There are various techniques and tools you can use distill complex data into digestible and actionable insights.
We are constantly inundated with data that demands our attention and comprehension. From avoiding traffic jams to tracking COVID cases, data has undoubtedly improved our personal lives in many ways. But valuable data is not inherently useful data. For data to be useful, it must be comprehensible and then actionable. Technical visual storytelling (often called data visualization) is a way to convey complex concepts and ideas clearly so the intended audience can act. In my experience, I’ve found that you first need to understand your audience.
Effective technical storytelling starts with empathy. When I first started studying music, I took lessons and failed. I failed to comprehend the notes on the page, failed at subdividing beats, and failed to understand the differences between a major and a minor third. I could instinctively understand the concepts but failed at bridging the gap between instinct and practical knowledge. These failures were not entirely the result of an inability to comprehend music theory. I partly failed because the story behind the data of music theory was obscured by expertise. I read books and took lessons that were not truly designed with a beginner in mind. This experience changed after learning from teachers who combined empathy with ability. These individuals explained music theory in a way that lifted the curtain behind the complexity. Although experts, they remembered what it was like to be a beginner.
Similarly, technical/visual storytelling is successful when you understand your audience and shape your data into a story that communicates with that specific audience. Consider the following questions to help you connect with your audience’s perspective:
- Who is my audience?
- What level of knowledge do they already have on this topic?
- How does my audience feel about this topic?
- What is the goal of the story I’m telling?
- What should the audience take away from the story?
Having the expertise to gather accurate, robust data isn’t enough; you must put yourself in the audience’s position and communicate the story within the data without overwhelming.
Mastering the three-step process to effective data visualization. Crafting your graphic is a multi-step process that includes:
- Planning. Once you understand your audience, you can determine how best to convey the story you want to tell. Sometimes, a simple bar chart, line graph, or table is the most concise way to communicate the message. When a process is complex, a more artistic approach may be needed. While most are familiar with the standard pie, bar, and line charts, additional data visualization types may better suit your graphic. Graphic types include:
- Treemaps. Visualizations that use rectangles of varying sizes to compare quantities. With treemaps, you’re able to nest categories and subcategories of data.
- Animations/motion graphics. Adding motion to data can increase comprehension by controlling where viewers focus their attention.
- Block diagrams. Diagrams showing underground topography.
- Data maps. Visuals that combine geographical and numerical information.
- Interactive graphics. Any visuals that invite and react to reader interaction.
- Story map/experience builder. This is an ESRI product that allows the creation of a large-scale, often scrollable graphic on a website requiring minimal coding.
- Technical illustrations. Renderings of a project, structure, or process that sometimes ignore the boundaries of reality in service of a story.
Drafting. Once you’ve planned the format of your graphic, it’s time to put a draft together. A draft can take many forms, from very rough to more polished. If you’re providing an idea to a designer, your draft may be a simple drawing with pencil and paper. If you are the designer, then maybe you feel comfortable jumping right into mocking something up digitally from a picture you have in your head.
Even a vague idea is enough to get started. Drawing skills are not necessary at all. What is essential is trust between the expert and the artist. Both parties need to be able to communicate freely so that the best ideas come out. Often, I will start sketching in real-time as people describe their data. These sketches are typically horrible, but the real-time feedback that is generated is essential to the process of creating a compelling visual story. It causes the subject-matter expert to think through their thinking.
If you’re asking yourself, “Well, what does that mean?” Thinking through your thinking is like organizing an unruly garage where only one person can find last year’s Christmas ornaments. The location that makes perfect sense to the organizer may be utterly illogical to another. As an artist sketches and the subject-matter expert reacts, a comprehensible data visualization begins to take shape. The discrete elements of rich data and expertise can be arranged, rearranged, shaped, and reshaped into a visual story that is coherent and meaningful to others.
Editing and refining. Seeing the first draft of your idea come together is usually exciting because you are starting to see the story behind the data come to life. This is also when you may see some opportunities for editing. Editing and refining is part of a circular process and vital step to help eliminate extraneous information that doesn’t support your story. It will also highlight the information that is most critical to ensuring your audience understands the message. Don’t skip or skimp on this step! You might go through multiple rounds of revisions, tweaking to strike the right balance between having enough detail to ensure comprehension and not having so much detail that your message is unclear.
Editing requires empathy. Edit for your audience. Empathize with the person watching your presentation or engaging with your content. Remove layers of data one by one and ask if the story is still intact. If it is, maybe you don’t need that data point. Constantly ask yourself if the story being told is worthy of the time the viewer spends engaging with it.
Effective data visualization is a conversation, iteration, and refinement process where your expertise is molded into a cohesive visual message. All ideas – even “bad” ones – could lead to insights and should be considered.
Communication is key. Distilling complex data into digestible and actionable insights requires empathy. There are various techniques and tools that can be employed to convey complicated data points to end users effectively. It is critical, however, to understand your intended audience and its goals, choosing the most impactful mode of communicating important data without it becoming overwhelming. Once you identify the best avenue for sharing the data, maintaining clear communication between the expert and the designer facilitates the planning, drafting, editing, and refining stages of the process as you work toward the action you hope to inspire or the end goal for sharing the data.
Raoul Rañoa is Dudek’s technical storytelling practice manager. He has provided data visualizations to the Jet Propulsion Laboratory and infographics to the California Institute of Technology and served as senior graphics and data journalist in the Los Angeles Times Data Visualization Department for 19 years. He can be reached at firstname.lastname@example.org.