visualization-choice-reporting

# Visualization Choice & Reporting

## Overview

**Visualization choice & reporting** matches visualization types to questions and data, then creates narrated dashboards that highlight signal and recommend actions.

**Three core components:**

**1. Chart selection:** Match chart type to question type and data structure (comparison → bar chart, trend → line chart, distribution → histogram, relationship → scatter, composition → treemap, geographic → map, hierarchy → tree diagram, flow → sankey)

**2. Visualization best practices:** Apply perceptual principles (position > length > angle > area > color for accuracy), reduce chart junk, use pre-attentive attributes (color, size, position) to highlight signal, respect accessibility (colorblind-safe palettes, alt text), choose appropriate scales (linear, log, normalized)

**3. Narrative reporting:** Lead with insight headline, annotate key patterns, provide context (vs benchmark, vs target, vs previous period), interpret what it means, recommend next actions

**When to use:** Data analysis, dashboards, reports, presentations, monitoring, exploration, stakeholder communication

## 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-selecti