Every organization that produces data visualizations to communicate their research should create a data visualization style guide. A data visualization style guide does for graphs what the Chicago Manual of Style does for English grammar. It defines the components of a graph and their proper, consistent use. Like a writing style guide, a comprehensive data visualization style guide breaks down the parts of graphs, charts, and tables to demonstrate best practices and strategies to design and style your charts. Elements like font and color, the widths of lines and style gridlines, and the use of tick marks are all choices that determine whether a graph is clear, engaging, and consistent—or whether it isn’t.

As an example, see this image from the beginning part of the style guide from the Urban Institute, a nonprofit research organization based in Washington, DC. Their style guide defines each part of their charts to reflect their styling preferences.

In organizations, a data visualization style guide serves three purposes:

First, it provides team members with the detailed styles and expectations about what should and should not be included in a visualization. Where should the title go? How large should it be? What font? What color?

Second, it guides those who may not be familiar with (or care about) all the styling and branding guidelines the organization may value. Instead of asking researchers and analysts to compile the data, create the graph, and then worry about which colors and fonts to use, a style guide makes those decisions easier. Building these styles into software tools streamlines the process and automates the application of graph styles.

Finally, a style guide sets the tone and expectations for people in the organization that the style, look, and details about data visualization are as important as other branding materials. But it’s also important to recognize that styles for charts may need to differ from the styles used in logos and other branding materials. Several dark colors may look great on the box packaging, but those same colors will be hard to tell apart as lines on a bar chart.

Even if you’re an individual working with data, a style guide can be worthwhile. A custom style guide will make your work more consistent and efficient, and it will build your individual brand so your work stands out. A good style guide handles the basic style decisions for you, so you can focus on more important aspects of creating data visualizations.

The difference between a grammar guide and a data visualization guide is that many of our data style decisions are subjective. While the word their is objectively different than they’re, and the use of one in a particular case is either correct or incorrect, there is no objectively correct or incorrect line thickness for a chart. There are, however, certain best practices to consider for specific chart types as well as color, font, and layout considerations. But for the most part, the styles you choose will reflect you and your organization’s preferences.

An effective, comprehensive data visualization style guide is best developed at the organizational level. If possible, bring your design and data teams together to determine branding guidelines that meet the needs of your organization. If your organization does not have these divisions, or if you are working to develop your own individual style guide, you might reach out to experts or refer to other published style guides—for example, this one from the National Cancer Institute, or explore you can explore more than 30 data visualization style guides in this collection—to develop branding guidelines and styles.

Image from the National Cancer Institute style guide showing color palettes.
Source: National Cancer Institute

Remember to treat your data visualization style guide as a living document. Revisit the guide as technologies and trends change. And remember to be flexible to the different needs, tools, and skills in your organization. Creating an instructive and clear guide that can be accessed and implemented by everyone is in the best interest of your organization and your readers.

This blog post is adapted from my new book, Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks, on sale now wherever you get your books.