3. Color Best Practices

• Sequential : Use for ordered data (light to dark)
• Diverging : Use for data with a meaningful midpoint
• Categorical : Use for unordered categories
• Highlight : Use accent colors to draw attention
• Test accessibility with colorblind simulators

4. Storytelling with Data

• Lead with the insight, not the data
• Use annotations to highlight key findings
• Arrange charts in logical flow
• Provide context and comparisons
• Include data sources and timestamp

Visualization Workflow

1. Understand the Data

   • Explore data structure and distributions
   • Identify key variables and relationships
   • Determine the message to communicate

2. Select Visualization Type

   • Match chart type to data characteristics
   • Consider audience and use case
   • Plan for interactivity if needed

3. Design the Visualization

   • Create initial draft
   • Apply design principles
   • Optimize for clarity and impact

4. Refine and Validate

   • Get feedback from stakeholders
   • Test on target audience
   • Iterate based on feedback
   • Verify accuracy

Common Mistakes to Avoid

• Using 3D charts unnecessarily (adds confusion)
• Too many colors or visual elements
• Missing or unclear axis labels
• Truncated y-axis to exaggerate differences
• Using pie charts for more than 5-6 categories
• Poor color choices (rainbow colors for sequential data)

Tools and Libraries

Recommend appropriate tools based on needs:

• Python : matplotlib, seaborn, plotly, altair
• R : ggplot2, plotly
• JavaScript : D3.js, Chart.js, Highcharts
• BI Tools : Tableau, Power BI, Looker

Example Use Cases

• Dashboard Design : "Create an executive dashboard for sales metrics"
• Exploratory Analysis : "Visualize patterns in customer behavior data"
• Report Charts : "Generate publication-ready charts for annual report"

Weekly Installs

65

Source

smithery.ai/ski…lization

First Seen

Mar 6, 2026

Security Audits

SocketPass

Installed on

claude-code42

codex23

opencode21

gemini-cli17

github-copilot17

amp16