How can you ensure your visualizations match your organization's style guidelines?

1 Find out the style guidelines

The first step is to find out what style guidelines your organization has for data visualization. You may have a dedicated document or website that outlines the specifications, or you may have to ask your manager, designer, or marketing team for guidance. If you are working on a specific project or report, you may also have to follow some additional or customized requirements. Make sure you understand the purpose, context, and audience of your visualization, and how it fits into the overall strategy and vision of your organization.

2 Choose the right tool

The next step is to choose the right tool for creating your visualizations. Depending on your needs and preferences, you may use a software application, a web-based platform, or a programming language. Some examples are Excel, Tableau, Power BI, Google Data Studio, R, or Python. Each tool has its own features, advantages, and limitations, so you need to select the one that suits your project and skill level. You also need to check if the tool supports the style guidelines of your organization, such as the color palette, font family, logo placement, and chart types.

3 Apply the style elements

The third step is to apply the style elements to your visualizations. Style elements are the components that define the appearance and layout of your visualizations, such as titles, labels, axes, legends, grids, backgrounds, and borders. You need to follow the style guidelines for each element, and make sure they are consistent, clear, and aligned across your visualizations. For example, you may need to use a specific font size and color for your titles, a certain format and position for your labels, and a standard width and margin for your charts.

4 Use the appropriate chart type

The fourth step is to use the appropriate chart type for your visualizations. Chart types are the shapes and forms that represent your data, such as bars, lines, pies, maps, or scatter plots. You need to choose the chart type that best suits your data type, analysis goal, and message, as well as the style guidelines of your organization. For example, you may need to avoid using pie charts if your organization prefers bar charts, or use a map if your organization has a geographic focus. You also need to avoid using too many or too complex chart types that may confuse or distract your audience.

5 Test and refine your visualizations

The final step is to test and refine your visualizations. Testing and refining are the processes of checking and improving your visualizations for accuracy, clarity, and impact. You need to test your visualizations for data quality, visual errors, and accessibility issues, such as missing values, overlapping labels, or color blindness. You also need to refine your visualizations for readability, simplicity, and aesthetics, such as removing unnecessary elements, adjusting the scale and range, or adding highlights and annotations. You can use various tools and techniques to test and refine your visualizations, such as data validation, feedback, or design principles.

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?