How can you visually communicate data in graphics?

1 Know your data

Before you start designing, you need to know what kind of data you have, what message you want to convey, and who your target audience is. Different types of data require different visualizations, such as bar charts, pie charts, line graphs, scatter plots, or heat maps. Consider the context and purpose of your data graphic, and how it fits with your overall story or narrative. Understand your audience's level of knowledge, interest, and expectations, and tailor your graphic accordingly.

2 Choose the right format

Once you have a clear idea of your data and your goal, choose the right format for your graphic. There are many factors to consider, such as the size, shape, orientation, and resolution of your graphic, as well as the medium and platform where it will be displayed. Think about the balance between simplicity and complexity and how much detail and information you want to include. A good format should match your data type, highlight your key points, and fit your design style.

3 Use colors and fonts wisely

Colors and fonts are essential elements of graphic design, as they can create contrast, hierarchy, harmony, and emotion. However, they can also be distracting, confusing, or misleading if used incorrectly. When choosing colors and fonts for your data graphic, it is important to follow some basic principles. Use a limited and consistent color palette that suits your theme and tone. Colors should be used to differentiate categories, emphasize trends, or show relationships; avoid using too many colors or colors that are hard to distinguish. Fonts should be clear, legible, and compatible with your data and message. They can create hierarchy, contrast, and alignment, although too many fonts or fonts that are too fancy or decorative should be avoided.

4 Add labels and legends

Labels and legends are essential for data graphics, as they provide context, explanation, and clarification. Without them, your audience may not understand what your graphic is displaying or how to interpret it. Labels and legends should be concise, accurate, and relevant and should adhere to some basic guidelines, such as using labels to identify axes, variables, units, titles, and captions without cluttering the graphic; using legends to explain symbols, colors, or shapes without placing them too far from the graphic; and using consistent and appropriate terminology, punctuation, and capitalization for your labels and legends.

5 Check for accuracy and clarity

The last step in creating a data graphic is to review it for accuracy and clarity. Make sure that the graphic is not only visually appealing, but also truthful, reliable, and easy to understand. Avoid common errors, such as using misleading scales or axes that distort your data, using inappropriate or irrelevant data or visualizations that do not match your message, using confusing or ambiguous labels, or using too much or too little data. By following these best practices, you can create data graphics that are both beautiful and informative. Data graphics can help you communicate your data in a more engaging and memorable way, inspiring your audience to take action or learn more.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?