How do you use data visualization software to communicate your findings?

1 Know your audience

Before you start designing your data visualization, you need to know who your audience is and what they want to learn from your data. Different audiences may have different levels of familiarity, interest, and expectations with your data, so you need to tailor your visualization accordingly. For example, if your audience is technical, you may want to use more complex and detailed visualizations that show the nuances and relationships in your data. If your audience is non-technical, you may want to use simpler and more intuitive visualizations that highlight the main insights and messages in your data.

2 Choose the right type of visualization

Data visualization software offers a variety of options for displaying your data, such as bar charts, line charts, pie charts, scatter plots, heat maps, and more. Each type of visualization has its own strengths and weaknesses, and you need to choose the one that best suits your data and your purpose. For example, if you want to compare the values of different categories, you may want to use a bar chart or a pie chart. If you want to show the trends or changes over time, you may want to use a line chart or a area chart. If you want to show the correlation or distribution of two variables, you may want to use a scatter plot or a bubble chart.

3 Use color, shape, and size wisely

Data visualization software also allows you to customize the color, shape, and size of your data points, which can help you enhance the visual appeal and the readability of your visualization. However, you need to use these features wisely and sparingly, as too much or too little can confuse or distract your audience. For example, you can use color to distinguish different groups or categories in your data, but you should avoid using too many colors or colors that are too similar or too contrasting. You can use shape and size to show additional dimensions or attributes in your data, but you should avoid using shapes and sizes that are too complex or too inconsistent.

4 Add context and clarity

Data visualization software also allows you to add context and clarity to your data by adding labels, titles, legends, annotations, and other elements that can help your audience understand and interpret your visualization. However, you need to balance the amount and the placement of these elements, as too much or too little can clutter or obscure your visualization. For example, you can use labels and titles to identify the variables and the units in your data, but you should avoid using labels and titles that are too long or too small. You can use legends and annotations to explain the meaning and the significance of your data points, but you should avoid using legends and annotations that are too far or too close to your data points.

5 Test and refine your visualization

Finally, data visualization software also allows you to test and refine your visualization by previewing it on different devices, formats, and platforms, and by getting feedback from your audience or peers. Testing and refining your visualization can help you identify and fix any errors, issues, or gaps in your data or your design, and ensure that your visualization meets your goals and expectations. For example, you can test your visualization on different screen sizes and resolutions to check if it is responsive and adaptable. You can test your visualization on different browsers and applications to check if it is compatible and functional. You can test your visualization on different audiences and contexts to check if it is clear and effective.

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?