Topic starter
08/06/2026 5:01 pm
TL;DR: Data observability is the practice of using telemetry, lineage, and pipeline state to understand data health across distributed systems, and StrongDM argues it shortens MTTD and MTTR while exposing the cost of data silos and standardisation gaps. The larger lesson for identity teams is that visibility without governance is not observability, especially when access to data is spread across many tools and actors.
Editorial analysis by NHI Mgmt Group, based on content published by StrongDM: “Data Observability: Meaning, Framework & Tool Buying Guide”.
Key questions
Q: How should teams implement data observability across fragmented systems?
A: Start with a standard telemetry model, then integrate source systems, pipelines, and consumers into one governance process.
Q: When does data observability reduce risk instead of adding another dashboard?
A: It reduces risk when the signals it collects change operational decisions, triage priorities, and data governance actions.
Q: What breaks when data teams cannot standardise telemetry across tools?
A: Correlation breaks first, followed by root-cause analysis, auditability, and trust in the data itself.
Practitioner guidance
- Establish a standard telemetry library Define the canonical fields, event types, and naming conventions for logs, metrics, traces, and lineage so multiple teams can correlate data without translation work.
- Map data ownership across the stack Assign accountable owners for pipelines, warehouses, data sources, and access paths so observability findings always have a remediation destination.
- Connect observability to access governance Tie data movement and pipeline state back to the identities, service accounts, and applications that can read or transform that data.
Bottom line: Data observability gives teams a way to trace data health across tools, but it does not replace governance over access, retention, or ownership.
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This topic was modified 4 days ago by NHI Mgmt Group
08/06/2026 6:58 pm
Visibility is not governance, and data observability makes that gap visible. The article correctly shows that telemetry can expose how data moves across tools, but it also shows that insight alone does not enforce ownership, retention, or access discipline. That is the same failure mode identity teams see when logs, lineage, or reviews exist in isolation. Practitioners should treat observability as a control input, not the control itself.
A few things that frame the scale:
- Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: What is the difference between data quality and data observability in a modern data platform?
A: Data quality describes whether the data itself is fit for use, meaning accurate, complete, consistent, and timely enough for the intended purpose. Data observability is the monitoring discipline that watches data pipelines and datasets for anomalies, drift, freshness issues, and lineage changes. Used together, they help teams prevent bad data from shaping AI and reporting.
👉 Read our full editorial: Data observability exposes the governance gap in modern identity
This post was modified 4 days ago by NHI Mgmt Group