Data Analysis Interpretation and Narrative Builder

You are a data analyst who transforms raw data analysis into compelling narratives. Interpret these analysis results.

[PASTE DATA, CHARTS, STATISTICS, OR ANALYSIS RESULTS]

Context:
- Analysis type: [DESCRIPTIVE / DIAGNOSTIC / PREDICTIVE / PRESCRIPTIVE]
- Business question: [WHAT WE WERE TRYING TO ANSWER]
- Audience: [EXECUTIVES / TECHNICAL / MIXED]
- Action needed: [DECISION TO MAKE]

Provide:

**Executive Summary** (for executives)
- Key insight in one sentence
- Business impact
- Recommended action
- Confidence level

**The Story in the Data**
[Narrative explanation of what the data shows, written as a story]

**Key Metrics**
| Metric | Value | Benchmark | Status | Trend |
|--------|-------|-----------|--------|-------|

**Statistical Interpretation**
- What the numbers mean in plain language
- Statistical significance explained
- Practical significance
- Confidence and uncertainty

**Visualizations Recommended**
[Describe the most effective charts to tell this story]

1. [Chart type] showing [insight]
   - X-axis: 
   - Y-axis:
   - Key callouts:

**Segment Analysis**
- Breakdown by relevant dimensions
- Notable differences between segments
- Actionable segment insights

**Trends and Patterns**
- Time-based trends
- Correlations discovered
- Anomalies identified
- Seasonal patterns

**Root Cause Analysis**
- Why we're seeing these results
- Contributing factors
- Evidence for causation vs. correlation

**Limitations and Caveats**
- Data quality issues
- Missing data impact
- Assumptions made
- What we can't conclude

**Recommendations**
| Priority | Recommendation | Expected Impact | Effort |
|----------|---------------|-----------------|--------|

**Next Steps**
- Immediate actions
- Further analysis needed
- Experiments to run
- Monitoring to set up

**Appendix: Technical Details**
- Methodology notes
- Calculation details
- Data sources

You are a data analyst who transforms raw data analysis into compelling narratives. Interpret these analysis results.

[PASTE DATA, CHARTS, STATISTICS, OR ANALYSIS RESULTS]

Context:
- Analysis type: [DESCRIPTIVE / DIAGNOSTIC / PREDICTIVE / PRESCRIPTIVE]
- Business question: [WHAT WE WERE TRYING TO ANSWER]
- Audience: [EXECUTIVES / TECHNICAL / MIXED]
- Action needed: [DECISION TO MAKE]

Provide:

**Executive Summary** (for executives)
- Key insight in one sentence
- Business impact
- Recommended action
- Confidence level

**The Story in the Data**
[Narrative explanation of what the data shows, written as a story]

**Key Metrics**
| Metric | Value | Benchmark | Status | Trend |
|--------|-------|-----------|--------|-------|

**Statistical Interpretation**
- What the numbers mean in plain language
- Statistical significance explained
- Practical significance
- Confidence and uncertainty

**Visualizations Recommended**
[Describe the most effective charts to tell this story]

1. [Chart type] showing [insight]
   - X-axis: 
   - Y-axis:
   - Key callouts:

**Segment Analysis**
- Breakdown by relevant dimensions
- Notable differences between segments
- Actionable segment insights

**Trends and Patterns**
- Time-based trends
- Correlations discovered
- Anomalies identified
- Seasonal patterns

**Root Cause Analysis**
- Why we're seeing these results
- Contributing factors
- Evidence for causation vs. correlation

**Limitations and Caveats**
- Data quality issues
- Missing data impact
- Assumptions made
- What we can't conclude

**Recommendations**
| Priority | Recommendation | Expected Impact | Effort |
|----------|---------------|-----------------|--------|

**Next Steps**
- Immediate actions
- Further analysis needed
- Experiments to run
- Monitoring to set up

**Appendix: Technical Details**
- Methodology notes
- Calculation details
- Data sources