Simple Tips for Designing Better Data Visualization

Very few roles in the workplace survive without interacting with some form of data visualization on a weekly, if not daily, basis. But choosing the right type of visualization and organizing a presentation of numerous data visualizations can be very challenging and leave little time for optimizing visuals.

We know good data visualization is meant to add context to data in a digestible and actionable way. But regardless of the insights, data visualization also has to be aesthetically appealing and engaging for stakeholders—while promoting action after capturing attention. Here are some tips that can help you design your next presentation to be both appealing and actionable.

Designing for Appeal

Regardless of the data visualization, any strong presentation of data must follow a set of simple rules to be aesthetically appealing and engaging. The color scheme, specifically, can be an easily overlooked aspect of data visualization, yet have a tremendous impact on the appeal and readability of content. While branding guidelines can help, as more data points or categories are included sometimes more colors are required. Below are some example approaches to finding the right color scheme and combination by utilizing the color wheel:

• Complementary: colors that sit opposite of each other
• Triad: three colors that sit equal distances from each other
• Analogous: combination of colors that sit side by side
• Monochromatic: different shades of the same color

Aside from color, font choice and sizing is also important to get right. Obviously keeping the font type simple and professional is a must, but incorporating different sizing of the font based on the importance of information is also a great way to make content outside of the data points more impactful and organized. This is likely most relevant to qualitative insights, but still applicable to quantitative visualizations.

Similar to font size, data labels and legends are also critical to get right when it comes to some of the less significant details of data visualization. Knowing when to use a legend over labels, as in the case when you have too many data points, can make a chart clearer and easier to read. These same design tips can also be applied to increase the engagement with other methods for sharing data like internal dashboards or survey infographics.

Designing for Action

Now you’ve got something that looks good, but does it still inform and promote action from the reader? Making insights more impactful is the number one objective of data visualization. Data visualization, therefore, can also leverage certain design aspects to be more actionable:

• Group similar data points (i.e., related categories) by proximity or color to add another level of depth to the data or for easier comparisons without cluttering the visual
• Sort data (specifically column, bar, or similar charts) by descending order to easily determine those categories with the highest frequency
• Use text boxes or specific markers to call out significant data points without taking away from the key finding

The more aspects your data visualization satisfies above, the more likely it is to be both appealing and actionable. But remember to ensure your data visualization and accompanying content is also relevant, concise, and shareable. To see some of our data visualization in action, check out the infographic below. You’ll also learn about GutCheck Constellation®, a study that connects survey data with big data.