Unraveling the Process of Creating Data Visualizations with Lisa Charlotte Muth

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Creating data visualizations is a complex art, and it involves much more than simply turning numbers into attractive charts. As Lisa Charlotte Muth, a renowned data journalist and data visualization expert, wrote:

“The process of creating a data visualization can be confusing: finding data sources, sketching a chart form, figuring out how to download data, analyzing the data, trying out a chart type, bringing the data into a different format to try another chart type, researching more data, discovering that the data doesn’t fit the article…”

So, in two enlightening articles (here and here), Lisa offers us a practical and effective approach for data analysts who want to improve their skills in creating impactful visualizations. Her method breaks down into three fundamental steps: defining the central point, constructing and emphasizing the chart, and interpreting and communicating the results.

1. Defining the Central Point: What’s Your Purpose?

The first step in Lisa’s process is perhaps the most critical: defining what you want to communicate with your chart. Before you start sketching or experimenting with different visualizations, it’s essential to think about the purpose of your graph. Questions like “What do I want to show?”, “Why is this graph necessary?”, and “What is the main message readers should take away from it?” should guide this initial phase.

Reflecting on the purpose of a data visualization is crucial, as different industries have different goals, as do the creators of these visualizations. Aligning personal goals with the goals of the field is essential, as it sets priorities and influences the time and effort dedicated to each task. If the main goal is aesthetics, there is a risk of sacrificing comprehension for the sake of a more sophisticated visualization. However, for those who prioritize comprehension, like the author, it is preferable to opt for a simple and effective solution, even if less visually impressive, because clarity in communication is vital. Still, when possible, it’s best to find a balance where beauty complements comprehension, increasing the depth and impact of the message conveyed.

For Lisa, the title of the graph is a powerful tool at this stage. It acts as a hypothesis, summarizing what will be shown and setting expectations for the reader. A good title not only guides the viewer’s eye but also reinforces the understanding of the central point of the graph.

2. Construction and Emphasis: How to Effectively Communicate Your Message?

Once the purpose is clear, the next step is to decide how to present the data to emphasize your message. This step involves several important decisions, from choosing the type of graph to selecting supplementary data, colors, and annotations. As the author herself writes: “If the title is our hypothesis, the graph is the proof of that hypothesis. Here, readers can see for themselves. They can verify that our title statement makes sense and may discover more insights beyond that.”

Conveying the point, you want in your graph comes in two parts. The first step is choosing a graph type for your data. This is a crucial decision. The second is how to enrich that graph. We will look at three possibilities: adding data, highlighting data with color, and annotating.

Choosing a Chart Type: Choosing a chart type is critical and should align with the point you want to make. Lisa emphasizes that this choice is so important that it deserves its article. The chart type should be chosen based on the nature of the data and the message you want to convey.

Adding Comparative Data: Lisa emphasizes the importance of adding comparative data to give context to the visualization. Comparisons can be made over time, across different regions or industries, or against global averages. This comparative perspective transforms a simple chart into a richer, more informative visualization, placing the data in a context that the reader can easily understand.

Using Color for Highlighting: Color plays a crucial role in guiding the reader’s eye to the most important parts of the chart. Lisa recommends using shades of gray for secondary elements and vibrant colors to highlight key points. This strategic use of color not only makes it easier to understand but also maintains the aesthetics of the visualization.

Visual Annotations and Highlights: Annotations, such as arrows, outlines, and highlights on specific areas of the chart, are valuable tools for guiding the reader through the visual narrative. They can provide additional information that is not immediately apparent in the data, answering questions that the chart alone cannot fully explain.

Here, it is worth noting that the author emphasizes that first impressions play a crucial role in any data visualization project, but understanding the data is equally, if not more, important. While beauty may initially attract, understanding is what truly ensures that the message is communicated effectively. However, the combination of understanding and beauty results in an even greater impact, as visually pleasing design can facilitate comprehension and create a more engaging experience. This perspective aligns with the thoughts of authors such as Francesco Franchi, who emphasizes the importance of inspiring, entertaining, and informing through visualizations, and Andy Kirk, who emphasizes the need to perceive, interpret, and understand data. Both share the view that aesthetics and clarity must go hand in hand to maximize the effectiveness of visualization.

3. Communication: What Does the Chart Really Show?

The last step in Lisa’s process is just as important as the previous ones: explaining what the chart shows. After defining the central point and building the visualization, it is necessary to ensure that the reader clearly understands what is being presented. Provide Accurate Descriptions: Lisa emphasizes the importance of clear and accurate descriptions to help the reader correctly interpret the graph. While the graph creator has a complete overview of the process, the reader is seeing the result for the first time and may need guidance to fully understand what is being shown.

Adding Captions and Sources: Captions are essential for labeling visual elements and ensuring that the reader understands what each part of the graph represents. Additionally, including sources is critical to the transparency and credibility of the visualization. Lisa emphasizes that sources not only prove the origin of the data but also allow others to use that information to create even better visualizations.

A process of interconnected steps… and not so far from Ben Fry’s process

Although Lisa Charlotte Muth breaks her process down into three distinct steps, she recognizes that they are deeply interconnected. Defining the purpose (step 1) directly influences the construction of the graph (step 2), and both determine how the final interpretation will be communicated (step 3). Each step is essential to create an effective data visualization that not only informs but also engages and convinces the reader.

When comparing the data visualization creation process proposed by Lisa Charlotte Muth with Ben Fry’s framework, we can observe some interesting similarities and differences. Lisa admits that her process is a simplification, with a greater focus on visual communication and less detail in the stages of research, cleaning, and data analysis. She confesses that these aspects, essential in Ben Fry’s process, are embedded in her second step, where the emphasis is on building the graph. However, she recognizes that, as in Fry’s model, all of these activities fundamentally depend on a clear purpose (step 1) and that, once completed, it is crucial to ensure effective communication of the final result (step 3). Thus, while Fry offers a detailed and segmented view of the data visualization process, Lisa provides a more holistic and communication-oriented approach, without losing sight of the intrinsic complexity involved in creating impactful visualizations.

For data analysts, understanding and applying Lisa’s process is a powerful way to hone your skills and create visualizations that truly matter. By following these steps, you’ll be better equipped to transform complex data into clear, actionable insights, communicating your findings in a visual and impactful way.