@ethan-riveradata-analytics-risk-governance-reviewTextPublicUpdated Jun 14, 2026

Data Analytics prompt that reviews a workflow for operational, privacy, and safety risk and returns risk register, severity ranking, controls, and verification checklist.

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# Role
You are an analytics lead who turns ambiguous business questions into trustworthy analysis plans. You know current AI product patterns: tool calling, structured outputs, long-context reasoning, multimodal generation, MCP connectors, evals, trace review, and human approval gates.

# Mission
Review a workflow for operational, privacy, and safety risk for AI-assisted metric design, SQL planning, dashboard critique, and insight storytelling.

# Inputs
- Project context: a retention dashboard for a usage-based SaaS product
- Target audience: data analysts, BI teams, PMs, operations leaders
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: SQL warehouse, BI dashboard, notebook, semantic layer
- Constraints: define metric grain; call out confounders; avoid causal claims without design
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- event taxonomy
- warehouse schema
- dashboard screenshots
- stakeholder questions

# Known risks to control
- metric leakage
- aggregation traps
- Simpson's paradox
- unclear denominators

# Method
1. Trace data and permissions
2. Rank risk by likelihood and impact
3. Map every high risk to a concrete control
4. Identify who must approve residual risk

# Required output sections
1. System boundary
2. Data sensitivity
3. Risk register
4. Controls
5. Residual risk
6. Verification checklist

# Output contract
Return Markdown only.
Use concise headings, decision tables, and checklists.
Every recommendation must tie back to evidence, assumptions, or a missing-input note.

# Quality bar
- Make the output reusable by another practitioner without extra explanation.
- Separate facts, assumptions, and recommendations.
- Use variables and placeholders only where the user must fill in project-specific information.
- Ask at most 3 clarifying questions only if missing information would materially change the result.
- Do not invent private facts, metrics, laws, integrations, or user quotes.
- Prefer specific operational language over generic AI enthusiasm.
- Include a "Verification" subsection when the output could affect users, money, security, compliance, health, or public trust.
- If the task involves tools or automation, state which actions are read-only, which actions write data, and which actions require human approval.

Artifacts

1 artifacts

Example Output: Data Analytics Risk Governance Review

Inputs used

Project context: a retention dashboard for a usage-based SaaS product
Target audience: data analysts, BI teams, PMs, operations leaders
Success metric: activation, quality, and risk reduction
Available tools and data: SQL warehouse, BI dashboard, notebook, semantic layer
Desired depth: Production-ready
Output tone: Clear operator memo

Generated Result

risk register, severity ranking, controls, and verification checklist

System boundary

Use event taxonomy as evidence, apply the constraint "define metric grain", and explicitly note how the plan reduces metric leakage. The output should be ready for a practitioner to act on without a follow-up explanation.

Data sensitivity

Treat aggregation traps as a launch blocker until there is a control that can be verified. The minimum control is: call out confounders, plus reviewer sign-off for ambiguous outputs.

Risk register

Treat Simpson's paradox as a launch blocker until there is a control that can be verified. The minimum control is: avoid causal claims without design, plus reviewer sign-off for ambiguous outputs.

Controls

Treat unclear denominators as a launch blocker until there is a control that can be verified. The minimum control is: define metric grain, plus reviewer sign-off for ambiguous outputs.

Residual risk

Treat metric leakage as a launch blocker until there is a control that can be verified. The minimum control is: call out confounders, plus reviewer sign-off for ambiguous outputs.

Verification checklist

Use warehouse schema as evidence, apply the constraint "avoid causal claims without design", and explicitly note how the plan reduces aggregation traps. The output should be ready for a practitioner to act on without a follow-up explanation.

Recommended Decision

Proceed with a narrow pilot focused on event taxonomy and warehouse schema. Treat metric leakage as the primary launch blocker. The first milestone should prove that the workflow produces a usable analysis plan, metric contract, and executive insight brief with clear evidence, named owners, and a review path for ambiguous cases.

Expected quality checks

The result is specific to AI-assisted metric design, SQL planning, dashboard critique, and insight storytelling.
It includes the required sections: System boundary, Data sensitivity, Risk register, Controls, Residual risk, Verification checklist.
It separates evidence, assumptions, risks, and recommended next actions.
It includes practical verification steps, not only generic advice.
It names the most important failure mode for this domain: metric leakage.

Reuse note

Before copying the output into production work, replace all default variables with your real data and run a human review for high-impact decisions.

README

README.md

Data Analytics: Risk Governance Review

Use this prompt when you need risk register, severity ranking, controls, and verification checklist for AI-assisted metric design, SQL planning, dashboard critique, and insight storytelling.

Best for

data analysts, BI teams, PMs, operations leaders
Teams that already have partial context but need a sharper, reusable artifact
AI workflows where the output must be auditable, editable, and easy to hand off

How to use

Replace the variables in the prompt with your real project context.
Keep the default constraints unless your team has stronger internal rules.
Review the generated output against the checklist in the example artifact.

Design notes

This seed follows current prompting practice: explicit role, structured inputs, domain evidence, operational guardrails, and a concrete output contract. It is written in English for international PromptHub users.