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.

Step 2: Select visualization type

Match question type to chart family using 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 (clear title, labeled axes, legend if needed, annotations, accessible colors). For complex cases (multivariate, dashboards, interactive) → Study 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.

Step 5: Validate and deliver

Self-assess using 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.

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Question-Data Profiling

Question Types → Chart Families

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.

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