Hard case
A quality finding where the classification is genuinely ambiguous because a business rule, legal requirement, or intentional design choice might explain the pattern, requiring lineage tracing or direct confirmation from the data's owning team before a fix is committed, rather than a mechanical application of the six-failure-mode test.
Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in Certified AI Data Governance Professional (CADGP).
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
A quality finding where the classification is genuinely ambiguous because a business rule, legal requirement, or intentional design choice might explain the pattern, requiring lineage tracing or direct confirmation from the data's owning team before a fix is committed, rather than a mechanical application of the six-failure-mode test.
One of five recurring, real-world scheduling situations, part-time hours, an externally gated technical block, multiple concurrent builds, a passed docking window, or cohort pacing, identified in this topic (Section 3H) as requiring a specific adjustment to the standard interleaving guidance rather than abandonment of the principle.
A request in which the underlying rail or pattern is correctly identifiable but does not, on its own, resolve whether the specific instance is legitimate or fraudulent, requiring the rail check to be paired with independent verification rather than used alone, as catalogued in Section 3G.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- The six failure modes of data quality, found live in your own warehouse · Quality as Physics, Certified AI Data Governance Professional (CADGP)
- How to interleave this program with the robots you are already building · Your Engineering Judgment Command Center, Engineering Judgment and Professional Formation
- The rail check as a daily habit · Everyday Transaction Scams, Senior AI Scam Defense
Where it shows up at work
A definition is where Hard case starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Profiles data, sets rules and thresholds, monitors, assigns issues to owners, and knows when data is not fit to train or run a model.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- Chief Data Officer, data leadership editionneeds it at depth
- Data Governance Leadneeds it at depth · 2 open postings this week
- AI Model Validatorneeds working knowledge
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: EU AI Act Article 10; ISO 8000.
One interview question about it
Which data quality rules would you monitor for a model in production, and what happens when one fails?
Asked of candidates for data quality rules and monitoring. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Data analysis, stewardship and business intelligence carries it as work you can show: Quality rules and profiling results are your artifacts.
- A data science, statistics or analytics degree carries it as a claim to back up: Data quality rules are familiar; monitoring them in production is new.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.