Data Health: KPI and Health Metrics in Data Quality, Data Observability and Data Governance

In my last blog we saw how we need data quality, data observability and data governance for tracking complete data health.
Now we will see the exhaustive KPI and metric landscape that enables us to measure and monitor data health holistically.

Just as we track blood pressure, cholesterol, and heart rate to understand physical health, data health requires well-defined vital signs across multiple domains. These KPIs ensure that data is not just accurate, but also reliable, observable, and compliant.

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📊 Data Quality Metrics

Data Quality focuses on whether data is fit for its intended use.

• Accuracy → % of records with valid values (e.g., correct customer emails)
• Completeness → % of missing values across critical attributes
• Consistency → Number of conflicts between systems (CRM vs ERP)
• Timeliness → Latency between event capture and reporting availability
• Uniqueness → Ratio of duplicate records in critical datasets

👉 These are the vital signs of the data itself.

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🔍 Data Observability Metrics

Observability is about monitoring data in motion and pipelines—like a Fitbit for your data ecosystem.

• Freshness → Average delay in ingestion or pipeline runs
• Volume → Deviation from expected dataset sizes
• Schema Changes → Number of schema drift incidents
• Lineage Coverage → % of datasets with end-to-end lineage
• Error Rate → % of failed pipeline jobs

👉 These are the early warning signals that keep data flowing reliably.

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🛡️ Data Governance Metrics

Governance provides the policies, controls, and compliance backbone.

• Policy Compliance → % of datasets correctly classified (PII, Confidential, Public)
• Stewardship Coverage → % of critical datasets with assigned data stewards
• Access Control → Number of unauthorized access attempts blocked
• Auditability → % of datasets with complete audit logs
• Regulatory Alignment → Number of non-compliance incidents in audits

👉 These ensure trust, ethics, and compliance in your data ecosystem.

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🧮 How to Calculate a Final Data Quality Score

Organizations often want a single composite metric to summarize overall data health. A Data Quality Score (DQS) can be computed by combining multiple metrics, weighted by their importance.

Step 1. Define Dimensions & KPIs
For example: Accuracy, Completeness, Consistency, Timeliness, Uniqueness.

Step 2. Assign Weights
Each dimension gets a weight based on business importance. Example:

• Accuracy = 30%
• Completeness = 25%
• Consistency = 20%
• Timeliness = 15%
• Uniqueness = 10%

Step 3. Measure Each KPI
Calculate individual percentages, e.g.:

• Accuracy = 95%
• Completeness = 90%
• Consistency = 85%
• Timeliness = 80%
• Uniqueness = 98%

Step 4. Apply Weighted Formula

[ \text{DQS} = \sum (Metric_i \times Weight_i) ]

For the example above:
DQS = (95×0.3) + (90×0.25) + (85×0.2) + (80×0.15) + (98×0.1)
DQS = 90.05%

👉 This Final Data Quality Score provides a single, easy-to-communicate measure of health while still being transparent about contributing factors.

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📋 Comparison of KPIs Across Dimensions

Category	Example KPIs
Data Quality	Accuracy %, Completeness %, Consistency checks, Timeliness, Uniqueness
Data Observability	Freshness, Volume deviation, Schema drift count, Lineage coverage %, Error rate
Data Governance	Policy compliance %, Stewardship coverage %, Unauthorized access attempts, Audit log coverage, Regulatory compliance

👉 Use this table as a quick reference to map KPIs across the three key dimensions of data health.

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🌐 Connecting the Three Dimensions

• Quality = data is fit for purpose
• Observability = data pipelines are reliable
• Governance = data usage is safe and compliant

When combined, these metrics shift organizations from reactive firefighting to proactive prevention and build lasting trust in data.

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✅ Best Practices for Data Health KPIs

1. Create a unified Data Health Dashboard with all three categories.
2. Automate monitoring using tools like Great Expectations, Monte Carlo, Collibra.
3. Define thresholds & alerts for proactive action.
4. Link KPIs to business outcomes (revenue impact, compliance cost).
5. Continuously evolve metrics as your data estate grows.

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📚 Further Reading

If you’d like to explore more on KPIs and data health frameworks, here are some recommended standards and references:

• ISO 8000: Data Quality Standard → International standard for measuring and managing data quality. Read more
• VACUUM Model → Conceptual framework for structured data quality in ML systems. Read more
• Collibra: 6 Dimensions of Data Quality → Practitioner guide to accuracy, completeness, consistency, timeliness, validity, uniqueness. Read more
• Monte Carlo: Data Quality Metrics → Industry benchmarks for downtime, uptime, schema drift, and error rates. Read more
• DQoPs Data Quality Score Methodology → How to compute a % quality score with severity levels. Read more
• Academic Paper: Data Quality Assessment: Challenges and Opportunities → Research-driven perspectives on quality evaluation. Read more
• Secureframe: Governance Metrics → KPIs for stewardship, compliance, and ownership. Read more

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🔑 Final Thoughts

Data health is not a project—it’s a continuous discipline.
By establishing KPIs across quality, observability, and governance, and by rolling them into a Final Data Quality Score, organizations build resilient, trusted, and future-proof data ecosystems.

Healthy data = healthy decisions = healthy business.