Data Governance & Lineage
Catalogues, column-level lineage, retention and deletion, access control and provenance for training corpora.
7concepts
72flashcards
51minutes of reading
- 01 Catalogues, Metadata and Discovery The three kinds of metadata a catalogue holds, why the technical layer is the only one that stays accurate for free, and what makes a catalogue get used rather than abandoned.
- 02 Column-Level Lineage What lineage answers that a dependency graph cannot, why column granularity changes the questions you can ask, and the parsing problem that makes it hard to capture correctly.
- 03 Data Classification and Sensitivity Labelling Why a sensitivity scheme is only as good as how labels propagate through joins, aggregates, embeddings and models, how the high-water-mark rule makes propagation mechanical, and the over-classification and inference failures that quietly turn labels into noise.
- 04 Access Control for Analytical Data Why role-based access breaks down on a data platform, how attribute-based policies and dynamic masking replace it, and the derived-table hole that undermines both.
- 05 Consent and Purpose Limitation for Training Data Why data collected for one purpose cannot simply be reused to train a model under GDPR, how the Article 6(4) compatibility test and the legitimate-interest route work in practice, what EDPB Opinion 28/2024 and the courts have said, and where the law stands as of September 2026.
- 06 Provenance for Training Corpora Why the governance question for training data is harder than for analytical data, what a defensible provenance record contains, and the specific obligations that make it necessary.
- 07 Retention, Deletion and the Right to Erasure Why deleting a row is not deleting data, the specific places copies survive in a modern data platform, and the architectures that make erasure tractable.