Data Quality · January 2026 · 10 min read

Data Quality Metrics That Actually Matter

You could track a hundred data quality metrics. Field completion percentages, format compliance scores, freshness indicators, consistency ratios. Most of them won't tell you anything useful.

The metrics that matter are the ones that predict operational problems. The ones that, when they go bad, mean something breaks. Here are those metrics, how to calculate them, and what targets to aim for.

The Problem with Most Data Quality Metrics

Traditional data quality frameworks give you metrics like "accuracy," "completeness," "consistency," and "timeliness." These sound reasonable but often miss the point.

Knowing that your "completeness score" is 78% doesn't tell you anything actionable. Completeness of what? Which fields? Does it matter if they're empty? What's the actual business impact?

Good metrics connect directly to operational outcomes. When this metric gets worse, something specific breaks. When it improves, something specific works better. That's the test.

The Core Metrics

How to measure: Run your contact emails through a verification service. Count valid/total. Segment by marketing status since active contacts matter more than dormant ones.

When to check: Monthly for active marketing contacts, quarterly for full database.

How to measure: Run duplicate detection using email exact match, name + company fuzzy match, and phone number match. Count unique records flagged as having duplicates. Divide by total records.

When to check: Monthly. Track trend over time to see if duplicates are being created faster than they're being merged.

How to measure: Identify the specific fields your routing logic uses (typically: company size, industry, country, maybe title or revenue). Calculate completion rate for each on your active lead population.

When to check: Weekly if you have high lead volume. At minimum, before any routing rule changes.

How to measure: Count contacts with no account/company association. Divide by total contacts. For HubSpot, this is contacts without company associations. For Salesforce, contacts without an account relationship.

When to check: Monthly. Spikes often indicate integration issues or import problems.

The Operational Metrics

Beyond the core data metrics, track metrics that show data quality impact on operations.

Building a Dashboard

Put these metrics on a single dashboard that gets reviewed regularly. Here's a structure that works:

Weekly Review (Ops Team)

• Campaign bounce rates from past week
• Routing exceptions from past week
• New duplicates created (if trackable)
• Any sudden changes in key metrics

Monthly Review (Leadership)

• Email validity rate (trend)
• Duplicate rate (trend)
• Routing field completion (current)
• Contact-account association rate (current)
• Month-over-month changes with context

Quarterly Deep Dive

• Full data quality audit with detailed metrics
• Decay rate calculation for the quarter
• Segment-specific analysis (by source, by age, by segment)
• Recommendations for next quarter's priorities

Setting Targets

The targets above are reasonable benchmarks, but your specific targets should consider:

• Where you are now: If email validity is at 60%, targeting 95% immediately is unrealistic. Set incremental targets.
• Your tolerance for operational issues: Some businesses can tolerate more routing exceptions than others.
• The cost of improvement: Moving from 90% to 95% is harder than 80% to 85%. Set targets where the ROI makes sense.

What Not to Measure

Some commonly tracked metrics aren't worth the effort:

• Overall "completeness score": Too vague. Completion of what fields? Not all fields matter equally.
• Format compliance: Unless bad formatting actually breaks something, it's not worth tracking.
• Data age: Age alone doesn't mean bad. A 3-year-old contact could be perfectly accurate.
• Record counts: More records isn't better if they're duplicates or invalid.

Focus on metrics tied to outcomes. If you can't explain how a metric affects operations, you probably don't need it.

Using Metrics to Prioritize

Once you have baseline metrics, use them to decide what to fix first:

1. Fix what's broken: If email validity is at 70%, that's your first priority. It's actively hurting deliverability.
2. Fix what blocks operations: If routing field completion is at 60%, leads aren't being handled properly. Fix that next.
3. Maintain what's working: If duplicate rate is at 4%, maintain it. Set alerts if it starts climbing.
4. Improve for optimization: Once basics are stable, improve metrics that unlock better performance (enrichment, segmentation accuracy, etc.).