How do you use design thinking to visualize your performance data?

1 Understand your audience

The first step of design thinking is to empathize with your audience and understand their needs, goals, expectations, and challenges. You need to know who will use your data visualization, what they want to learn from it, how they will interact with it, and what actions they will take based on it. To do this, you can conduct interviews, surveys, observations, or personas to gather insights and feedback from your audience.

2 Define your problem

The next step is to define your problem or opportunity clearly and concisely. You need to articulate what question or issue you want to address with your data visualization, what data you have available, what constraints or limitations you face, and what value you want to deliver to your audience. You can use a problem statement or a point of view to summarize your problem and guide your design process.

3 Ideate your solutions

The third step is to generate as many ideas as possible for your data visualization. You need to explore different ways to present your data, tell your story, and convey your message. You can use brainstorming, sketching, mind mapping, or other techniques to stimulate your creativity and divergent thinking. You can also look for inspiration from existing examples, best practices, or trends in data visualization.

4 Prototype your designs

The fourth step is to turn your ideas into tangible and testable prototypes. You need to create low-fidelity or high-fidelity versions of your data visualization, depending on the level of detail and feedback you need. You can use paper, digital tools, or code to create your prototypes. You can also use mockups, wireframes, or storyboards to show how your data visualization will look and function.

5 Test your results

The final step is to evaluate your prototypes and collect feedback from your audience. You need to test your data visualization for usability, clarity, accuracy, relevance, and impact. You can use methods such as user testing, interviews, surveys, or analytics to measure and improve your data visualization. You can also iterate and refine your design based on the feedback and data you receive.

Using design thinking can help you create data visualizations that are not only aesthetically pleasing, but also meaningful, engaging, and persuasive for your performance reporting. By following these steps, you can ensure that your data visualization meets the needs and expectations of your audience and delivers the value and insights that you want to share.

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?