Data Visualization
7 issues tagged with this topic.
Color in infographics and data visualization: the rules for visual encoding
Data visualization has its own color grammar — rules derived from research on how humans interpret visual information, with higher stakes than most design contexts because incorrect color choices can actively mislead the viewer. Sequential palettes, diverging palettes, and categorical palettes each have distinct structural requirements. This issue covers the fundamental rules for encoding data accurately with color, the practical constraints of real-world display environments, and how to build a production-ready data visualization color system.
Color in financial data visualization: beyond the red-green traffic light
Financial dashboards default to red-for-loss, green-for-gain because the convention is universal and understood. But this binary traffic light system breaks down in complex financial contexts: it excludes 8% of male users with color vision deficiency, it fails when more than two states need representation, and it creates perceptual overload in dense data grids. A more sophisticated approach uses the traffic light as a baseline and builds a full semantic color system around it that works for all users and all data states.
Color for SaaS dashboards: data-dense, analytical interfaces and the case for restraint
SaaS dashboards are among the most color-challenging design problems in product design. They must display dense data clearly, communicate state and status at a glance, support extended work sessions without cognitive fatigue, and remain visually coherent across many different chart types, table structures, and metric categories. The designers who do it best use color sparingly and purposefully — not to make the interface look sophisticated, but to make the information readable.
Color in Data Visualization: Encoding, Perception, and the Rules That Are Actually Followed
Data visualization color is a specialized discipline within color design, governed by how humans perceive differences in hue, saturation, and value when those differences carry quantitative meaning. The rules are different from brand color, UI color, or print color — and violating them silently degrades the accuracy of the data communication, sometimes in ways that are worse than using no color at all.
Color in Data Visualization: Principles for Charts, Dashboards, and Infographics
Data visualization color is a distinct discipline from brand or UI color. The goal is not aesthetic harmony but accurate encoding — color must communicate data structure, not override it. This issue covers the four semantic color roles in dataviz, perceptual uniformity requirements, color blindness constraints, and the specific rules that distinguish good chart color from misleading chart color.
Color in Data Visualization: Choosing Palettes That Inform Without Misleading
Data visualization is one of the highest-stakes environments for color decision-making. The wrong palette can make a chart misleading, inaccessible to colorblind viewers, or simply unreadable when printed in grayscale. This issue covers the three main palette types (sequential, diverging, categorical), how to select and validate them, and the most common mistakes that turn an informative chart into a confusing one.
Choosing Colors for Charts and Data Visualizations
The specific rules for selecting and ordering chart colors — categorical, sequential, and diverging palettes — and why standard brand colors often fail in data contexts.