Data can seem like a daunting stream of numbers across Excel spreadsheets and paper reports. However, it’s difficult for the brain to perceive too much data at one given time. Colors are a great way to decipher and help you better analyze what you’re looking at. People often associate information with certain colors. For example, the color red usually means stop or danger, and green typically represents good or go. Using color adds distinguishing features to datasets and enhances the qualitative and quantitative data. Even NASA supports using color in data – especially in their maps that demonstrate data surrounding different climates and atmospheres.
Your data needs color in order to help your eyes make sense of the information in quick glance. It also breaks the data down into manageable bits. By using data visualization, you can paint a picture of what future goals and past accomplishments look like. Here are a few things to keep in mind when you add color to your data.By using #dataviz, you can paint a picture of what future goals and past accomplishments look like. Click To Tweet
How To Choose the Right Colors for Your Charts and Graphs
- Choose the right color palette. Not just any color will do when you are using it for data. Extremely bright colors like lime green and yellow hurt the eyes. Choose a color palette that adds contrast, but is not overly bright. Primary colors are always a good place to start and if you combine them with a grayscale, you can easily add a contrasting element. But be sure the colors are different enough that your data is still readable, even for those who are colorblind.
- Use a color key. It can be difficult to understand the data if there is no reference to what each color means. When you add color, make sure to include a color key to clearly explain what each shade on the dataset represents.
- Choose the complementary colored text. While the background color is vital, the text color always needs to be readable. It’s important to remember that black text will not always be the best choice. A light-colored text on a dark background may do better in making your data legible and distinctive.
- Don’t use too many colors. It’s tempting to use a wide array of colors, but you’ll want to restrain yourself and choose your colors wisely. Too many colors can be confusing, so try to stick to five colors or less. There may be occasions where you have to use more colors, but keep them complementary or contrasting – depending on the type of data.
- Shapes make a difference. Data can be presented in many different ways. From line graphs to pie charts, color choices are important. Primary colors do well for line graphs, but more colorful shades could make a pie chart really pop. Be sure to take into account how well the graph shapes show up when you choose each color.
Read next: 19 Inspiring Data Viz Designs
Data does not have to be boring or difficult to read. Color makes data visualization more effective and helps viewers see the big picture in a simple glance. Using colors will make your data appear more professional and well-organized. However, you don’t need a rainbow of color to make your data more fun or organized. You just need enough color to catch the attention of your readers. If you’re interested in stepping up your Excel spreadsheets and reports, consider using a dashboard to bring greater readability and interactivity to your data.You don’t need a rainbow of #color to make your #data more fun or organized. Click To Tweet
- 1.Quick Tip: Matching Colors = Effective Branding
- 2.Color Your Data
- 3.Data Visualization and the 9 Fundamental Design Principles
- 4.Neuroaesthetics and Informative Art
- 5.Painting by Numbers: Designing Your Data Picture
- 6.Developing Your (Color) Palette
- 7.Get Inspired: 19 Inspiring Data Viz Designs
- 8.INFOGRAPHIC: Fundamental Principles for Data Visualization Design
Get the Guide Fundamental Design Principles for Dashboards
Even if you’re not the artistic type, this guide will have you thinking like a graphic designer and making informed choices that support your data narrative.