In 2014, I created one of the first ever data visualization style guides for the Sunlight Foundation and then another one for the Consumer Financial Protection Bureau (CFPB) in 2017. I’m excited to see more organizations devote resources into standardizing their data visualization work.

The first step is defining what exactly a data visualization style guide is. As part of the Data Visualization Society, I’ve begun collecting examples of guides across multiple types of organizations. I will be taking a deep look at what is common practice, how this differs across organization types, and identifying innovative extensions that others might consider incorporating into their own guides. The more examples I have for this process, the better, so please submit guides and add to the spreadsheet!

Data visualization style guides defined

Definition: Data visualization style guides are standards for formatting and designing representations of information, like charts, graphs, tables, and diagrams. They include what (e.g. types of charts) and why (e.g. reasons for using specific colors). Templates for various tools (like Excel, R, D3.js or Tableau) often accompany a guide to show the how and to make it easy for people to apply the standards from the guide.

Data visualization style guides fit within an organization’s larger design system. They include how other guidelines, like brand standards or editorial guidelines, apply to data visualization. For example, they specify how elements like a logo, brand colors, and language tone specifically apply to charts, tables, and diagrams.

Style guides maintain uniformity across different tools and software that produce charts. An organization’s charts should be consistent across tools and look visually similar to the rest of the blog or report it’s part of. Having a style guide with principles and components that work across multiple tools, rather than just one template for one tool, helps achieve this consistency.

Designing for data is a unique challenge. It requires considerable precision and numeracy, but also careful thinking about audience, perception and accessibility constraints. Because the information needs to be conveyed accurately and understood properly, there are additional design constraints when it comes to writing chart titles and displaying connection between labels and data. Styles and colors that may work when applied to illustration do not always work when applied to the density of information data visualization often needs to convey.