Adding any form of text to data visualizations may seem to be counterintuitive, but with the right application, text can be a powerful addition to any data visualization. Before you go crazy adding text to your visuals, however, you should adhere to certain rules to avoid cluttering your visuals:

  • Use as few words as possible. Text added to any data visualization must be complementary. Space is usually very limited, so your goal is to explain your visual with as few words as possible. E-mail marketers do this all the time, sometime spending hours or even days on a single subject line, hoping to gain the highest e-mail open rate possible. The open rate is calculated by expressing the number of e-mails opened as a percentage of total e-mails.

    This process usually requires careful word choices, consideration of the limited space available in a single line, and multiple split tests to confirm the effectiveness of the chosen subject line. Done correctly, the process can increase e-mail open rates astronomically. The same is true of adding a line or two of text to a given data visualization. Done correctly, it can virtually eliminate misinterpretation of the data.

    Split testing is a marketing method that splits e-mail subscribers into two groups. Each group is sent a separate e-mail that is then tracked and compared based on specific metrics. The one with the better metrics is used.

  • Stick to simple words or single characters. Using simple words is the key to providing your users at-a-glance understanding of your visual and accompanying text. Say that a number has gone up or use an up arrow (↑) instead of saying that something has increased, or say that something is "bad" rather than "negative." These examples present information in a way that's easy for anyone to digest.

    Simplicity is the key when it comes to choosing words.

    Avoid using acronyms that require some existing knowledge or additional action to interpret.

  • Use single lines of text. Besides keeping text simple, you need to keep it short. Just the sight of multiple lines of text may make a user hesitant to start reading that text. Your goal should be to use a single line of text that's easy to read and interpret.

  • Apply color sparingly. Although adding RAG (red, amber, and green) alert colors can be useful, adding random colors to make text stand out against your visuals isn't a good idea. In general, data visualizations are color-rich, and adding text in a color that contrasts with the visual causes the text to compete with the visual. When you're a beginner, try to stick to text colors such as black, blue, and gray.

    RAG (short for Red, Amber, and Green) colors are used as alert colors in data visualizations. Think of a traffic light: Red means "Stop, something is wrong!"; yellow means "Proceed, but with caution!"; and green means "Go, everything is OK!"

  • Know your data. One cardinal rule about adding any form of text to describe a visualization is to make sure that the text applies to every scenario of the data being displayed — especially if the data is dynamic. This means that you can't include a static set of text for only one scenario of the data visualization, because the text won't be applicable when the data changes along with the scenario. This situation is where dynamic text comes in handy.