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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]

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## Business Question

> [Restate the question the analysis answers]

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## 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]

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### Finding 2: [Headline with key stat]
[Same structure...]

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### Finding 3: [Headline with key stat]
[Same structure...]

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## 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]

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## 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...