When done right, data visualization effectively translates complex data into easily understood visuals in the form of graphs, charts, maps, plots, animations, and more.
Data visualization is the representation of data through the use of common graphics, such as charts, plots, infographics, animations, and others. These displays of information are used to communicate complex data relationships and data-driven insights in a way that is easy to understand. In this article, Charles Mahler discusses seven best practices for data visualization which include:
- Have a goal in mind.
- Choose the right visualization tool.
- Choose the right type of visualization.
- Use succinct labels and titles.
- Choose the right colors.
- Avoid clutter and unnecessary elements.
- Use data that is clean and up to date.
Mahler also offers some criteria for good data visualizations. Specifically, good data visualization should:
- Show complex data connections in a way that is easy to understand, clear, concise, and clutter-free.
- Enable your audience to quickly grasp the key points you are trying to communicate.
- Effectively communicate the information and ideas in the data using the right visual elements.
- Consider the needs of various audiences, while being accessible and inclusive by using clear and legible fonts and text sizes.
- Be simple and straightforward without unnecessary elements or distractions.
- Be based on accurate, current, and reliable data.
For details, please read the source article.
FULL STORY: 7 Best Practices for Data Visualization
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