Completeness
A data quality dimension measuring whether all expected data is present and usable; splits into structural completeness (no missing fields or rows) and semantic completeness (the present values are true and meaningful).
Defined in 4 GAGE programs, which carry 5 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 data quality dimension measuring whether all expected data is present and usable; splits into structural completeness (no missing fields or rows) and semantic completeness (the present values are true and meaningful).
Whether enough data is present across all the cases that matter, including the groups and situations the AI will face. A dataset can be accurate yet dangerously incomplete about who it left out.
Whether a control acts on everything it should. Tested by an independent count against a source of truth rather than against the control's own records.
The share of required fields that are actually populated with real values, not blanks or placeholders.
The dimension of data quality measuring whether all values and records that should be present actually are present, typically measured through null counts and expected-versus-actual row counts.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Data Literacy for AI: Datasets, Quality, and Pipeline Basics · Advanced AI Literacy, Certified AI Practitioner: Workplace Foundations
- The six failure modes of data quality, found live in your own warehouse · Quality as Physics, Certified AI Data Governance Professional (CADGP)
- Freshness, completeness, and the lie of the green dashboard · Quality as Physics, Certified AI Data Governance Professional (CADGP)
- Data Landscape Audit and Quality Assessment · Diagnose the Organization, Certified AI Transformation Professional (CATP)
- Design effectiveness and operating effectiveness · Internal Control Architecture and Control Engineering, Certified Enterprise Governance, Risk and Compliance Professional (CEGP)
Where it shows up at work
A definition is where Completeness 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.
3 lessons on this page teach it as a primary outcome.
Maps risks to preventive, detective and corrective controls, then tests design and operation with samples, evidence and defensible findings.
One lesson on this page teach it as a primary outcome.
Designs role-based AI training that measures skill, not attendance, with approved-use guidance, office hours and communities of practice.
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
- Chief Audit Executive, AI audit editionneeds it at depth
- Senior AI Compliance Analystneeds working knowledge
- AI Auditorneeds it at depth · 9 open postings this week
- AI Compliance Managerneeds it at depth · 2 open postings this week
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; COSO; ISO/IEC 42001 Annex A.
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.
- Compliance and regulatory affairs carries it as work you can show: Control mapping and testing evidence exist in your files.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.