Data Visualization Design Process: A 4-step Journey presented by Andy Kirk
Over the past two weeks, I have presented two very interesting processes for creating a data visualization: the one proposed by Ben Fry presents a process from data acquisition to the creation of the graph itself, but disregards important design issues; on the other hand, the one proposed by Lisa Charlotte Muth, which is equally important, adds design issues, but does not take into account other important issues such as stakeholders.
These issues only show something that the author I will be talking about this week (Andy Kirk) has said in the past: Creating data visualizations is a process that can be both iterative and challenging. In fact, Andy Kirk’s process can be seen in his book Data Visualisation: A Handbook for Data Driven Design, in which Andy proposes a structured design approach that guides the designer from the initial objective to the publication and review of the final creation, although the creation process may seem chaotic at times — when we are actually going to build a visualization (as Andy himself states).
So why should I learn this process, Antonio? Simple: This process was used by Andy to create “The Pursuit of Faster,” which received an honorable mention in Visualizing.org’s Summer 2012 Olympics competition. What better way to demonstrate that following this process can lead to good results? Before we dive into the process itself, however, it’s important to highlight the three design principles that the author advocates.
1. Trustworthy: The visualization should be accurate and based on reliable data.
2. Accessible: The design should be clear and understandable to the intended audience.
3. Elegant: The visualization should be aesthetically pleasing without compromising functionality.
With that said, let’s talk about Andy Kirk’s process…
1. Formulating your brief: Establish the purpose and identify the key factors
The first step in the design process is to clearly establish the purpose of the visualization. To do this, it is essential to understand the purpose, target audience, stakeholders, and topic. With these elements in mind, we can formulate the “Central Curiosity”, a question that summarizes the main problem that the visualization seeks to address. By clearly defining what we want to achieve, we better direct the design process, ensuring that the final visualization meets the expectations and needs of the stakeholders.
The essence of formulating the brief is to “identify the context in which your work is carried out and then define its purpose: the who, what, where, when and how”. This is the phase in which you begin to ideate your work, and this phase can be formal or informal.
Valuable tips that should not be overlooked in this phase:
• Keep a notebook: Recording the thoughts and steps of the process is crucial to reflect on the work and improve in future projects.
• Clearly articulate the project’s purpose and intended effect: Defining what you hope to achieve with the visualization is critical to measuring the project’s success.
2. Working with Data: Acquisition, Preparation, and Exploration
Once you’ve defined your purpose, the next step is to work with your data to find insights. This process is divided into four steps:
• Data Acquisition: Identifying and collecting the data needed to answer your Core Curiosity.
• Data Examination: Understanding the physical properties and meaning of the data you’ve collected, including data types, field definitions, and value ranges.
• Data Transformation: Modifying, adding, or removing data as needed to fit the context and purpose of your project.
• Data Exploration: Analyzing your data to uncover interesting insights or raise new questions that can be explored in your visualization.
3. Establishing Editorial Thinking: Editorial Focus on Visualization
Editorial thinking refers to the process of curating, organizing, and presenting content in a way that is engaging, informative, and easy to understand for the intended audience. It involves making decisions about what content to include, how to structure it, and how to present it in a way that effectively communicates the intended message. This concept is often used in journalism, publishing, and content creation. As the author himself pointed out:
“Without context, you’re just looking at numbers or graphs. But with context, these numbers and graphs tell a story.”
In this step, Andy highlights the importance of three key elements: angle, framing, and focus.
• Angle: This defines how we will present the data, ensuring that we show what is relevant to the audience and that we respond to the Central Curiosity.
• Framing: Just as a photographer decides what to include or exclude in a photo, we need to filter the data to show only what is relevant.
• Focus: Even with a small amount of data, the visualization can be confusing if there are distractions or poor organization. In this step, we aim to reduce the “noise” and highlight the most important information.
4. Developing the Design Solution: Develop Your Design Specification
In this step, the visualization author’s concern should be to transfer the hidden thinking to the “visible” thinking. So, Andy introduces us here to elements of visual coding and discusses when we should resort to interactivity or not. Because of this, Andy introduces us to the concepts of marks and channels.
• Marks: Visible elements that represent data, such as points, lines, or areas. These are the elements that will characterize the type of graph itself. What will your graph be visually characterized by? Lines? Words? Circles? Points?
• Channels: Variations applied to marks to differentiate them, such as size, color or shape. Each channel has different degrees of effectiveness, depending on the type of data we are working with. For example, using the size of a circle to indicate magnitude is intuitive and effective, while using colors may be more appropriate to differentiate categories.
A valuable tip that should not be overlooked: Use interactions only when necessary: Some of the best visualizations are static. Interaction should only be added when it truly contributes to the understanding of the data.
After these steps, the work of building, testing, feedback, and revising the visualization you have built follows.
Final Thoughts
Andy Kirk’s data visualization design process offers a structured and effective approach to transforming raw data into valuable, visual insights. By following the steps and principles outlined, you can create visualizations that not only communicate complex information clearly but also engage your target audience in a meaningful way. By maintaining a clear editorial focus, working carefully with the data, and developing an elegant design solution, any designer can achieve impactful results in their data visualization projects.
But believe me: that’s not all! Still on this week, on my Linkedin, I’ll be writing a post introducing a very interesting aspect of Andy’s work: the archetypes and roles of data visualization creators and how they interact with the steps of the process. Until then, tell us: of the processes presented so far… which one did you like the most? Which one most resembles your development process?