across several dimensions into one 0-100 number. KPIs tell you where the problem is; the score tells you the overall health.
Next Steps
- Measuring Data Quality: build the measurement program, cadence, and ROI case around these KPIs
- What Is a Data Quality Score?: how the weighted composite behind your scorecard is calculated
- The Five Dimensions: the concepts each KPI measures
- Salesforce Data Quality Dashboard: the metrics worth watching alongside the headline score