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.
A practical, fillable spec template to define data product purpose, consumers, metric and event definitions, data quality checks, SLAs, ownership, privacy and retention rules, instrumentation verification steps, test dataset guidance, and rollout & monitoring requirements for learning-oriented measurement and experimentation.
A targeted playbook explaining governance controls tailored to analytics and experimentation use cases, balancing access, privacy, and speed.
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, ready-to-adapt toolkit of policy templates and guidance to govern taxonomy, content lifecycle, learning-data access, retention, and vendor risk — plus clear steps for tailoring, ownership, and review so teams use governance instead of being blocked by it.
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.