visualization-choice-reporting

What this skill does

# Visualization Choice & Reporting

## Workflow

Copy this checklist and track your progress:

```
Visualization Choice & Reporting Progress:
- [ ] Step 1: Clarify question and profile data
- [ ] Step 2: Select visualization type
- [ ] Step 3: Design effective chart
- [ ] Step 4: Narrate insights and actions
- [ ] Step 5: Validate and deliver
```

**Step 1: Clarify question and profile data**

Define the question you're answering (What's the trend? How do X and Y compare? What's the distribution? What drives Z? What's the composition?). Profile your data: type (categorical, numerical, temporal, geospatial), granularity (daily, user-level, aggregated), size (10 rows, 10K, 10M), dimensions (1D, 2D, multivariate). See [Question-Data Profiling](#question-data-profiling).

**Step 2: Select visualization type**

Match question type to chart family using [Chart Selection Guide](#chart-selection-guide). Consider data size (small → tables, medium → standard charts, large → heatmaps/binned), number of series (1-3 → standard, 4-10 → small multiples, 10+ → interactive/aggregated), and audience expertise (executives → simple with insights, analysts → detailed exploration).

**Step 3: Design effective chart**

For simple cases → Apply [Design Checklist](#design-checklist) (clear title, labeled axes, legend if needed, annotations, accessible colors). For complex cases (multivariate, dashboards, interactive) → Study [resources/methodology.md](resources/methodology.md) for advanced techniques (small multiples, layered charts, dashboard layout, interaction patterns).

**Step 4: Narrate insights and actions**

Lead with insight headline ("Revenue up 30% YoY driven by Enterprise segment"), annotate key patterns (arrows, labels, shading), provide context (vs benchmark, target, previous), interpret meaning ("Suggests product-market fit in Enterprise"), recommend actions ("Double down on Enterprise sales hiring"). See [Narrative Framework](#narrative-framework).

**Step 5: Validate and deliver**

Self-assess using [resources/evaluators/rubric_visualization_choice_reporting.json](resources/evaluators/rubric_visualization_choice_reporting.json). Check: Does chart answer the question clearly? Are insights obvious at a glance? Are next actions clear? Create `visualization-choice-reporting.md` with question, data summary, visualization spec, narrative, and actions. See [Delivery Format](#delivery-format).

---

## Question-Data Profiling

**Question Types → Chart Families**

| Question Type | Example | Primary Chart Families |
|---------------|---------|------------------------|
| **Trend** | How has X changed over time? | Line, area, sparkline, horizon |
| **Comparison** | How do categories compare? | Bar (horizontal for names), column, dot plot, slope chart |
| **Distribution** | What's the spread/frequency? | Histogram, box plot, violin, density plot |
| **Relationship** | How do X and Y relate? | Scatter, bubble, connected scatter, hexbin |
| **Composition** | What are the parts? | Treemap, pie/donut, stacked bar, waterfall, sankey |
| **Geographic** | Where is it happening? | Choropleth, bubble map, flow map, dot map |
| **Hierarchical** | What's the structure? | Tree, dendrogram, sunburst, circle packing |
| **Multivariate** | How do many variables interact? | Small multiples, parallel coordinates, heatmap, SPLOM |

**Data Type → Encoding Considerations**

- **Categorical** (product, region, status): Use position, color hue, shape. Bar length better than pie angle for accuracy.
- **Numerical** (revenue, count, score): Use position, length, size. Prefer linear scales; use log only when spanning orders of magnitude.
- **Temporal** (date, timestamp): Always use consistent intervals. Annotate events. Show seasonality if relevant.
- **Geospatial** (lat/lon, region): Use maps for absolute location; use tables/charts if geography not central to insight.

---

## Chart Selection Guide

| Question Type | Chart Types | When to Use |
|---------------|-------------|-------------|
| **Comparison** | Bar (horizontal), Column, Grouped bar, Dot plot, Slope chart | Categorical → Numerical. Horizontal bar for long names/ranking. Grouped for 2-3 metrics. Slope for before/after. |
| **Trend** | Line, Area, Sparkline, Step, Candlestick | Time → Numerical. Line for continuous trends. Area for cumulative/part-to-whole. Sparkline for inline. Step for discrete changes. |
| **Distribution** | Histogram, Box plot, Violin, Density plot | Numerical → Frequency. Histogram for shape/outliers. Box for quartiles across groups. Violin for full density. |
| **Relationship** | Scatter, Bubble, Hexbin, Connected scatter | Numerical X → Numerical Y. Scatter for correlation. Bubble for 3rd/4th variable (size/color). Hexbin for dense data. |
| **Composition** | Treemap, Pie/Donut, Stacked bar (100%), Waterfall, Sankey | Parts of whole. Treemap for hierarchy. Pie for 2-5 categories (part-to-whole key). Waterfall for cumulative. Sankey for flow. |
| **Geographic** | Choropleth, Bubble map, Flow map | Spatial patterns. Choropleth for regions. Bubble for precise locations. Flow for origin-destination. |
| **Multivariate** | Small multiples, Heatmap, Parallel coordinates | Many variables. Small multiples for consistent comparison. Heatmap for matrix (time×day). Parallel for dimensions. |

---

## Design Checklist

**Essential Elements**

- [ ] **Insight headline title:** Not "Revenue by Month" but "Revenue Up 30% YoY, Driven by Enterprise"
- [ ] **Clear axis labels with units:** "Revenue ($M)", "Month (2024)", not just "Revenue", "Date"
- [ ] **Legend if multiple series:** Position near chart, use direct labels on lines when possible
- [ ] **Annotations for key points:** Arrows, labels, shading for important events/patterns
- [ ] **Source and timestamp:** "Source: Analytics DB, as of 2024-11-14" builds trust

**Perceptual Best Practices**

- [ ] **Start Y-axis at zero for bar/column charts** (to avoid exaggerating differences)
- [ ] **Use position over angle/area** (bar > pie for accuracy, scatter > bubble when size isn't critical)
- [ ] **Colorblind-safe palette:** Avoid red-green only; use blue-orange or add patterns
- [ ] **Limit colors to 5-7 distinct hues** (more requires legend lookup, slows comprehension)
- [ ] **Use pre-attentive attributes** (color, size, position) to highlight signal, not decoration

**Declutter**

- [ ] **Remove chart junk:** No 3D, no gradients, no heavy gridlines, no background images
- [ ] **Mute non-data ink:** Light gray gridlines, thin axes, subtle colors for reference lines
- [ ] **Use white space:** Don't cram; let data breathe

**Accessibility**

- [ ] **Alt text describing insight:** "Line chart showing revenue grew from $2M to $2.6M (30% increase) from Q1 to Q4 2024, with Enterprise segment contributing 80% of growth."
- [ ] **Sufficient contrast:** Text readable, lines distinguishable
- [ ] **Patterns in addition to color** for critical distinctions (dashed/solid lines, hatched fills)

---

## Narrative Framework

**Structure: Headline → Pattern → Context → Meaning → Action**

**1. Headline (one sentence, insight-first):**
- Not: "This chart shows monthly revenue."
- **But:** "Revenue grew 30% YoY, driven by Enterprise segment."

**2. Pattern (what do you see?):**
- "Q1-Q2 flat at $2M/month, then steady climb to $2.6M in Q4."
- "Enterprise segment grew 120% while SMB declined 10%."

**3. Context (compared to what?):**
- "vs target: 15% above plan"
- "vs last year: Q4 2023 was $2.0M, now $2.6M"
- "vs industry: Our 30% growth vs 10% industry average"

**4. Meaning (why does it matter?):**
- "Suggests product-market fit in Enterprise; SMB churn indicates pricing mismatch."
- "If sustained, Q1 2025 could hit $3M/month."

**5. Action (what should we do?):**
- "Prioritize: Hire 2 Enterprise AEs, launch SMB annual plans to reduce churn."
- "Monitor: Enterprise win rate, SMB churn by plan type."

**Example Full Narrative:**

> **Headline:** Enterprise revenue up 120% YoY while SMB d