| name | analyze-insights |
| compatibility | Requires Altertable MCP server |
| description | Interprets Insight data to identify patterns, anomalies, and trends. Use when analyzing visualizations, extracting findings, or explaining patterns in graphs. |
| metadata | {"author":"altertable-ai","requires":"altertable-mcp"} |
Analyzing Insights
Quick Start
When analyzing an insight:
- Call
view_insight for saved insights, or use provided chart data directly
- Identify the chart type and what it measures
- Look for patterns (trends, seasonality, anomalies)
- Quantify observations with specific numbers
- Provide actionable interpretation
When to Use This Skill
- User asks "what does this insight show?"
- Analyzing visualization results
- Identifying trends or anomalies
- Explaining patterns in data
- Generating insights from visual data
Chart Types
| Type | Best For | Look For |
|---|
| Line | Time series trends | Direction, inflection points |
| Bar | Category comparison | Relative sizes, outliers |
| Area | Volume over time | Growth, composition |
| Pie | Distribution | Proportions, dominance |
| Table | Detailed data | Patterns, sorting |
| Metric | Single values | Change from baseline |
| BarList | Ranked items | Top performers, long tail |
Analysis Framework
1. Describe What You See
Start with objective observations:
- What is being measured?
- What is the time range?
- What are the key dimensions?
2. Identify Patterns
Look for:
- Trends: Upward, downward, flat
- Seasonality: Weekly, monthly, yearly cycles
- Anomalies: Spikes, drops, outliers
- Inflection points: Where direction changes
3. Quantify Observations
Always include numbers:
- Absolute values
- Percentage changes
- Comparisons to baselines
4. Provide Interpretation
Explain significance:
- Why might this be happening?
- What are the implications?
- What actions should be considered?
Pattern Recognition
Trend Patterns
Upward Trend
- Consistent growth over time
- Look for: slope, acceleration/deceleration
- Note: sustainability, growth rate
Downward Trend
- Consistent decline over time
- Look for: rate of decline, stabilization
- Note: severity, projected impact
Flat/Stable
- No significant change
- Look for: volatility within range
- Note: whether stability is expected
Seasonality Patterns
Weekly Cycles
- Weekday vs weekend differences
- Monday dips, Friday spikes
- Note: business day patterns
Monthly Cycles
- Beginning/end of month patterns
- Billing cycles, payroll effects
- Note: calendar effects
Yearly Cycles
- Holiday impacts
- Seasonal business patterns
- Note: YoY comparisons
Anomaly Patterns
Spikes
- Sudden increase
- Look for: magnitude, duration
- Consider: campaigns, events, bugs
Drops
- Sudden decrease
- Look for: recovery pattern
- Consider: outages, issues, seasonality
Outliers
- Values far from normal range
- Look for: explanation
- Consider: data quality, real events
Analysis by Chart Type
Line Charts
Focus on:
- Overall trend direction
- Volatility/smoothness
- Inflection points
- Comparisons between lines
Questions to answer:
- Is the metric growing or declining?
- Are there regular patterns?
- Where are the peaks and troughs?
Bar Charts
Focus on:
- Relative bar heights
- Ordering (if applicable)
- Gaps between categories
- Outlier categories
Questions to answer:
- Which category leads/lags?
- Is distribution expected?
- Are there surprising values?
Pie Charts
Focus on:
- Dominant segments
- Small segments
- Unexpected proportions
Questions to answer:
- Is any segment too dominant?
- Are proportions as expected?
- Has composition changed?
Tables
Focus on:
- Sorting patterns
- Extreme values
- Null/missing data
- Relationships between columns
Questions to answer:
- What patterns emerge?
- Are there data quality issues?
- What correlations exist?
Quantification Guidelines
Describing Changes
| Change | Description |
|---|
| +/-5% | Slight change |
| +/-10-20% | Moderate change |
| +/-20-50% | Significant change |
| +/-50%+ | Dramatic change |
| 2x | Doubled |
| 3x | Tripled |
Time Comparisons
- WoW: Week-over-week
- MoM: Month-over-month
- QoQ: Quarter-over-quarter
- YoY: Year-over-year
Statistical Context
- Compare to historical average
- Note standard deviation if known
- Reference typical ranges
Communication Patterns
Good Insight Format
[What]: Revenue increased 23% this week
[Context]: From $45,000 to $55,350
[Comparison]: This is 15% above the 4-week average
[Interpretation]: Likely driven by the holiday promotion
[Recommendation]: Consider extending the campaign
Avoid Vague Statements
| Bad | Good |
|---|
| "Revenue went up" | "Revenue increased 23% to $55,350" |
| "There's a trend" | "Daily active users grew 5% WoW for 6 consecutive weeks" |
| "Something changed" | "Conversion dropped from 3.2% to 2.1% on March 15" |
Common Pitfalls
- Making claims without numbers
- Ignoring context (seasonality, events)
- Confusing correlation with causation
- Over-interpreting normal variance
- Missing obvious anomalies
- Not considering data quality issues
Reference Files
