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24/06/2026 7:40 pm
TL;DR: AI failures often trace back to unprofiled data, where completeness, consistency, and hidden relationships were never checked before production use, according to Collibra. Data profiling turns assumption into evidence early, which improves reliability, governance, and downstream AI decision quality.
NHIMG editorial — based on content published by Collibra: Before the algorithm: Why data profiling is the unsung hero of AI reliability
Questions worth separating out
Q: How should organisations use data profiling before AI deployment?
A: Organisations should use data profiling as a readiness gate before training or deploying AI.
Q: Why does unprofiled data create more AI risk than traditional reporting risk?
A: Unprofiled data creates more AI risk because AI systems can produce confident output even when the input is incomplete, inconsistent, or biased.
Q: What should security and governance teams look for in profiling results?
A: Teams should look for null-heavy fields, inconsistent formats, out-of-range values, and unexpected distribution shifts.
Practitioner guidance
- Profile critical datasets before AI use Run profiling on source tables, derived features, and reference data before training, prompting, or decision automation.
- Tie profiling results to data ownership Assign accountable owners for each dataset and require them to sign off on known quality limits, acceptable anomalies, and remediation paths.
- Set release gates for high-impact use cases Block production use when profiling shows unresolved drift, ambiguous definitions, or material missingness in fields that drive access, compliance, or AI decisions.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- A fuller breakdown of how data profiling fits into a practical reliability framework before automation and optimisation.
- Examples of the kinds of quality signals that matter most when teams are assessing whether data can support AI use.
- The article's own explanation of how profiling supports governance decisions across ownership, policy, and accountability.
- Context for the broader data reliability workflow the post only summarises here.
👉 Read Collibra's blog post on why data profiling underpins AI reliability →
Data profiling and AI reliability: what IAM teams should notice?
Explore further
25/06/2026 5:02 am
Data profiling is the first governance control that determines whether AI reliability is real or imaginary. The article is correct that failures often appear later, but the deeper point is that later failure usually reflects earlier uncertainty that was never made visible. When data is not profiled, teams are governing by confidence instead of evidence. Practitioners should treat profiling as the point where reliability becomes measurable rather than assumed.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap, according to The State of Secrets in AppSec.
A question worth separating out:
Q: How do organisations decide whether a dataset is fit for high-impact use?
A: A dataset is fit for high-impact use when its quality issues are understood, documented, and within agreed tolerance. That means the owner can explain known limitations, the profiling output matches the intended use, and downstream controls are aligned to the data’s actual behaviour. If any of those are missing, the dataset is not governance-ready.
👉 Read our full editorial: Data profiling is the missing control for AI reliability
