Data lineage
The traceable record of a dataset's origin and every system it has flowed into or fed, including any model it has trained. The lineage map built in Module 1 is the prerequisite artifact that makes the retention-state classification in Section 3C possible for any given model in an organization's estate. (see Topic 1.6)
Defined in 2 GAGE programs, which carry 2 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.
The traceable record of a dataset's origin and every system it has flowed into or fed, including any model it has trained. The lineage map built in Module 1 is the prerequisite artifact that makes the retention-state classification in Section 3C possible for any given model in an organization's estate. (see Topic 1.6)
The traceable path a record follows from where it is created, through every copy and transformation, to where the AI consumes it; without it, data cannot be fully trusted or debugged.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Retention versus the model that memorized: deleting data a model already learned from · Consent, Purpose, and the Law of Data, Certified AI Data Governance Professional (CADGP)
- Data Landscape Audit and Quality Assessment · Diagnose the Organization, Certified AI Transformation Professional (CATP)
Where it shows up at work
A definition is where Data lineage 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.