An introduction to data visualization

Understand how to tell a story using data and design

What is data visualization?

Data visualization is the visual and graphic representation of data through charts, graphs, maps, interactive dashboards, and other visual imagery. Data visualization can help simplify large or complex datasets to encourage broader interest on an issue or topic. Common types of data visualization include pie charts, bar charts, maps, tables, and line charts. Dashboards or infographics may feature multiple data visualizations.

Why is data visualization important?

Data visualization can facilitate shared understanding. Visualizing data supports the rapid interpretation of information; reveals patterns, trends, and relationships; and provides evidence-based insights to improve decision-making.

There are no firm or set rules for how to create or design data visualizations, but there are recommendations and best practices that can help ensure a visualization communicates effectively. Effective data visualizations are:

  • Appropriate for the intended audience
  • Accurate in the presentation of data and its meaning
  • Actionable, because the information is clarifying or useful

How can I create data visualizations?

When you create a data visualization, your goal should be to transform data into actionable information. This process can take many forms, but making a visualization typically involves the following seven steps.

Step 1. Establish and understand the research question

A clear, focused research question will guide the scope of your analysis. You may need to break down your research question into multiple components, which may be more clear to address in more than one visualization.

Note

The 18F User Experience Guide provides examples of research questions. While centered on user research, you can use the definition and principles to guide your own brainstorming.

Step 2. Identify data sources

Determine if you can get data that will address your question. You may need to use multiple data sources to validate and cross-check, to get a holistic view of the subject.

Step 3. Clean and explore your data

Verify your data is complete, consistent, and standardized. To ensure consistency, establish clear data strategies and governance, use standardized procedures and data dictionaries, and invest in data quality tools. This will allow you to carry out a comprehensive analysis.

Step 4. Analyze the data and choose a visual

The chart type you use should support your research goal and fit the type of data you are showing. For example, pie charts are useful to emphasize proportions between a few categories that are parts of a whole. Bar charts, meanwhile, are ideal for displaying data in categories and effective at showing comparisons across multiple categories.

Step 5. Check your visualization for bias

Identify any limitations in the way the data was collected. Approaching visualization with a lens of empathy and equity can ensure visualizations are more well-justified, accurate, and impactful.

Step 6. Ensure that your data is accessible

Carefully consider the language, icons, colors, and category order you use. Design your visualization with a focus on accessibility from the start to ensure everyone can understand and navigate your data.

Step 7. Identify and customize your message or story based on your audience — and get feedback

Help your audience understand your intent by including enough metadata (such as title, caption, and labels) for them to follow the information in your visualization. Use plain language for clarity. You can also test your visualization: ask someone from your intended audience (preferably) or a colleague (if needed, or with early drafts) to review your visualization. Gathering feedback can help you understand if people will be able to grasp the story you are trying to tell.

What can I do next?

Explore resources on data visualization, design, and analytics on Digital.gov.

You can join the Digital.gov Web Analytics and Optimization Community of Practice to connect with government professionals and learn best practices, tips, and tools for web analytics, search, and search engine optimization.

You can also join the Digital.gov User Experience Community of Practice to connect with practitioners interested in producing efficient, effective, and useful user experiences and technologies.