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10 Knowledge/CTO Academy/Data/Data Observability.md

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+**MD file:** `Data Observability.md`  
+**Estimated reading time:** 10–12 minutes
+
+# Data Observability
+
+## Definition
+
+**Data Observability** is the ability to understand the health of data systems and quickly detect, diagnose, and resolve data problems.
+
+## Simple Mental Model
+
+> **Monitoring tells you that a pipeline ran. Data Observability tells you whether the data produced by that pipeline still makes sense.**
+
+## How It Works
+
+Common signals include:
+
+- Freshness
+- Volume
+- Schema changes
+- Distribution changes
+- Quality-rule violations
+- Downstream impact through lineage
+
+Example:
+
+```text
+NULL platform_id
+Normal: 0.2%
+Today: 46%
+```
+
+## Example
+
+A pipeline reports success but processes only 3.2M events when the normal range is 8M–10M.
+
+Observability should alert before executives consume the resulting dashboard.
+
+## How It Fits Into the Bigger Picture
+
+```text
+[[Data Quality]]
+Rules / expectations
+       +
+[[Data Lineage]]
+Dependencies
+       +
+Operational telemetry
+       ↓
+[[Data Observability]]
+       ↓
+Detect + diagnose + alert
+```
+
+## My Company / Real-World Context
+
+Useful signals could include:
+
+- Viewing events received/hour
+- Missing content ID percentage
+- Unknown platform percentage
+- Time since last successful load
+- Source-to-Gold processing delay
+
+## CTO Perspective
+
+Ask:
+
+> **How do we know our data is broken before a business user tells us?**
+
+Avoid alerting on everything. Prioritize executive KPIs, revenue, compliance, and critical operational dashboards.
+
+### Questions to Ask
+
+- How quickly do we detect stale or incomplete data?
+- Do we monitor volume and distribution, not just pipeline status?
+- Which critical datasets have automated checks?
+- Can alerts show downstream impact through [[Data Lineage]]?
+- Who owns incident response?
+
+## Meeting Scenario
+
+**Situation:** Executives repeatedly discover broken dashboards before the data team does.
+
+**Possible response:**
+
+> "The recurring issue isn't only data quality; it's detection. We need observability on critical data products so freshness, volume, schema, and key quality conditions are checked automatically."
+
+## Key Takeaways
+
+- Data Observability detects and diagnoses unhealthy data systems.
+- A successful pipeline does not guarantee healthy data.
+- Freshness, volume, schema, distributions, and quality are common signals.
+- [[Data Lineage]] helps identify downstream impact.
+- Prioritize observability based on business criticality.
+
+## Related Concepts
+
+- [[Data Quality]]
+- [[Data Lineage]]
+- [[Data Governance]]
+- [[Metadata]]
+- [[Data Contract]]
+- [[Monitoring]]
+- [[Data Incident]]