How do you match your color and font choices with your data story and audience? This is a crucial question for data visualization, as the visual elements of your graphics can enhance or undermine your message. In this article, we will explore some principles and tips for selecting colors and fonts that suit your data and your audience.
Colors and fonts are not just cosmetic details in data visualization. They can convey meaning, emotion, contrast, hierarchy, and emphasis. They can also affect the readability, accessibility, and attractiveness of your graphics. Choosing the right colors and fonts can help you communicate your data story more effectively and engage your audience more deeply.
I agree, and not only the right colours, but the hue (H), saturation (S) and luminance (L) of the chosen colours also play significant part in stimulating different emotions for different types of target audience. The HSL model-based colour theory has evidence in data that proves people’s reaction to different colours actually varies with different individuals, cultures and purpose. So based on the end users and the purpose of the story that needs to be told, one can use colour theory to create powerful visualisations.
When creating data graphics, colors can be used to highlight, group, differentiate, or encode data points. However, not all colors are suitable for data visualization. To choose the right colors, you should use a color palette that matches your data type and avoid using too many colors or colors that are too similar or too bright. Additionally, consider the cultural and emotional associations of colors and test your colors for contrast, visibility, and color blindness. It's also important to provide alternatives or labels for accessibility.
Over the years of designing dashboards for a wide range of organizations, one of the most vital elements is change awareness. Often our visualizations are carriers of change, and people are being introduced to new data and visuals. Therefore, we must build on a foundation of familiarity, lest the change impact be too great for some. So, what does this have to do with fonts you might ask? Most organizations have a standard brand font. This is a great starting place for the font used in your visualizations. It is familiar to people and instills comfort, even without the end user being aware of it. The lesson, leverage the internal brand font when and where available.
A few of my personal favourites: 1?? Use a color scheme that is appropriate for the data you're presenting. For example, you might use a diverging color scheme for the data that has both positive and negative values, or a sequential color scheme for the data that is ordered from low to high. 2?? Use color to highlight important data points or trends. For example, you might use a different color to highlight the maximum or minimum value in a chart, or use a gradient to show a change in value over time. 3?? Avoid using too many colors in a single chart. Instead, use a limited color palette and reserve certain colors for specific purposes, such as highlighting important data points or trends.
Leverage your company's (or client's) guidelines If your company has an official colour palette or official fonts, take those into account. When possible, ask for the brand guidelines. For instance, your designers might have very carefully crafted guidelines about contrast, which fonts to use for which context. Guidelines might also include: key elements of the design system (photography look and feel, illustrations, shapes), not only colours and fonts. Leverage the existing work!
The number of different colours in a data visualisation also matters and the scale of data can be useful when making that decision. For example sequential data is best visualised using a single colour, while divergent data can be represented with two colours. On the other hand, qualitative data can require as many colours as possible. But when using more than 7-8 colours, users might not be able to recall what each one represents. This is where one can be playful and creative with different saturations and hues.
A fun way to incorporate color in your executive level slides is use the color pallette of the corporate logo, companies pay a lot of money for ideal palletes so use that to your advantage.
When creating data graphics, fonts can be used to display text elements such as titles, labels, annotations, or legends. However, not all fonts are suitable for data visualization. To help you choose the right fonts for your data graphics, consider clarity, legibility, and consistency with the data story and tone. Too many fonts or overly decorative or informal ones should be avoided. Additionally, pay attention to font size, weight, alignment, and spacing; adjust them for readability and balance. Finally, test your fonts for compatibility, resolution, and scalability, and provide alternatives or fallbacks for different devices and formats.
The most important thing about font selection for data graphics is to use a monospace font for all numerical values. All symbols in such a font have the exact same width, which facilitates order of magnitude comparisons. Monospace fonts also tend to be more legible. Finding one that pairs well with the proportional font used for texts can be a challenge though. Use tools like Google Fonts to help your selection process.
A few of the techniques I use: 1?? Select highly readable fonts that are easy to skim and interpret. Most companies that leverage effective data visualizations use sensor typefaces such as Roboto, Open Sans and Proxima Nova. These fonts are easy on the eyes and help your viewers focus. 2?? Use bold fonts only for your headings and emphasis points, not the entire charts, as that can flatten the layers of your charts and make them difficult to interpret. 3?? Avoid thin or highly creative fonts. They might look cool, but most likely your audience will find them distracting and hard to see. Remember, fonts might not be part of your core message but they can help you deliver better stories.
Colors and fonts are not only functional but also expressive. When used correctly, they can help you create a visual identity and style for your data story and audience. To ensure the colors and fonts chosen match your data story and audience, consider the following questions: What is the main message or goal of your data story? Who is the intended audience and what are their preferences, expectations, and needs? What is the tone and mood of your data story and how do you want your audience to feel? What is the context and format of your data story and how will it affect your color and font choices?
Here are some steps to consider Understand your data story and audience: Before start matching colors and fonts, you need to have a clear understanding of data story and audience. Think about the message you want to convey and whom you want to convey it. This will help you choose colors and fonts that are appropriate and effective. Use colors strategically: Use colors to highlight key points in visualization and to guide the viewer's eye to important information. Avoid using too many colors Choose fonts that are easy to read: When choosing fonts, make sure they are easy to read and that they complement the tone and message of viz. Use font size and weight to highlight: Use font size and weight to highlight important information in viz.
Colors and fonts are not static, but dynamic. They can change and interact depending on the data, the graphic type, and the design. To make the most of colors and fonts in data graphics, you should use them to create contrast, hierarchy, and emphasis to avoid visual clutter and noise. At the same time, strive for harmony, coherence, and consistency to prevent visual confusion and conflict. Additionally, use colors and fonts to create variety, interest, and appeal in your graphics to prevent visual boredom and monotony. Lastly, colors and fonts should be used to create feedback, interaction, and engagement in your graphics to avoid visual detachment and indifference.
Have in mind colour blindness, green and red are frequently used for contrast, but switching to blue and orange instead is a good idea as the contrast will be seen by everyone
Also, Having a consistent color pallet for your visualizations is essential. For example, if you show a particular attribute as green in one of the slides/presentations/documents, it is always good to consistently show it as green throughout the presentation for the audience to have less confusion about the metric
You can also take advantage of your company's color palette if they have one. Using a pre-defined color palette ensures that slides look uniform, and it makes it easy to create a template that you and your colleagues can use for multiple presentations.
More often than not, looking at obvious colour palettes or font options is the best technique. It is quick to implement, and also has the added benefit of easy adoption by users because of the familiarity factor. It gives their brain more bandwidth to focus on the actual insights rather than train it for the new more creative colours and fonts. The ultimate aim of visualisation should be to support the data story and not overpower it.