Data Infrastructure for Learning — Practical Checklist

How to use this checklist

This checklist helps teams prepare data infrastructure that reliably supports experiments, dashboards, and learning loops. Use it as a conversation guide between product, analytics, data engineering, and site owners. Mark each item with an owner, status (Not started / In progress / Done), and brief evidence (link to PR, query, dashboard, runbook, or ticket).

1) Measurement plan alignment with learning questions

Goal: Every metric traces back to a learning question or decision. Avoid building indicators without a clear use.

• Define 1–3 learning questions or decisions the data will inform (owner: Product/Research).
• For each question, list the primary indicator(s), secondary signals, and proposed experiments or actions.
• Document how each indicator affects decisions (e.g., stop, iterate, scale) and the cadence for review.
• Capture stakeholder consumers and required SLAs for freshness and completeness.
• Acceptance criteria: Measurement plan stored in a shared location and reviewed by at least one downstream consumer.

2) Event and schema checklist (naming, versioning, owner)

Goal: Events and schemas are discoverable, consistent, and safely change-managed.

• Have a canonical event taxonomy documented (naming conventions, namespaces, and examples).
• Every event/schema has a documented owner and contact information.
• Use semantic, stable names (avoid business jargon that will change); include versioning in schema metadata.
• Change process defined: backward-compatible changes allowed automatically; breaking changes require deprecation schedule and migration plan.
• Schema registry or catalog contains examples, field descriptions, types, required vs optional flags, and sample events.
• Acceptance criteria: Schema registry entries exist for critical events and contain owner, version, and sample payloads.

3) Pipeline observability and SLAs

Goal: Data pipelines are monitored so consumers can trust timeliness and completeness.

• Define SLAs for freshness and delivery for each critical dataset (e.g., 5 min for near-real-time, 24 hours for daily batches).
• Monitor key pipeline metrics: input throughput, processing lag, error rates, backfill status, and schema drift.
• Create alert thresholds and on-call runbooks for common failure modes (retries, partial reprocess, manual replay).
• Record data lineage so consumers can trace how a metric is generated from source events.
• Acceptance criteria: Dashboards for pipeline health exist and alerting is tested at least once per quarter.

4) Data product interface and documentation

Goal: Data products (tables, views, APIs, dashboards) are accessible with clear contracts and usage examples.

• Define the consumer-facing contract: canonical name, description, fields, units, expected cardinality, and update cadence.
• Provide sample queries and code snippets for common consumer tasks (e.g., join patterns, filter examples, sampling.)
• Document limitations, approximations, and known edge cases (e.g., late-arriving events, dedup rules).
• Assign an owner responsible for compatibility and a deprecation policy for changing the contract.
• Acceptance criteria: Each published data product has a README and example queries linked from the data catalog.

5) Access and security basics for learning use cases

Goal: Consumers can access data for analysis safely and quickly while protecting sensitive information.

• Define roles and permissions for data access (who can read, query, export, or modify products).
• Classify data sensitivity and apply masking, aggregation, or access controls where required.
• Provide a lightweight self-service access request flow and tracking for approvals.
• Log access for audit and troubleshooting (who ran what queries and when) when policy requires it.
• Acceptance criteria: Access roles are documented and at least one sample masked dataset is available for learning use cases.

6) Example validation queries and rollout checklist

Goal: Provide reproducible validation checks and a safe rollout plan for new instrumentation or schema changes.

• Example validation queries to check coverage and correctness (adapt to your stack):
  • Basic sanity: SELECT COUNT(*) FROM events WHERE event_time >= DATE_SUB(current_date, INTERVAL 1 DAY);
  • Schema presence: SELECT COUNT(*) FROM events WHERE payload->'$.required_field' IS NULL;
  • Uniqueness: SELECT id, COUNT(*) FROM events GROUP BY id HAVING COUNT(*) > 1;
  • Drift check: compare daily distribution of key categorical fields over last 7 days.
• Rollout checklist for instrumentation or schema changes:
  1. Implement change in a feature branch with automated tests (schema validation, nullability checks).
  2. Smoke test in staging with sample traffic and run validation queries.
  3. Deploy to Canary (small percentage of traffic) and monitor pipeline health and metrics for 24–72 hrs.
  4. Gradually increase traffic; run full validation queries and record results in the change ticket.
  5. Post-release: run end-to-end tests of dashboards and experiment metrics to confirm no regression.
• Acceptance criteria: Change ticket contains validation query outputs, owner sign-off, and rollback plan.

Additional guidance and common pitfalls

• Favor simpler metrics that are easier to validate over complex black-box scores early on.
• Avoid aggregating without preserving raw event context—aggregates should be reproducible from raw data.
• Beware vanity metrics: ask "who will act on this?" before committing to it.
• Keep an evolving "data playbook" that captures patterns, gotchas, and lessons from incidents.

Owner and next steps

Suggested owners: Product or Research (measurement plan); Data Engineer (pipelines, SLAs); Analytics Engineer (data product contracts); Security/Compliance (access & masking). Consider turning this checklist into an interactive form so teams can save evidence and track progress.