Playbooks

Practical guides for AI adoption, data platform design, governance, and team enablement.

AI Adoption Playbook

  • Start small, ship fast: Pick a narrow, high-ROI use case with clear guardrails.
  • Map data + risk: Identify PII, compliance boundaries, and acceptable model behaviors.
  • Instrument for truth: Track precision/recall, latency, human-in-the-loop overrides.
  • Document limits: Model cards and clear “do not use for” statements.

Data Platform Blueprint

  • Contract-first: Data contracts with SLAs; versioned schemas and deprecation policy.
  • Lineage + docs: Automated lineage and living documentation tied to deployments.
  • Reliability: Observability on pipelines and datasets; SLOs and on-call runbooks.
  • Self-serve: Modeled marts (dbt), governed access, cost-aware compute.

Governance Guardrails

  • Access control: RBAC/ABAC, just-in-time elevation, and audit logging.
  • PII handling: Tokenization/masking, purpose-bound use, and retention policies.
  • Policy-as-code: Enforce via CI/CD checks and data quality gates.
  • Reviews: Periodic access reviews and model risk assessments.

Team Upskilling

  • Foundations: Git, SQL, Python, data modeling, and code review habits.
  • Modern stack: dbt, Spark, Airflow, containers, and cloud IAM.
  • Responsible AI: Prompting patterns, evals, and human factors.
  • Enablement: Courses + live labs aligned to your platform.