Dataset Summary and Insights
Paste or describe a dataset and get an instant summary of key statistics, patterns, anomalies, and actionable insights.
Prompt Template
You are a senior data analyst. Analyze the following dataset and provide a comprehensive summary: Dataset description: [what the data is about] Source: [where it came from] Time period: [date range] Key columns: [list column names and types] Business question: [what we're trying to answer] [PASTE SAMPLE DATA OR DESCRIBE IT HERE] Provide: 1. Dataset overview (rows, columns, data quality notes) 2. Key statistics for numeric columns (mean, median, range, outliers) 3. Top 5 findings / patterns 4. Anomalies or data quality issues 5. 3 actionable recommendations based on the data 6. Suggested next analyses to run
Example Output
Dataset Analysis: E-commerce Sales Q1 2026
**Overview:** 12,450 rows × 8 columns. Data appears clean with 0.3% null values in 'discount_applied' column (acceptable).
Key Statistics
| Metric | Value |
|--------|-------|
| Total Revenue | $2.4M |
| Avg Order Value | $192 |
| Median Order Value | $134 (right-skewed — a few large orders pull mean up) |
| Highest Revenue Category | Electronics (38%) |
| Return Rate | 11.2% |
Top 5 Findings
1. **Weekend orders have 23% higher AOV** than weekday orders
2. **February dip:** Revenue dropped 31% in week 2 — correlates with shipping delay incident
3. **Mobile conversion** (2.1%) is half of desktop (4.3%) — UX opportunity
4. **Repeat customers** (28% of base) generate 61% of revenue
5. **Returns highest in Clothing** (18.4%) — sizing issue likely
Recommendations
1. Investigate and fix mobile checkout friction (potential +$240K/quarter)
2. Launch retention campaign targeting the 72% of customers who've only ordered once
3. Review clothing size guides and add size charts to reduce returns
Tips for Best Results
- 💡Paste a 10-20 row sample with column headers — even a small sample enables much better analysis than a description alone
- 💡Always state the business question upfront — the same data yields different insights depending on what you're trying to decide
- 💡Ask for a 'data quality scorecard' separately if your dataset is large — issues like nulls and duplicates need their own audit
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