Dashboard Design

Learn how to create dashboards that effectively communicate insights, drive action, and engage users through thoughtful design, layout, and visualization choices.

Dashboard Design Principles

Dashboard Design Framework

Key components and relationships in effective dashboard design

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Legend

Components

Planning

Content

Design

Experience

Technical

Connection Types

Process Flow

Informs

Determines

Guides

Dashboard Purpose and Types

🎯

Dashboard Types

Technical

Different dashboard types serve distinct business purposes:

• Operational dashboards: Monitor daily activities and immediate action items
• Strategic dashboards: Track progress toward long-term goals and strategies
• Analytical dashboards: Enable exploratory analysis to discover insights
• Tactical dashboards: Support mid-level management decision-making
• Executive dashboards: Provide high-level overview for leadership

🎯

Dashboard Types

Non-Technical

Technical implementation considerations by dashboard type:

• Operational dashboards: real-time data connections and alerting
• Strategic dashboards: trend analysis with historical data models
• Analytical dashboards: deep drill-down capabilities and large datasets
• Tactical dashboards: interactive filtering and mid-level metrics
• Executive dashboards: simplified KPIs with mobile optimization

Dashboard Purpose Statement Template

Before designing any dashboard, create a clear purpose statement:

This dashboard helps [AUDIENCE] to [ACTION] by showing [METRICS] 
over [TIME PERIOD] at [GRANULARITY] level, enabling decisions about [PROCESS].

Example:

This dashboard helps regional sales managers to allocate resources by showing 
sales performance over the current quarter at the product category level, 
enabling decisions about inventory management and sales team deployment.

Information Visualization Principles

📊

Visual Encoding Best Practices

Technical

Effective visual encoding is essential for business communication:

• Reduces cognitive load for decision-makers consuming information
• Highlights key insights that might be missed in tables of numbers
• Makes complex relationships and patterns immediately apparent
• Increases retention of important metrics and trends
• Supports faster, more accurate decision-making

📊

Visual Encoding Best Practices

Non-Technical

Technical implementation of visual encoding:

• Match data types to appropriate chart types (categorical, ordinal, quantitative)
• Use appropriate color scales (sequential, diverging, categorical)
• Apply gestalt principles for visual grouping
• Implement proper axis scaling and numeric formatting
• Configure effective legends and tooltips

Chart Selection Guide

Data Relationship	Recommended Charts	When to Use
Comparison	Bar/Column charts, Bullet charts	Compare values across categories
Composition	Pie charts, Stacked bar charts, Treemaps	Show parts of a whole
Distribution	Histograms, Box plots, Scatter plots	Understand data spread and outliers
Trend	Line charts, Area charts, Sparklines	Display changes over time
Correlation	Scatter plots, Bubble charts, Heatmaps	Show relationships between variables
Geographic	Maps, Choropleth maps, Cartograms	Display location-based data
Hierarchical	Treemaps, Sunburst diagrams, Network graphs	Show nested relationships
Ranking	Ordered bar charts, Bump charts	Compare items by position

Dashboard Layout and Structure

Dashboard Layout Patterns

Common structural patterns for organizing dashboard content

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Legend

Components

Layout

Connection Types

Process Flow

Similar to

Can combine with

Derived from

Layout Best Practices

1. Consistent Grid Structure

   • Use a consistent grid system with logical spacing
   • Align elements to create visual harmony
   • Group related information together

2. Visual Hierarchy

   • Place most important information in the upper left
   • Use size and color to emphasize key metrics
   • Create clear visual pathways through information

3. White Space

   • Include adequate padding between elements
   • Avoid cluttered layouts that overwhelm users
   • Use white space to create logical groupings

4. Responsive Design

   • Ensure dashboards function across device sizes
   • Implement fluid layouts when possible
   • Consider mobile-first designs for executive dashboards

Visualization Component Selection

🔢

KPI Cards and Metrics

Technical

KPI cards translate raw metrics into actionable business insights:

• Make performance against goals immediately visible
• Highlight exceptional conditions requiring attention
• Provide context to raw numbers through comparisons
• Create accountability by associating metrics with objectives
• Support the "at a glance" needs of busy decision makers

🔢

KPI Cards and Metrics

Non-Technical

Technical implementation of KPI components:

• Use calculated fields for comparison to targets and baselines
• Implement conditional formatting for visual cues
• Create period-over-period calculations in the data model
• Use custom tooltips for contextual information
• Implement parameterized targets and dynamic benchmarks

Effective KPI Card Design

function KpiCard({ 
  title, 
  value, 
  previousValue, 
  target, 
  format = 'number', 
  icon 
}) {
  const percentChange = ((value - previousValue) / previousValue) * 100;
  const percentToTarget = ((value - target) / target) * 100;
  
  const statusColor = percentToTarget >= 0 ? '#34c759' : '#ff3b30';
  const trendColor = percentChange >= 0 ? '#34c759' : '#ff3b30';
  
  return (
    <div className="kpi-card">
      <div className="kpi-header">
        <h3>{title}</h3>
        {icon && <span className="kpi-icon">{icon}</span>}
      </div>
      
      <div className="kpi-value">
        {formatValue(value, format)}
      </div>
      
      <div className="kpi-meta">
        <div className="kpi-trend" style={{ color: trendColor }}>
          {percentChange >= 0 ? '↑' : '↓'} {Math.abs(percentChange).toFixed(1)}%
          <span className="trend-label">vs. previous</span>
        </div>
        
        <div className="kpi-target" style={{ color: statusColor }}>
          {percentToTarget >= 0 ? '↑' : '↓'} {Math.abs(percentToTarget).toFixed(1)}%
          <span className="target-label">vs. target</span>
        </div>
      </div>
    </div>
  );
}

📈

Chart Design Principles

Technical

Effective chart design principles that drive business value:

• Focus attention on the data and insights, not decoration
• Ensure accurate interpretation of trends and relationships
• Reduce time required to extract meaning from visualizations
• Present data in familiar formats for your specific audience
• Make context and comparisons clear at first glance

📈

Chart Design Principles

Non-Technical

Technical chart implementation best practices:

• Remove chart junk (unnecessary gridlines, borders, backgrounds)
• Use consistent axis ranges and scales across related charts
• Implement direct labeling instead of legends when possible
• Set appropriate number formats with right level of precision
• Create custom tooltips with contextual information

Interactive Elements and Filtering

🖱️

Dashboard Interactivity

Technical

Interactive dashboards provide significant business advantages:

• Enable self-service exploration without requiring new dashboards
• Accommodate different user questions and perspectives
• Support the natural analytical workflow from overview to detail
• Increase user engagement and dashboard adoption
• Reduce the time from question to insight

🖱️

Dashboard Interactivity

Non-Technical

Technical implementation of interactive features:

• Cross-filtering architecture with action filters
• Parameter controls for dynamic analysis
• Drill-down hierarchies with level awareness
• Custom navigation actions and tooltips
• URL actions for integration with external systems

Effective Filter Design

1. Global Filters

   • Place most common filters prominently at the top
   • Use dropdown menus for categorical filters with many options
   • Implement date range controls for time-based analysis
   • Show clear visual indication of active filters

2. Local Filters

   • Place specific filters near the visualizations they affect
   • Use visual filter controls when appropriate (e.g., sliders)
   • Provide quick filter reset options

3. Cross-Filtering

   • Allow clicking on chart elements to filter related visuals
   • Indicate which visualizations are affected by cross-filtering
   • Provide clear visual feedback when filters are applied

Color Usage in Dashboards

🎨

Color Strategy

Technical

Strategic use of color delivers key business benefits:

• Highlights areas requiring immediate attention or action
• Creates meaningful associations between related data points
• Reinforces brand identity in customer-facing analytics
• Improves comprehension speed for complex information
• Ensures inclusive access to insights for all team members

🎨

Color Strategy

Non-Technical

Technical implementation of dashboard color schemes:

• Configure categorical palettes with appropriate color distances
• Implement sequential and diverging color scales correctly
• Use transparency for overlapping elements
• Apply conditional formatting with meaningful thresholds
• Ensure accessibility with color blindness testing

Color Guidelines

1. Functional Color Use

   • Use color to convey meaning, not for decoration
   • Limit palette to 5-7 colors for categorical data
   • Use sequential color scales for quantitative data
   • Apply diverging scales for values with meaningful midpoints

2. Color for Highlighting

   • Use contrasting colors for key metrics or exceptions
   • Apply consistent color meaning across all dashboards
   • Avoid using too many highlight colors which dilutes effect

3. Brand Alignment

   • Incorporate company color scheme when appropriate
   • Ensure brand colors work functionally within visualizations
   • Create custom themed templates for consistency

4. Accessibility

   • Ensure sufficient contrast between text and background
   • Test dashboards with colorblindness simulators
   • Use patterns or shapes as secondary encoding when necessary

Performance Optimization

⚡

Dashboard Performance

Technical

Dashboard performance directly impacts business value:

• Reduces wait time for critical decision-making information
• Increases user adoption and engagement with analytics
• Lowers infrastructure costs through efficient resource usage
• Enables real-time and operational use cases
• Improves user satisfaction and analytics ROI

⚡

Dashboard Performance

Non-Technical

Technical performance optimization techniques:

• Implement efficient query design with appropriate aggregations
• Use extract data sources with optimized data structures
• Apply filters at the database level rather than in-memory
• Minimize the number of queries per dashboard
• Configure appropriate caching policies

Performance Best Practices by Platform

Tableau Performance Tips

1. Use extracts instead of live connections when possible
2. Apply context filters to reduce data processed
3. Create hierarchies for efficient drill-down
4. Use parameter actions instead of filter actions
5. Minimize the number of worksheets per dashboard

Power BI Performance Tips

1. Implement star schema modeling with proper relationships
2. Use appropriate DAX measures with context transition awareness
3. Apply query folding in Power Query transformations
4. Enable incremental refresh for large datasets
5. Use aggregations for large tables

Looker Performance Tips

1. Create aggregate awareness PDTs
2. Implement persistent derived tables for complex calculations
3. Use effective datagroups for caching strategy
4. Apply symmetric aggregates to prevent fan-out
5. Avoid unnecessary joins in LookML models

Platform-Specific Dashboard Development

BI Platform Comparison

Key capabilities and design approaches across major BI platforms

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Legend

Components

Platform

Concept

Connection Types

Process Flow

Visual-first

Report-oriented

Model-driven

Platform Selection Considerations

When choosing a BI platform for dashboarding, consider:

1. Audience Technical Proficiency

   • Power users vs. casual consumers
   • Self-service requirements
   • Mobile access needs

2. Data Infrastructure

   • Connection types and performance
   • Data volume and refresh frequency
   • Security and governance requirements

3. Visualization Requirements

   • Custom visualization needs
   • Interactive capabilities required
   • Embedding and sharing needs

4. Resource Availability

   • Development team skills and availability
   • IT support and infrastructure
   • Budget constraints

Dashboard Documentation and Rollout

📝

Dashboard Documentation

Technical

Effective documentation delivers key business benefits:

• Ensures consistent interpretation of metrics across teams
• Accelerates onboarding of new dashboard users
• Reduces support requests through self-service help
• Creates transparency around data sources and calculations
• Supports governance and compliance requirements

📝

Dashboard Documentation

Non-Technical

Technical documentation implementation:

• Inline documentation with tooltip explanations
• Data dictionary integration with field definitions
• Version control and change management
• Query and calculation documentation
• Performance benchmarking and testing results

Dashboard Release Checklist

✅ Purpose and audience clearly defined
✅ Metrics and calculations verified
✅ Filters and interactions tested
✅ Performance optimized for target audience
✅ Mobile/responsive design validated
✅ Documentation and tooltips complete
✅ Accessibility guidelines followed
✅ User testing feedback incorporated
✅ Data refreshes configured and tested
✅ Security and permissions set correctly

Additional Resources

• Tableau Visual Best Practices
• Power BI Dashboard Design Guidelines
• Information Dashboard Design by Stephen Few
• Storytelling with Data by Cole Nussbaumer Knaflic
• Data Visualization Society