Clutter and confusion are not attributes of data—they are shortcomings of design. – Edward Tufte
Michael Friendly defines data visualization as “information which has been abstracted in some schematic form, including attributes or variables for the units of information.” In other words, it is a coherent way to visually communicate quantitative content. Depending on its attributes, the data may be represented in many different ways, such as a line graph, bar chart, pie chart, scatter plot, or map.
It’s important for product designers to adhere to data visualization best practices and determine the best way to present a data set visually. Data visualizations should be useful, visually appealing and never misleading. Especially when working with very large data sets, developing a cohesive format is vital to creating visualizations that are both useful and aesthetic.
Principles
Define a Clear Purpose
Data visualization should answer vital strategic questions, provide real value, and help solve real problems. It can be used to track performance, monitor customer behavior, and measure effectiveness of processes, for instance. Taking time at the outset of a data visualization project to clearly define the purpose and priorities will make the end result more useful and prevent wasting time creating visuals that are unnecessary.
Know the Audience
A data visualization is useless if not designed to communicate clearly with the target audience. It should be compatible with the audience’s expertise and allow viewers to view and process data easily and quickly. Take into account how familiar the audience is with the basic principles being presented by the data, as well as whether they’re likely to have a background in STEM fields, where charts and graphs are more likely to be viewed on a regular basis.
Visual Features to Show the Data Properly
There are so many different types of charts. Deciding what type is best for visualizing the data being presented is an art unto itself. The right chart will not only make the data easier to understand, but also present it in the most accurate light. To make the right choice, consider what type of data you need to convey, and to whom it is being conveyed.
Make Data Visualization Inclusive
Color is used extensively as a way to represent and differentiate information. According to a recent study conducted by Salesforce, it is also a key factor in user decisions.
They analyzed how people responded to different color combinations used in charts, assuming that they would have stronger preferences for palettes that had subtle color variations since it would be more aesthetically appealing.
However, they found that while appealing, subtle palettes made the charts more difficult to analyze and gain insights. That entirely defeats the purpose of creating a visualization to display data.
The font choice can affect the legibility of text, enhancing or detracting from the intended meaning. Because of this, it’s better to avoid display fonts and stick to more basic serif or sans serif typefaces.
Conclusion
Good data visualization should communicate a data set clearly and effectively by using graphics. The best visualizations make it easy to comprehend data at a glance. They take complex information and break it down in a way that makes it simple for the target audience to understand and on which to base their decisions.
As Edward R. Tufte pointed out, “the essential test of design is how well it assists the understanding of the content, not how stylish it is.” Data visualizations, especially, should adhere to this idea. The goal is to enhance the data through design, not draw attention to the design itself.
Keeping these data visualization best practices in mind simplifies the process of designing infographics that are genuinely useful to their audience.
