Data quality and observability: what IAM teams should notice

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?

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