Data Health: Unifying Data Quality, Observability, and Governance
A deep dive into why multiple terms exist in the data space, how they differ, what tools implement them, and how together they form a holistic view of data health.
🌐 The Expanding Vocabulary of Data Management
If you’ve been around the modern data stack, you’ve probably noticed a growing set of buzzwords: data quality, data observability, and data governance.
At first glance, they sound like different names for the same idea—trustworthy data. But in practice, each emerged at a different time, solves a different problem, and together they provide a complete picture of data health.
📖 Why So Many Terms?
The terminology reflects the evolution of challenges in data ecosystems:
2000s → Data Quality
Enterprises realized that reporting errors were caused not by systems, but by bad data. Rules-based checks (accuracy, completeness, duplicates) became the focus. Tools like Informatica Data Quality and later Great Expectations grew here.2010s → Data Governance
As data volume exploded, organizations needed control over who owns data, how it’s secured, and whether it complies with regulations (GDPR, HIPAA, etc.). Governance was coined as the framework for responsibility, metadata, and policies. Tools like Collibra, Alation, Atlan became popular.2020s → Data Observability
With cloud pipelines, data started breaking silently. Teams needed real-time monitoring of freshness, schema drift, lineage, and anomalies. Observability was borrowed from DevOps and applied to data. Tools like Monte Carlo, Databand, Bigeye entered the scene.
👉 The terms were required because each solved a different pain point in the maturing data lifecycle.
🧩 Differentiating the Three
| Term | Focus | Typical Metrics | Example Tools |
|---|---|---|---|
| Data Quality | Accuracy & reliability of datasets | % nulls, duplicates, validation rules | Great Expectations, Deequ, Soda |
| Data Governance | Policies, ownership, compliance | Stewardship %, lineage coverage, policy violations | Collibra, Alation, Atlan |
| Data Observability | Monitoring pipelines & anomalies | Freshness SLA, anomaly alerts, schema drift | Monte Carlo, Bigeye, Databand |
🛠️ How Are They Implemented?
- Data Quality → Implemented via validation rules, profiling, and automated tests at ingestion or transformation.
- Data Governance → Implemented via metadata catalogs, access controls, lineage graphs, and policy enforcement.
- Data Observability → Implemented via pipeline monitors, anomaly detection, SLA alerts, and root cause tracing.
Each requires different tooling but overlaps in purpose: ensuring trustworthy, usable data.
🩺 Data Health: The Unified View
Think of data health like human health. A doctor doesn’t rely only on blood pressure; they look at multiple indicators. Similarly, data health requires a dashboard combining quality, governance, and observability.
Example Dashboard Metrics
- ✅ Quality Trust Score → Weighted metric of validation checks.
- 🛡️ Governance Compliance % → % of datasets policy-compliant.
- 🔄 Pipeline SLA Status → % of jobs meeting SLA.
- 👤 Stewardship Coverage → % of datasets with assigned owners.
- 🚨 Active Incidents → Current open alerts.
⚠️ Is It Truly Comprehensive?
The trio covers ~80–90% of data health. But gaps remain:
- Security & Privacy Health → breaches, access audits.
- Cost & Efficiency Health → resource overuse, storage sprawl.
- AI/ML Data Health → model drift, bias, feature quality.
Forward-looking platforms are extending dashboards to capture these as well.
✅ Takeaway
- Data Quality ensures correctness.
- Data Governance ensures accountability.
- Data Observability ensures reliability.
Together, they form the core pillars of data health.
The future of data management lies not in treating these separately, but in weaving them into a single, unified Data Health Dashboard that both engineers and executives can trust.