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 3 GAGE programs, which carry 4 distinct definitions of it. The wording above is taught in AI Data Governance: The Data Chair.
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.
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 7 topics of every program are free with a free account.
- Data Literacy for AI: Datasets, Quality, and Pipeline Basics · Advanced AI Literacy, AI Literacy & Professional Conduct
- The six failure modes of data quality, found live in your own warehouse · Quality as Physics, AI Data Governance: The Data Chair
- Freshness, completeness, and the lie of the green dashboard · Quality as Physics, AI Data Governance: The Data Chair
- Data Landscape Audit and Quality Assessment · Diagnose the Organization, Business AI Transformation
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.