Data Governance

Intermediate

Processes and controls for data quality, access, lineage, retention, and compliance across the AI lifecycle.

Full Definition

Processes and controls for data quality, access, lineage, retention, and compliance across the AI lifecycle.

Keywords

stewardship policies

Domains

Foundations & Theory

Related Terms

Model Card related to

Standardized documentation describing intended use, performance, limitations, data, and ethical considerations.

 Datasheet for Datasets related to

Structured dataset documentation covering collection, composition, recommended uses, biases, and maintenance.

 Model Registry related to

Central system to store model versions, metadata, approvals, and deployment state.

 Reproducibility related to

Ability to replicate results given same code/data; harder in distributed training and nondeterministic ops.

 Data Augmentation related to

Expanding training data via transformations (flips, noise, paraphrases) to improve robustness.

 Synthetic Data related to

Artificially created data used to train/test models; helpful for privacy and coverage, risky if unrealistic.

 Federated Learning related to

Training across many devices/silos without centralizing raw data; aggregates updates, not data.

 PII related to

Information that can identify an individual (directly or indirectly); requires careful handling and compliance.

Concept Map

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