Analytics Governance RACI & Policy Starter Template

Purpose and how to use this template

This starter template helps teams clarify who does what across analytics activities so data can be used responsibly and at scale. Use it as a living document: copy it into your site or team domain, adapt role names to your organization, and record local owners, review cadences, and contact points.

Quick guidance

Before customizing the matrix and policies, gather representatives from analytics, product, legal/privacy, IT/security, and an executive sponsor. Agree on a simple escalation and enforcement approach so responsibilities don’t become “paper” only.

Example RACI matrix (tasks vs. roles)

Replace example role names with your organization’s titles. Mark R = Responsible, A = Accountable, C = Consulted, I = Informed.

Tip: Keep the matrix small and focused on recurring operational decisions. For one-off or unusual choices, use a short decision record linked from the matrix.

Copy-ready policy snippets

Access provisioning (short)

Policy: Access to analytics datasets must be requested through the standard access request process and approved by the dataset Data Steward and IT Security. Access levels (view, query, extract) will be granted based on role-based justification and least privilege. Temporary access must include an expiry date. Requests without a documented business justification will be denied.

Data retention and deletion (short)

Policy: Data retention periods are set at the dataset level and documented in the dataset metadata. Retention decisions must balance business value, regulatory requirements, and privacy risk. Data past its retention date will be deleted or anonymized unless a Data Owner requests and documents an exception with a fixed review date.

Model approval and production readiness (short)

Policy: Models intended for production use must pass a formal approval checklist including reproducibility of training, documented performance metrics vs agreed baselines, bias and fairness reviews where applicable, monitoring plan, rollback plan, and an assigned Model Owner. The Governance Board (or delegated committee) signs off on high-risk models.

Data contracts (skeleton)

Data Contract: [SOURCE SYSTEM] -> [ANALYTICS SYSTEM]
- Owner: [NAME / ROLE]
- Source description: [what data, cadence, expected format]
- SLAs: [latency, freshness, schema stability]
- Quality expectations: [acceptable null rates, accuracy checks]
- Security: [encryption, transport requirements]
- Privacy constraints: [PII handling, consent limits]
- Change process: [pipeline/schema change notifications]
- Review cadence: [e.g., quarterly]
  

Implementation checklist

1. Customize role names and populate the RACI matrix with live contacts and escalation points.
2. Publish dataset-level metadata: owner, steward, retention, sensitivity label, contact info.
3. Adopt the short policies above; link to operational procedures (access request form, exception template).
4. Create a simple model approval checklist and assign a Model Owner for each production model.
5. Schedule initial training for stewards, owners, and analysts (one-hour practical session).
6. Implement an audit cadence (e.g., quarterly checks of high-risk datasets and models).
7. Measure adoption: % of critical datasets with owners, % of production models with approval records.

Enforcement and monitoring steps

• Log and review access provisioning events monthly; flag anomalies to IT Security and the Data Steward.
• Run automated data-quality checks daily for critical feeds; report exceptions to the steward with SLA targets.
• Require documented exceptions (with expiration) for any policy deviation; route exceptions to Governance Board for recurring issues.
• Maintain a lightweight evidence repository: approval records, change logs, and model performance dashboards.

Communication plan for role or policy changes

When a role assignment or policy changes:

1. Notify affected teams via email and add an entry to the team knowledge base with effective date and contact points.
2. Update dataset metadata and the RACI matrix in the shared domain.
3. Hold a 30-minute review session with stewards and frequent consumers to explain practical impacts.
4. Record the change in the governance change log and schedule a follow-up check after one cycle (e.g., 30–90 days).

Practical examples and pitfalls

Example: If the Data Owner changes but metadata isn’t updated, access requests stall. Fix: require metadata update as part of any ownership handoff checklist.

Common pitfall: Over-centralizing approvals for low-risk datasets. Fix: adopt risk tiers (high, medium, low) and simplify approvals for low-risk items to avoid blocking teams.

Next steps and customization suggestions

• Make this template part of your domain kit so teams can copy and adapt it locally.
• Consider adding a short interactive request form and an approvals audit that stores submissions (see Capability notes below).
• Track simple KPIs: % datasets with owners, mean time to fulfill access request, number of approved models with monitoring in place.

Appendix: Short glossary

Data Owner

Business role accountable for data meaning and business use.

Data Steward

Operational role responsible for day-to-day quality, metadata, and access requests.

Model Owner

Person accountable for a model in production, including monitoring and rollback.