Practical policies and controls for governing data used in organizational learning — covering access, classification, labeling, retention, consent, and ethical use.
A practical, role-aware checklist to make event instrumentation, schemas, pipelines, observability, data product APIs, access controls, and validation practices dependable enough to support experiments, dashboards, and continuous learning.
An actionable, fillable checklist to assess dataset readiness for learning and experiments. Covers classification, PII, consent/legal basis, anonymization, permitted uses, retention, DSAR risk, auditability, mitigations, and a lightweight approval workflow.
A practical, action-oriented checklist to evaluate privacy, consent, retention, access, bias, provenance, and operational risks when datasets are repurposed for organizational learning, analytics, or AI. Includes guidance on how to use the checklist, suggested mitigations, and a simple action template for assignment and follow-up.