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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.
NHIMG editorial — based on content published by StrongDM: Data Observability: Meaning, Framework & Tool Buying Guide
By the numbers:
- Considering most organizations maintain an average of 400 data sources, standardizing telemetry across systems is often a manual effort.
- One-third of data analysts report spending over 40% of their time standardizing data for analysis.
- 57% of organizations still find transforming their data to be an extremely challenging task.
Questions worth separating out
Q: How should security teams connect data observability to access governance?
A: Security teams should treat data observability as a visibility layer that feeds governance, not as a substitute for it.
Q: Why do data silos make observability fail in practice?
A: Data silos prevent teams from correlating telemetry across warehouses, pipelines, applications, and storage.
Q: What should organisations standardise before adopting a data observability platform?
A: Organisations should standardise telemetry definitions, logging conventions, retention rules, and data quality expectations before broad platform adoption.
Practitioner guidance
- Define telemetry standards before platform rollout Create a shared library for logs, metrics, traces, and data quality rules so every source is measured against the same definitions.
- Map lineage to access ownership Require each critical pipeline to show upstream sources, downstream consumers, and the identities that can alter or publish data.
- Treat retention rules as part of observability design Build storage and retention policy into the observability architecture before scaling telemetry collection.
What's in the full article
StrongDM's full blog covers the operational detail this post intentionally leaves for the source:
- Step-by-step explanation of the five observability pillars and how StrongDM applies them to data health
- Tool selection guidance for teams comparing observability platforms across warehouses, pipelines, and storage
- Implementation advice on standardising telemetry, retention, and governance rules before rollout
- Practical examples of how observability can support security monitoring and reduce MTTD and MTTR
👉 Read StrongDM's guide to data observability, framework design, and tool selection →
Data observability and IAM: what visibility teams are missing?
Explore further
08/06/2026 6:58 pm
Observability without access governance is just better diagnostics for a broken operating model. The article makes a strong case for end-to-end visibility, but the deeper issue is that many organisations still treat data flow as a monitoring problem rather than an identity problem. When service accounts, APIs, and human users all touch the same data paths, visibility must extend to who can change, move, or retain data, not just whether a pipeline is healthy. Practitioners should read observability as a governance signal, not a standalone control.
A few things that frame the scale:
- Two-thirds of enterprises have endured a successful cyberattack resulting from compromised non-human identities, with a quarter encountering multiple attacks, according to The 2024 ESG Report: Managing Non-Human Identities.
- Only 5.7% of organisations have full visibility into their service accounts, which is why observability and governance need to be designed together rather than treated as separate disciplines.
A question worth separating out:
Q: How do teams know whether observability is actually improving data quality?
A: Teams should look for fewer unresolved schema breaks, faster root cause analysis, better freshness compliance, and less manual reconciliation across sources. If observability is working, incidents should become easier to diagnose and repeated data issues should decline over time. If the same issues keep reappearing, the programme has visibility but not governance.
👉 Read our full editorial: Data observability exposes the governance gap in modern identity
