Good data visualization is the key to sharing information with stakeholders in an effective manner. However, we often make certain mistakes or leave out certain elements from the visualization. Due to this, we often fail to share the intended information easily and effectively. In this article, we will discuss some of the best practices for data visualization that will help you create charts and graphs that convey information to the audience in a better manner.
- What Are The Data Visualization Best Practices?
- Keep The Audience in Mind While Creating Visualizations
- Keep The Design Simple
- Use Consistent Colors and Symbols
- Do Not Neglect The Context
- Don’t Focus Too Much on Aesthetics
- Don’t Misrepresent The Data
- Use The Right Type of Visualization
- Always Try to Tell a Story
What Are The Data Visualization Best Practices?
Data visualization is an effective way to communicate complex information in a clear and engaging manner. Best practices in data visualization are heuristics that we can follow to make our visualizations more effective. In this article, we will discuss the following best practices for data visualization.
- Keep the audience in mind while creating visualizations.
- Keep the design simple.
- Use consistent colors and symbols.
- Do not neglect the context.
- Don’t Focus too much on aesthetics.
- Don’t misrepresent the data.
- Use the right type of visualization.
- Always try to tell a story.
Let us discuss each of these data visualization best practices one by one.
Keep The Audience in Mind While Creating Visualizations
Before designing any visualization, you must consider who will be viewing it and what insights they are seeking. I consider this as the first element of data visualization best practices. You need to tailor the visualizations according to the audience. For instance, an audience consisting of researchers will always want a dense visualization having a lot of information.
On the other hand, politicians are very likely to just see the graph once. So, it is really important to convey your message in both cases. If you are presenting the visualization to people who want deeper insights, you can use dense visualizations. On the other hand, if you are presenting to a person who won’t even look at the visualization completely, you should design it in such a way that it still conveys the message you want to convey. To understand how to design a visualization, you can read this article on the data visualization wheel by Alberto Cairo.
Keep The Design Simple
While working with visualizations, we often try to include elements that contribute no information to the visualization. They are just for decorative purposes. Having more decorative elements in the visualization may lead to distracting the viewer from the original information that we want to convey. Hence, it is important to make sure the intended audience is able to understand the visualization without any distractions.
For this, you need to keep the design simple and yet decorative. Always avoid cluttering your visualizations with excessive data or unnecessary elements. You should focus on the key message and use visual cues, such as color, size, and position to highlight important information instead of non-functional decorative elements. I suggest you read this article on how to avoid chart junk to understand how you can create simpler visualizations.
Use Consistent Colors and Symbols
Using inconsistent colors or symbols may lead to confusion while looking at a visualization. Colors can enhance visual appeal and convey meaning. However, we need to use it judiciously. You should always choose a color scheme that is easy on the eyes and ensures sufficient contrast. In the visualization, we also need to use fonts and text sizes that are easy to read. Labels, titles, and axis markers should be clear and legible.
Do Not Neglect The Context
For the audience to understand the visualization correctly, they should know the context. Hence, always try to provide background information for the audience to understand the relevance of the data shown in the visualization. Don’t try to serve the audience with an isolated visualization with no context. For instance, consider the following visualization.
In the above image, the host is probably showing the forecast of temperatures for each day of the week. However, there is no context about what the narrator is trying to convey. If a person looks at this visualization, they will never be able to identify if this graph represents temperature or wind speed or the number of accidents, or anything else. Hence, providing the context with the data is really important.
Don’t Focus Too Much on Aesthetics
Creating a visually appealing chart is important as it lasts longer in the audience’s memory. However, we should never sacrifice functionality for aesthetics. We should create a visualization that is easy to understand. Hence, try to create visualizations that are efficient as well as visually appealing. To understand why creating efficient visualizations with minimum decoration is important, you can read this article on data-ink ratio.
Don’t Misrepresent The Data
We should always try to make sure that the data conveys the truth correctly. There should be no scope for misinterpretation. For instance, consider the following example.
In the above image, you can observe that the percentage of Americans who have tried marijuana in three different years is shown in a pie chart. Now, a pie chart is used to show percentages of a whole and represents percentages at a set point in time. Due to this, the audience may mistake the visualization showing the following information.
- All the people participating in the survey tried marijuana.
- 51 percent of the population tried marijuana today.
- 43 percent of them tried it last year.
- 34 percent of them tried marijuana in 1997.
However, the reality is entirely different. The graph is trying to show that
- Today, 51 percent of the total population has tried marijuana. 49 percent of them haven’t.
- Last year, 43 percent of the total population tried marijuana. 57 percent of them didn’t.
- In 1997, only 34 percent of the total population tried marijuana. 67 percent of them didn’t.
To avoid these kinds of misinterpretation, we should always choose a suitable chart type to show the data. For example, if we use bar charts in place of pie charts in this visualization, no such confusion will occur.
Use The Right Type of Visualization
In the previous example, you can observe the misinformation arising due to using pie charts instead of bar charts to represent the data. Hence, choosing the right type of visualization is really important. Each visualization tool has its own advantages and disadvantages. You should always choose the visualization elements that best suit the data and the information you want to convey.
Always Try to Tell a Story
The human mind can remember stories and patterns longer than numbers. Hence, you need to create a narrative flow in your visualization to guide the audience through the data. You should arrange the visual elements logically and provide a clear sequence that leads to the key insights or conclusions. This will help you keep the audience engaged while presenting the visualization and they might remember the takeaways for longer periods of time.
In this article, we discussed different data visualization best practices. To learn more about the data domain, you can read this article on data science vs software engineering. You might also like this article on is data science hard to learn.
I hope you enjoyed reading this article. Stay tuned for more informative articles.
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