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01/08/2026 8:55 pm
TL;DR: High-quality training data improves predictive model accuracy, while poor data quality can produce weak, misleading outputs and costly business decisions, according to Openlayer's analysis. Data profiling, validation, and governance are now baseline controls for AI programmes, not optional hygiene.
NHIMG editorial — based on content published by Openlayer: Understanding and measuring data quality
Questions worth separating out
Q: How should security teams govern data quality for AI and identity systems?
A: Treat data quality as an operational control with owners, thresholds, and audit trails.
Q: Why does poor data quality create security risk as well as model risk?
A: Poor data quality undermines trust in automated decisions, including fraud signals, access decisions, and AI recommendations.
Q: What do organisations get wrong about data quality in machine learning pipelines?
A: They often focus on model architecture first and input quality second.
Practitioner guidance
- Define acceptance criteria for model inputs Set explicit thresholds for completeness, accuracy, timeliness, and schema consistency before any dataset can be used for training or scoring.
- Implement data profiling at ingestion Run profiling checks as soon as new data lands so outliers, missing fields, and duplicate records are detected before they spread into analytics or model pipelines.
- Standardise key fields across systems Normalize field names, formats, and units across source systems so downstream systems do not have to reconcile inconsistent representations.
What's in the full article
Openlayer's full post covers the operational detail this post intentionally leaves for the source:
- Step-by-step explanations of each data-quality dimension and how to evaluate them in practice.
- Examples showing how profiling and validation reduce downstream model errors before deployment.
- The article's own framing of how data governance improves machine learning reliability.
- A practical walkthrough of how deep learning can be used to identify outliers in large datasets.
👉 Read Openlayer's analysis of data quality and machine learning reliability →
Data quality governance: what it means for AI model risk?
Explore further
01/08/2026 9:03 pm
Data quality is an AI governance control, not just a data-management concern. The article shows that training data defects flow directly into model outcomes, which means quality failures become decision failures. For identity and security programmes, that matters wherever scoring, risk decisions, or automation depend on reliable data. The practical conclusion is that AI governance needs measurable input controls before it needs more model tuning.
A question worth separating out:
Q: How do you know if data quality controls are actually working?
A: Look for fewer manual remediation cycles, faster detection of inconsistencies, and higher confidence in shared datasets across teams. Effective controls should reduce debate about whether data can be used and increase the speed at which defects are corrected. If people still hesitate to act on the data, the control environment is not yet working.
👉 Read our full editorial: Data quality governance is now an AI model risk control
