Data observability and DQ gaps: are your controls keeping up?

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

View Full Forum →  |  NHI Foundation Course →