Analytics Delivery & Handoff Template

1) Artifact overview and owner

• Artifact name: (e.g., Daily_Sales_Forecast_v1.2)
• Artifact type: Report / Dashboard / Model / Feature Table / Pipeline
• Version / Tag / Commit:
• Short description: One-sentence business purpose and expected impact.
• Primary owner: name, role, team, email, phone/IM link
• Secondary/backup owner (on-call or team):
• Primary consumers: teams, roles, automated systems
• Dependencies: upstream datasets, APIs, model artifacts, external services (include versions)
• Access requirements: who needs what permissions and where (databases, buckets, dashboards)
• Location / artifacts: repo URLs, storage paths, dashboard links, container image name/tag

2) Inputs and data contracts

List every dataset, API, or feature used. Treat these as explicit contracts.

• Dataset / Source name (e.g., sales.events)
• Owner:
• Source type: streaming / batch / API
• Schema: link to schema doc and sample rows; note required fields and types
• Refresh frequency and expected latency
• SLAs: availability, staleness limits, acceptable null rates
• Access method: SQL, API endpoint, message topic, file path (include credentials/roles needed)
• Data quality checks: null thresholds, value ranges, distribution checks, uniqueness constraints (link to test queries)
• Contract change policy: how schema or semantics changes are communicated and handled

3) Expected outputs and SLAs

• Outputs produced: file/report/table names, formats (parquet/CSV/JSON/BI dashboard), partitions
• Consumers: teams, downstream pipelines, dashboards, alerts
• Delivery frequency and expected availability window
• Performance SLAs: e.g., processing completes within X minutes after data arrival; query response < Y sec
• Accuracy / business acceptance thresholds: e.g., forecast MAE < Z, completeness > 99.5%
• Retention and archival policy for outputs and logs

4) Validation artifacts and test cases

Provide links and summaries for each validation artifact so operations can reproduce tests.

• Unit tests: repo path, test names, pass criteria
• Integration tests: test data, test harness, expected outputs
• End-to-end (acceptance) tests: business-user validation scenarios and pass/fail criteria
• Test data: location of deterministic test fixtures or mocking strategy
• Observed test results: attach last run outputs, timestamps, and responsible tester
• Data quality checks and thresholds: SQL snippets or data-qa jobs to run pre/post-deploy
• Validation gate checklist (must pass before sign-off):
1. All unit tests: pass
2. Integration tests against staging: pass
3. Data quality checks: within thresholds
4. Performance tests: meet SLAs
5. Security & access review: completed

5) Deployment instructions and rollback plan

Provide step-by-step runnable commands, alternate manual steps, required approvals, and a tested rollback procedure.

• Environment targets: staging, pre-prod, prod (include endpoints / cluster/project IDs)
• Required pre-deploy steps: config variables, secrets, migrations, infra changes, maintenance windows
• Deployment steps: exact commands or CI job names with ordering (example):

- Trigger CI pipeline: ci/jobs/deploy-analytic-product (tag: v1.2)
- Run schema migration: db/migrations/2026-08-01-add-col.sql
- Deploy container: k8s apply -f deploy/analytic-product.yaml
- Run smoke tests: scripts/smoke_test.sh

• Canary or phased rollout strategy (if applicable)
• Rollback plan: exact commands, how to restore previous data artifacts, who approves rollback, maximum allowable rollback time
• Post-deploy validation: list of smoke checks, sample queries, dashboards to confirm
• Estimated downtime and user-notice process (if any)

6) Monitoring & alerts to be installed

Define metrics, thresholds, alert routing, and the operations runbook for each alert.

• Key operational metrics (with definitions):
  • Pipeline run success rate (last 24h)
  • Data freshness lag (minutes)
  • Output row counts and expected ranges
  • Model latency / inference time
  • Model health indicators (drift, prediction distribution)
  • Consumer error rate (if downstream APIs)
• Alert rules and thresholds (example):
  • Data freshness > 60 minutes -> SEV-2 page/team
  • Pipeline failure count > 3 in 1h -> SEV-1 on-call
  • Output null rate > 5% -> data team, include sample failing queries
• Dashboard links for monitoring and runbook pages
• Alert routing & escalation: who is paged first, backup, SLA to acknowledge and resolve
• Runbook for common alerts: step-by-step triage (investigate logs, re-run job, verify data, rollback if necessary)
• Logging & observability: where logs are retained, correlation IDs, trace links

7) Training, documentation and contact list

• Training materials: slide deck, short video, walkthrough notebook, scheduled sessions
• Runbooks & how-tos: links to operational runbooks and FAQs
• Knowledge base & glossary: define key terms used by this artifact
• Contacts & escalation matrix (example format):
  • Primary owner: Name — Role — email — phone — slack
  • Secondary owner/on-call: Name — Role — contact
  • Data platform on-call: Team/rotation link
  • Business contact for acceptance: Name — Role

8) Acceptance checklist and sign-off

Fill each item and capture sign-off. No production rollout until required sign-offs are present.

• All validation gates passed (unit/integration/acceptance)
• Data contracts approved by owners
• Deployment and rollback instructions validated by ops
• Monitoring & alerts configured and tested
• Training materials published and key consumers notified
• Security review and access control checks completed
• Compliance or privacy review (if relevant) completed
• Business owner acceptance test completed

Sign-off template (populate names, roles, dates):

• Developer / Data Lead: __________________ Date: ______
• Data Owner: __________________ Date: ______
• Operations / SRE: __________________ Date: ______
• Business Owner: __________________ Date: ______

Annex A — Example artifacts & snippets (copy and adapt)

Include links to real examples so reviewers can reproduce tests quickly.

• Sample validation SQL checks (point to repo)
• Example alert definition (prometheus/alertmanager or cloud metric rule)
• Smoke test script example
• Rollback script example
• Sample API contract (OpenAPI or proto) if applicable

Annex B — Common mistakes to avoid

• Assuming schema or semantics won't change — include explicit contract and change policy.
• Skipping small-sample or edge-case tests — include tests for empty partitions, nulls, date boundaries.
• Not specifying monitoring thresholds — define them and test the alerts before go-live.
• Lack of a clear rollback plan — practice it in staging at least once.
• Delivering outputs without consumer-ready documentation — provide examples and explanation of fields.

Suggested post-handoff activities

• Run a 1-week observation window after production deployment and record incidents and mitigations.
• Schedule a retrospective with developers, ops, and consumers to capture improvements to the template or process.
• Version the handoff document and attach it to the release artifact for traceability.

Quick fill checklist (one-page)

1. Artifact name, owner, and version
2. Upstream data contracts signed off
3. Validation gates passed (unit/integration/acceptance)
4. Deployment steps and rollback verified
5. Monitoring and alerts configured
6. Training & docs published to consumers
7. All required sign-offs captured