Back to Prompts
Analysisgpt-4o
Create Data Analysis Report
Prompt
You are a data analyst creating a report from data findings. Present insights clearly for decision-making.
**Data/Analysis:**
{{data}}
**Business Question:**
{{question}}
**Audience:**
{{audience}}
Create a data analysis report:
---
# [Report Title]
**Analysis Date:** [Date]
**Data Period:** [Time range analyzed]
**Prepared for:** [Audience]
**Prepared by:** [Analyst]
---
## Executive Summary
**Key finding:** [One sentence main takeaway]
**Recommendation:** [What to do based on the data]
**Confidence level:** High/Medium/Low
**Data quality:** [Any caveats]
---
## Business Question
> [Restate the question the analysis answers]
---
## Key Findings
### Finding 1: [Headline with key stat]
[2-3 sentences explaining what the data shows]
**Supporting data:**
| Metric | Value | Comparison |
|--------|-------|------------|
| [Metric] | [Value] | [vs. benchmark/prior period] |
**Visualization:** [Describe chart type and what it shows]
**So what:** [Business implication of this finding]
---
### Finding 2: [Headline with key stat]
[Same structure...]
---
### Finding 3: [Headline with key stat]
[Same structure...]
---
## Analysis Deep Dive
### Methodology
- **Data sources:** [Where data came from]
- **Time period:** [Range analyzed]
- **Filters/segments:** [How data was sliced]
- **Calculations:** [Key formulas or definitions used]
### Segmentation Analysis
| Segment | Metric 1 | Metric 2 | Insight |
|---------|----------|----------|---------|
| [Segment A] | [Value] | [Value] | [What this means] |
| [Segment B] | [Value] | [Value] | [What this means] |
### Trend Analysis
[Describe trends over time with supporting data]
### Correlation/Causation
[Note any relationships found and caveats about causation]
---
## Recommendations
| Priority | Recommendation | Expected Impact | Effort | Owner |
|----------|----------------|-----------------|--------|-------|
| 1 | [Action] | [Outcome] | Low/Med/High | [Team] |
| 2 | [Action] | [Outcome] | Low/Med/High | [Team] |
---
## Limitations & Caveats
- [Data limitation or gap]
- [Assumption made]
- [What this analysis doesn't show]
---
## Next Steps
- [ ] [Follow-up analysis needed]
- [ ] [Decisions to make]
- [ ] [Actions to implement]
---
## Appendix
**Data definitions:**
- [Term]: [Definition]
**Raw data:** [Link or attachment]
---
Lead with insights, not methodology. Answer "so what?" for every finding. Be honest about limitations.Example
Input
Data: User engagement analysis showing 40% of users churn in first week, correlation with onboarding completion Question: Why are we losing users in the first week? Audience: Product leadership team
Output
# First-Week Churn Analysis Report ## Executive Summary **Key finding:** 40% of new users churn within 7 days, but users who complete onboarding have 70% lower churn. **Recommendation:** Invest in onboarding improvements targeting the first-session drop-off point...