Data foundations for AI
On This Lesson 4 topics
This lesson is planned. The outline below lists the topics it will cover. The full lesson has not been published yet.
In this lesson
show that AI quality depends on data ownership, quality, and access.
Planned topics
- RAG and fine-tuning use your data; unclear ownership and poor quality produce poor answers.
- Needs: current catalogs, documented sources, access controls, and freshness guarantees.
- Sensitive data requires classification before it goes near a model.
- Build the data contract and ownership first (SRA-004) and AI second.
Draft outline details
Demonstration
a readiness checklist per use case: data source, owner, freshness, quality checks, sensitivity class, access path.
Check your understanding
Why does AI strategy depend on data ownership?
Show answer
Without owners and quality, retrieved or training data is stale or wrong, and so are the answers.
Practice
Apply this lesson with the module's lab, exercises and quiz.
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