data-visualization

name

data-visualization

description

Guides data visualization design—chart type selection for message and audience, honest scales and labeling, color and accessibility, executive and operational dashboard layout, actuarial and insurance charts (loss triangles, trends, distributions), ethics and misleading-viz avoidance, and visualization specs for engineers versus slides. Tool-agnostic patterns (matplotlib, plotly, Tableau concepts). Use when the user says "data visualization", "chart type", "dashboard design", "visualize this data", "which chart should I use", "misleading chart", "accessible chart", "executive dashboard", "loss triangle chart", or "data viz spec"—not full statistical analysis, BI platform admin, ETL pipelines, narrative-only storytelling, or hands-on warehouse or dbt modeling.

Data Visualization

When to Use

• Choose chart types that match the analytical question and audience decision
• Apply design principles: honest axes, labels, color, hierarchy, small multiples
• Meet accessibility needs: colorblind-safe palettes, contrast, alt-text guidance
• Design executive summaries and operational monitoring dashboards (layout and viz layer)
• Build actuarial/insurance views: loss triangles, trend panels, distributions, scenario bands
• Audit or fix misleading charts (truncated axes, dual-axis abuse, cherry-picking)
• Write viz specs for engineers (data bindings, encodings) or slide narrative outlines
• Review matplotlib, plotly, ggplot, or BI tool outputs for clarity and integrity

When NOT to Use

• Full exploratory analysis, modeling, A/B tests, or MLOps → data-scientist
• Narrative arc, key messages, and storytelling without chart design focus → storytelling
• Cloud cost allocation, CUR analysis, or FinOps cadence → finops-analyst
• Dashboard SQL, KPI definitions, cohort/funnel queries, or BI tool admin → bi-analyst
• dbt marts, warehouse modeling, tests, and lineage → analytics-data-engineer
• Assumption selection, governance packs, or change control → assumption-setting
• ETL/ELT pipeline build, orchestration, or data quality frameworks → data-warehouse-engineer
• Interactive HTML dashboard products with filters and deployment → route to frontend or product skills if present; pair with bi-analyst for metric definitions

Related skills

Need	Skill
ML, statistics, experiments, production models	data-scientist
Story spine, executive narrative, data story wording	storytelling
Cloud spend charts tied to allocation and optimization	finops-analyst
KPI definitions, analytical SQL, BI delivery	bi-analyst
Warehouse marts and analytics engineering	analytics-data-engineer
Assumption packs, sensitivity grids, governance	assumption-setting
Pricing, reserving, triangle mechanics	actuary

Core Workflows

1. Frame message, audience, and medium

1. State the decision or question the viz must support
2. Identify audience (exec, ops, regulator, engineer) and medium (slide, dashboard, report, spec)
3. List metrics with definitions; confirm numerator/denominator with bi-analyst if unclear
4. Note uncertainty (ranges, confidence, scenarios) before choosing encodings
5. Pick one primary message per view; defer secondary points to appendix or drill-down

See references/data_visualization_scope.md.

2. Select chart type and encoding

1. Map question type (comparison, trend, distribution, relationship, composition, geography) to chart family
2. Prefer simplest chart that carries the message; add small multiples before exotic forms
3. Document encoding: x, y, color, size, facet, and sort order
4. Flag when tables beat charts (exact lookup, many dimensions, audit trails)

See references/chart_selection_and_message.md.

3. Apply design and accessibility

1. Set axis baselines, units, and tick density; justify log scales
2. Choose color for meaning (not decoration); test colorblind and contrast
3. Write labels, titles that state the insight, and source/refresh footnotes
4. Provide alt text or long descriptions for static exports

See references/design_principles_and_accessibility.md.

4. Design dashboards and executive views

1. Apply visual hierarchy (F-pattern, KPI strip, drill paths)
2. Limit density; separate monitoring vs exploratory layouts
3. Add context: targets, prior period, benchmarks, annotations for events
4. Specify interactions only when they change decisions (filters, drill, alerts)

See references/executive_and_dashboard_design.md.

5. Actuarial and insurance visualization

1. Use standard loss triangle layouts; label development and valuation periods
2. Show trends and distributions with explicit basis (accident year, calendar year)
3. Present scenarios as bands or small multiples—not false point precision
4. Coordinate labels with assumption-setting and actuary for technical definitions

See references/actuarial_insurance_visualization.md.

6. Ethics check and handoff

1. Run misleading-viz checklist before publish
2. Produce engineer spec (data schema, encodings, refresh) or slide outline (headline per chart)
3. Separate exploration drafts from production assets

See references/misleading_viz_and_ethics.md.

Output standards

• One primary insight per chart; title states the takeaway
• Axes labeled with units; zero baseline when magnitude comparisons matter
• Source, as-of date, and filters documented on every external-facing viz
• No fabricated data or smoothed series without disclosure
• Accessibility: do not rely on color alone; meet contrast targets for text and UI chrome
• Specs list fields, aggregations, sort, and edge cases (nulls, small n)

When to load references

Topic	Reference
Scope, boundaries, tool posture	references/data_visualization_scope.md
Chart selection and message fit	references/chart_selection_and_message.md
Design, color, accessibility	references/design_principles_and_accessibility.md
Executive and dashboard layout	references/executive_and_dashboard_design.md
Actuarial and insurance charts	references/actuarial_insurance_visualization.md
Misleading viz and ethics	references/misleading_viz_and_ethics.md