Designing Graphs for Decision-Makers

Abstract

Data graphics can be a powerful aid to decision-making—if they are designed to mesh well with human vision and understanding. Perceiving data values can be more precise for some graphical types, such as a scatterplot, and less precise for others, such as a heatmap. The eye can extract some types of statistics from large arrays in an eyeblink, as quickly as recognizing an object or face. But perceiving some patterns in visualized numbers—particularly comparisons within a dataset—is slow and effortful, unfolding over a series of operations that are guided by attention and previous experience. Effective data graphics map important messages onto visual patterns that are easily extracted, likely to be attended, and as consistent as possible with the audience’s previous experience. User-centered design methods, which rely on iteration and experimentation to improve a design, are critical tools for creating effective data visualizations.