data-visualization

name	Data Visualization
description	Guide for creating effective data visualizations and dashboards that communicate insights clearly
emoji	📈
tags	["visualization","dashboards","charts","design"]

Data Visualization Skill

Chart Selection Guide

Bar Chart

Use when:

• Comparing categories
• Showing rankings
• Displaying discrete data

Best practices:

• Sort bars by value (descending) unless natural order exists
• Use horizontal bars for long category names
• Limit to ~10 categories for readability
• Use consistent colors

Example: Product sales by category, user signups by region

Line Chart

Use when:

• Showing trends over time
• Displaying continuous data
• Comparing multiple series

Best practices:

• Use time on x-axis
• Limit to 3-5 lines for clarity
• Use distinct colors/styles
• Mark important events/annotations

Example: Daily active users over time, revenue trends

Scatter Plot

Use when:

• Showing relationships between two variables
• Identifying correlations
• Finding outliers

Best practices:

• Use appropriate axis scales
• Add trend line if relationship exists
• Color-code by category if relevant
• Label outliers

Example: Price vs. demand, user engagement vs. retention

Heatmap

Use when:

• Showing patterns in two dimensions
• Comparing categories across time
• Displaying correlation matrices

Best practices:

• Use intuitive color scale (low to high)
• Include values in cells if space allows
• Sort rows/columns meaningfully
• Use diverging colors for centered data

Example: User activity by hour/day, correlation matrix

Pie Chart

Use when:

• Showing parts of a whole
• Few categories (3-5 max)

Best practices:

• Limit to 5-7 slices
• Sort slices by size
• Use distinct colors
• Consider bar chart alternative

Example: Market share, revenue by segment

Histogram

Use when:

• Showing distribution of continuous data
• Identifying skewness
• Understanding data shape

Best practices:

• Choose appropriate bin size
• Use consistent bin widths
• Label axes clearly
• Overlay distribution curve if helpful

Example: User age distribution, transaction amount distribution

Dashboard Design Principles

Key Metric Prominently

• Place most important metric at top-left
• Use large, clear font
• Show trend indicator (↑↓)
• Include comparison (vs. previous period)

Drill-Down Capability

• Enable filtering by time period
• Allow dimension breakdowns
• Provide "View Details" links
• Support export functionality

Consistent Time Ranges

• Use same time range across all charts
• Default to meaningful period (7d, 30d, YTD)
• Allow easy switching between ranges
• Show time range selector prominently

Visual Hierarchy

• Most important information = largest/most prominent
• Group related metrics together
• Use whitespace effectively
• Guide eye flow top-to-bottom, left-to-right

Performance

• Optimize query performance
• Use appropriate aggregation levels
• Cache frequently accessed data
• Show loading states

KPI Dashboard Template

Header Section

• Dashboard Title: [Name]
• Last Updated: [Timestamp]
• Time Range: [Selector]
• Filters: [Key filters]

Primary KPIs (Top Row)

• [KPI 1]: [Value] [Trend] [Change %]
• [KPI 2]: [Value] [Trend] [Change %]
• [KPI 3]: [Value] [Trend] [Change %]
• [KPI 4]: [Value] [Trend] [Change %]

Trend Charts (Middle Section)

• [Metric] Over Time: Line chart
• [Breakdown]: Bar chart
• [Comparison]: Grouped bar chart

Detailed Tables (Bottom Section)

• Top Performers: Table with drill-down
• Recent Activity: Time-series table
• Alerts/Issues: List of items needing attention

Footer

• Data Sources: [List]
• Refresh Cadence: [Frequency]
• Owner: [Contact]

Executive Summary Report Template

One-Page Layout

Header:

• Report Title
• Date Range
• Prepared By
• Date

Key Metrics (4-6 boxes):

• [Metric Name]: [Value] [Trend] [% Change]

Main Chart:

• Most important visualization (large, clear)
• Caption explaining key insight

Supporting Charts (2-3):

• Secondary visualizations
• Smaller but clear

Key Insights (Bullet Points):

• Insight 1: [Finding]
• Insight 2: [Finding]
• Insight 3: [Finding]

Recommendations:

• Action 1: [What to do]
• Action 2: [What to do]

Footer:

• Data sources and methodology
• Contact for questions

Color and Accessibility Guidelines

Color Palette

• Primary: Use brand colors sparingly
• Data Colors: Use colorblind-friendly palette
  • Blue, Orange, Green, Red, Purple
  • Avoid red-green combinations
• Semantic Colors:
  • Green: Positive/good
  • Red: Negative/alert
  • Yellow: Warning
  • Gray: Neutral/inactive

Colorblind-Friendly Palettes

• Option 1: Blue, Orange, Teal, Pink, Brown
• Option 2: Use patterns/textures in addition to color
• Option 3: Use lightness/darkness variations

Accessibility

• Contrast: Minimum 4.5:1 for text
• Text Size: Minimum 12pt for body text
• Labels: Always label axes and data points
• Alt Text: Provide descriptions for screen readers
• Keyboard Navigation: Ensure dashboards are keyboard accessible

Best Practices

• Don't rely solely on color to convey information
• Use icons or shapes in addition to color
• Test with colorblind simulators
• Provide high-contrast mode option
• Use consistent color meanings across dashboard

Common Visualization Mistakes to Avoid

1. Too Many Colors: Stick to 5-7 colors max
2. 3D Charts: Avoid 3D, it distorts data
3. Pie Charts for Many Categories: Use bar chart instead
4. Missing Labels: Always label axes and data
5. Inconsistent Scales: Use same scale for comparisons
6. Chartjunk: Remove unnecessary decorations
7. Misleading Axes: Start y-axis at 0 for bar charts
8. Overcrowding: Leave whitespace for clarity