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24/06/2026 9:06 pm
TL;DR: Fragmented governance, quality and observability tools leave only 37% of data and AI executives saying they have improved data quality, and less than a third of organisations use a single unified platform, according to Collibra. The governing issue is not just tool sprawl but the inability to trace cause, impact and accountability across the data flow fast enough to act.
NHIMG editorial — based on content published by Collibra: Unification of data quality and observability with data and AI governance
By the numbers:
- Only 37% of data and AI executives said they have been able to improve data quality.
- The out-of-the-box passing range is 90-100.
Questions worth separating out
Q: How should teams unify data governance with quality and observability?
A: Teams should connect policy, technical monitoring, lineage and ownership to one asset model.
Q: When does a data quality score become operationally useful?
A: A score becomes operationally useful when it is tied to agreed thresholds and response ownership.
Q: What do organisations get wrong about data observability?
A: They often treat observability as a monitoring dashboard instead of a governance mechanism.
Practitioner guidance
- Map quality rules to a single governance asset model Align business policies, technical monitors and asset ownership so every finding points to one accountable steward and one remediation path.
- Set score thresholds before you operationalize monitoring Define passing, warning and failing bands up front, then tie each band to a specific escalation or stewardship workflow.
- Correlate lineage, alerts and ownership in one workflow Replace manual stitching across tools with a workflow that links alerts to lineage context and the responsible data owner.
What's in the full article
Collibra's full post covers the operational detail this post intentionally leaves for the source:
- Step-by-step setup flow for Data Quality Jobs across schemas and tables
- Exact monitor types for schema change, null values, uniqueness and data type checks
- How score aggregation maps column and table results into catalog assets
- Configuration detail for notifications, schedules and dashboard alerts
👉 Read Collibra's post on unified data quality and observability →
Data quality and observability: what IAM teams should notice?
Explore further
25/06/2026 6:06 am
Fragmented governance is the real control failure, not weak point tools. The article's core point is that organisations keep policy, quality and observability in separate systems, then pay for the resulting manual stitching. That splits evidence from enforcement and makes it difficult to determine cause, impact and accountability at speed. The practitioner conclusion is straightforward: if the control plane is fragmented, response time and governance confidence will both degrade.
A few things that frame the scale:
- 1 in 4 organisations are already investing in dedicated NHI security capabilities, with an additional 60% planning to do so within the next twelve months, according to The State of Non-Human Identity Security.
- Another finding shows that 85% of organisations lack full visibility into third-party vendors connected via OAuth apps, with 38% reporting no or low visibility and 47% only partial visibility.
A question worth separating out:
Q: How can data teams reduce manual troubleshooting across governance tools?
A: They should reduce tool fragmentation and route alerts, rules and ownership through a shared workflow. When governance, quality and observability sit in separate products, teams waste time stitching together evidence and assigning tasks. A unified operating model shortens triage and makes remediation easier to audit.
👉 Read our full editorial: Data quality and observability need unified governance
