Data Quality Scorecard

Use this scorecard to audit your CRM data quality monthly. Poor data quality silently undermines lead scoring, routing, attribution, and reporting. This scorecard makes the problem visible and measurable.

Data Quality Dimensions

Score each dimension from 1 (critical issues) to 5 (excellent). Assess at both the contact and account levels.

1. Completeness (Weight: 30%)

Measures whether required fields have values.

Object	Required Fields	Target Fill Rate	Score 5	Score 3	Score 1
Contact	First name, last name, email, title, company	95%+	> 95%	80-95%	< 80%
Contact	Phone number	70%+	> 70%	50-70%	< 50%
Account	Company name, industry, employee count, website	95%+	> 95%	80-95%	< 80%
Account	Revenue, technology stack	60%+	> 60%	40-60%	< 40%
Lead	Source, campaign, UTM parameters	90%+	> 90%	70-90%	< 70%
Opportunity	Amount, close date, stage, owner	98%+	> 98%	90-98%	< 90%

2. Accuracy (Weight: 25%)

Measures whether the data values are correct and up-to-date.

Check	Method	Target	Score 5	Score 3	Score 1
Email deliverability	Bounce rate on recent sends	< 2% bounce	< 2%	2-5%	> 5%
Phone number validity	Sample verification of 50 numbers	> 85% valid	> 85%	60-85%	< 60%
Job title accuracy	LinkedIn cross-reference sample	> 80% match	> 80%	60-80%	< 60%
Company data (size, industry)	Third-party data comparison	> 85% match	> 85%	65-85%	< 65%

3. Freshness (Weight: 20%)

Measures how recently data has been verified or updated.

Check	Target	Score 5	Score 3	Score 1
% of contacts updated in last 90 days	> 60%	> 60%	30-60%	< 30%
% of contacts with no activity in 180+ days	< 20%	< 20%	20-40%	> 40%
% of accounts with stale enrichment (180+ days)	< 15%	< 15%	15-30%	> 30%
Average age of contact records	< 12 months	< 12 months	12-24 months	> 24 months

4. Consistency (Weight: 15%)

Measures whether data follows standardized formats and values.

Check	Target	Score 5	Score 3	Score 1
Industry values using picklist (not free text)	> 95%	> 95%	80-95%	< 80%
Country/state using standard format	> 95%	> 95%	80-95%	< 80%
Lead source values using picklist	> 90%	> 90%	70-90%	< 70%
Duplicate contact rate	< 3%	< 3%	3-8%	> 8%
Duplicate account rate	< 2%	< 2%	2-5%	> 5%

5. Uniqueness (Weight: 10%)

Measures the extent of duplicate records in your database.

Check	Target	Score 5	Score 3	Score 1
Contact duplicates (same email)	< 1%	< 1%	1-3%	> 3%
Account duplicates (same domain)	< 2%	< 2%	2-5%	> 5%
Orphaned contacts (no associated account)	< 5%	< 5%	5-15%	> 15%

Overall Data Quality Scorecard

Dimension	Weight	Score (1-5)	Weighted Score
Completeness	30%
Accuracy	25%
Freshness	20%
Consistency	15%
Uniqueness	10%
Total	100%		/5.0

Score Interpretation

Weighted Score	Rating	Action
4.5 - 5.0	Excellent	Maintain current processes. Focus on automation to sustain quality.
3.5 - 4.4	Good	Address 1-2 weakest dimensions. Minor process improvements needed.
2.5 - 3.4	Fair	Data quality is impacting operations. Prioritize a data cleanup initiative.
1.5 - 2.4	Poor	Significant data issues across multiple dimensions. Dedicate resources to a data quality project.
1.0 - 1.4	Critical	Data is unreliable for reporting and operations. Stop building on top of bad data and fix the foundation.

Monthly Tracking

Month	Completeness	Accuracy	Freshness	Consistency	Uniqueness	Overall Score	Trend

Remediation Priorities

After scoring, identify the top 3 actions to improve your score.

Priority	Dimension	Issue	Action	Owner	Deadline
1
2
3