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23/06/2026 7:01 pm
TL;DR: Data observability is the shift from rule-based checking to continuous diagnosis, using freshness, volume, distribution, schema and lineage to reveal what broke, when it started and what it affects, according to Collibra. Rule-based quality alone misses silent failures that can degrade AI systems and audit outcomes before anyone notices.
NHIMG editorial — based on content published by Collibra: Data observability platform: How to proactively monitor and trust your data at scale
Questions worth separating out
Q: How should security teams decide where data observability is needed first?
A: Start with the data that affects models, compliance reporting, privileged workflows and other decisions that cannot tolerate silent drift.
Q: Why do rule-based data quality checks fail in fast-changing environments?
A: They only catch failures that were anticipated and written as rules.
Q: What signals show that a data observability programme is actually working?
A: You should see faster detection of schema changes, fewer unresolved freshness issues, shorter triage times and clearer ownership when incidents occur.
Practitioner guidance
- Separate rule checks from observability coverage Inventory the datasets that feed models, reports and controls, then identify where rule-based checks are still being treated as full coverage.
- Tie every critical dataset to an owner and downstream map Ensure each monitored asset has a named owner, an upstream source map and a list of downstream consumers.
- Use drift signals to protect AI and regulatory outputs Prioritise observability on data that influences AI inference, financial reporting and compliance evidence.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- How the five observability pillars map to real monitoring workflows across data estates
- Examples of schema, freshness and distribution failures that can quietly break downstream systems
- The role of data lineage in routing alerts to the right owner and proving blast radius
- How Collibra describes data health scoring, alert suppression and governance integration
👉 Read Collibra's analysis of how data observability closes quality gaps →
Data observability and DQ gaps: are your controls keeping up?
Explore further
25/06/2026 2:47 am
Observability is now a trust control, not a reporting enhancement. The article correctly shows that modern data programmes fail when teams know a rule broke but cannot explain the change, the start time or the blast radius. That is the same trust problem identity teams face when lifecycle evidence exists but context does not. The field should stop treating observability as an advanced dashboard feature and start treating it as a prerequisite for accountable automation.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- 43% of security professionals are concerned about AI systems learning and reproducing sensitive information patterns from codebases, according to The State of Secrets in AppSec.
A question worth separating out:
Q: How does data observability support AI governance and compliance?
A: It provides continuous evidence that the inputs to AI systems and regulated reports are being monitored, not just checked occasionally. That matters when input drift can degrade model performance or create audit findings before anyone notices. Observability helps teams prove that data health was tracked across the reporting period, not reconstructed after the fact.
👉 Read our full editorial: Data observability closes the gap left by rule-based quality checks
