Present Insights to Executives prompts for Data Analyst
Ready-to-use AI prompts for present insights to executives — written for Data Analyst and easy to paste into ChatGPT, Claude or Gemini.
Turn an analysis into an executive summary
You are a analytics director writing for executives who read the first three lines. Analysis:
<analysis>
[analysis]
</analysis>
Audience: [audience]. Decision at stake: [decision]. My recommendation: [recommendation].
Write it top-down: the recommendation and the number that justifies it first, then the three supporting findings each in one sentence with the figure, then the caveats that could change the conclusion, then what happens next and who owns it.
No methodology in the body — put it in an appendix note. Translate every metric into business impact (dollars, customers, hours). Under 300 words. Then give me the single sentence I would say if I had one line in a meeting.
Prepare for the questions I will get
Act as a skeptical executive who has been burned by bad analysis. Here is what I am about to present:
<presentation>
[presentation]
</presentation>
Method: [method]. Audience: [audience].
Ask me the eight hardest questions, in the order they would come up: about the data source, the definition of the metric, what is not in the sample, alternative explanations, why the number differs from [another team's number], the cost of acting on this, and what would happen if we did nothing.
For each, tell me whether I can answer it from what I have, and if not, what I need to prepare. Then name the one weakness in this analysis that I should disclose myself before someone finds it.
Explain a technical result to non-technical stakeholders
You are translating a technical result for people who do not do statistics. Result: [result]. Audience: [audience]. What they need to do with it: [action].
Explain it in plain language with a concrete analogy from their domain, state what it means for their decision, and be explicit about the uncertainty in terms they can act on ('between X and Y, most likely around Z') rather than statistical vocabulary.
Avoid p-values, significance language, and model jargon entirely. Do not oversimplify to the point of being wrong — if the honest answer is 'we do not know yet', say that and explain what would change it. Under 200 words.