@ethan-riveradata-analytics-executive-decision-memoTexto únicoPúblicoActualizado el 14 jun 2026

Data Analytics prompt that creates an executive memo that makes tradeoffs explicit and returns one-page recommendation, options table, risks, and next actions.

43Star0Fork66Copia

Prompt

Vista previa

Abrir en ChatGPT

# 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
Create an executive memo that makes tradeoffs explicit 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. Lead with the decision
2. State assumptions and confidence
3. Compare options on the same criteria
4. End with named owners and dates

# Required output sections
1. Decision needed
2. Recommendation
3. Options
4. Evidence
5. Risks
6. Next actions

# 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.

Artefactos

1 artefacto(s)

Example Output: Data Analytics Executive Decision Memo

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

one-page recommendation, options table, risks, and next actions

Decision needed

The immediate decision is whether a retention dashboard for a usage-based SaaS product is mature enough for a controlled pilot. The strongest evidence should come from event taxonomy and warehouse schema; if either source is missing, mark the recommendation as provisional rather than filling the gap with assumptions.

Recommendation

Recommendation: run a narrow pilot before broad rollout. Prefer a governance-forward pilot if evidence suggests aggregation traps; prefer a speed-forward pilot only when warehouse schema and dashboard screenshots are already reliable.

Options

Option A optimizes speed by shipping a limited workflow around dashboard screenshots. Option B optimizes control by adding reviewer sign-off and rollback steps. Option C waits until evidence from stakeholder questions is stronger. Use the same success metric for all three options.

Evidence

Evidence to trust: stakeholder questions, event taxonomy, and reviewer notes from semantic layer. Evidence to treat cautiously: anecdotes that are not tied to a time window, cohort, or source owner.

Risks

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.

Next actions

Next actions: validate warehouse schema, assign a reviewer for aggregation traps, and schedule a decision checkpoint after the first pilot cohort. Do not expand scope until the review path works in practice.

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: Decision needed, Recommendation, Options, Evidence, Risks, Next actions.
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: Executive Decision Memo

Use this prompt when you need one-page recommendation, options table, risks, and next actions 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.