Data quality management and AI readiness: where are teams failing?

TL;DR: Poor data quality is usually discovered after it reaches board reporting, model outputs, or regulators, and Collibra argues that continuous monitoring, not periodic cleanup, is what makes data reliable, compliant, and AI-ready. The real issue is that governance frameworks still treat data quality as a post hoc control when it now sits upstream of operational risk and AI performance.

NHIMG editorial — based on content published by Collibra: Data quality management, a framework for reliable, trusted and AI-ready data

Questions worth separating out

Q: How should teams implement data quality management for AI-ready data?

A: Start with the datasets that directly feed models, reporting, and control decisions.

Q: Why do data quality failures keep surfacing late in organisations?

A: Because many teams still rely on periodic review, manual cleanup, and downstream detection.

Q: What do security and governance teams get wrong about data quality?

A: They often treat data quality as a data operations issue rather than a control dependency.

Practitioner guidance

• Embed quality checks in pipeline design Define pass or fail rules at ingestion and transformation stages so nulls, schema drift, and duplicate records are caught before they reach reporting or AI systems.
• Separate controls by quality dimension Write distinct rules for accuracy, completeness, consistency, timeliness, validity, and uniqueness instead of assuming one validation layer can protect all data assets.
• Route failures to named data owners Connect each failed rule to a steward, source system, and lineage path so remediation can focus on root cause rather than manual tracing across platforms.

What's in the full article

Collibra's full blog post covers the operational detail this post intentionally leaves for the source:

• How Collibra structures data quality rules across ingestion, transformation, storage, and consumption stages
• The monitoring and stewardship workflow used to connect quality failures to lineage and ownership
• How quality evidence is tied to regulatory obligations such as BCBS 239 and GDPR
• The platform integration details behind catalog, lineage, governance, and observability workflows

👉 Read Collibra's data quality management framework for AI-ready data →

Data quality management and AI readiness: where are teams failing?

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