Have you ever looked at a chart and thought to yourself "Something’s not right here"? You’re not alone. As leaders, it’s highly likely that you encounter data visualization that is confusing or doesn’t quite convey the data or underlying message in an effective way. Good data visualization turns facts and figures into visual stories and this helpful tool is popular for a good reason: half of our brain's capacity is dedicated to visual processing. Data visualization is a crucial tool for communicating insights and patterns, particularly when it involves decision-making. In this article, we explore examples of good and bad data visualizations, and what we can learn from them.
Good: Inbound Marketing Job Trends Graph
Image Source: Direction
This bar chart clearly illustrates the trends in the digital marketing job market. You have good use of coloring, solid labels, comparative data sets, and annotations that convey meaning. This data presents only the essential information and key variables, leaving no room for confusion.
What you can learn from this: In many cases, using straightforward graphs or charts is the way to go. By simplifying visuals and concentrating on the core data, you not only enhance comprehension but also, you ensure that the most important takeaways are apparent to your audience. This is particularly necessary when dealing with complex data sets or, like this case, showing trends.
Bad: Major League Soccer Salaries Chart
Image Source: Tableau
The way Major League Soccer players' salaries are visualized here is nearly impossible to read. While the chart is interactive, the volume of data and overwhelming variables complicate the visual experience. Despite having a legend, the chart uses acronyms excessively, making it difficult to follow. The interactive filtering is convenient on dedicated pages, (and on desktop), in this instance, you need to hover over cells for specifics, making it difficult to use on mobile.
How you can avoid this: The first question to ask is: How can I subcategorize this day to make it clear? The MLS should have had a distinct page for each state or an executive summary with key trends. Always prioritize the data you need, and dump the data you don’t (or drop it in the appendix if absolutely necessary). Another tip is to order the variables logically, whether numerically or sequentially. According to Tableau, it’s challenging to understand a visualization if a pattern doesn’t make sense. The aim is to guide understanding without overwhelming the audience with information.
Good: Mailchimp’s 2020 Annual Report
Image Source: MailChimp
This data tells a story. What makes this infographic so compelling is the surprising presentation of data within an office scene. The information is clear, the font is legible and the colors are vibrant. But what sets MailChimp apart and makes them a model for how to do annual reports is their interactive storytelling. In their 2020 report, users can journey through it, greeted by vibrant animations. It's not only easy to understand but also captivating to go through.
What you can learn from this: Reflect on and document the story you are trying to tell. To make your data visualization engaging, plan a user-friendly journey as part of your story. Where relevant you can use animations or transitions but this should be used in very specific scenarios (i.e. designing an activation, an exhibit, or a portal) and there should always be a documented (un-interactive) version of the data to be used for regular reporting where efficiency matters most.
Bad: TitleMax’s Map of Disney’s Worldwide Assets
Image Source: TitleMax
At first glance, this Disney assets map falls into the trap of overwhelming information (much like the MLS salaries chart). Yet, its issues run deeper. This chart doesn’t effectively use design elements such as font and circle size. There are multiple different shapes within the big circles, such as rectangles and squares, which can confuse users. Also, this chart doesn’t follow basic grouping rules. It doesn’t present the essential information quickly, and groups variables based on importance.
How you can avoid this: Prioritize clarity and effective grouping. Grouping variables based on criteria helps guide the reader's understanding and form a narrative that they can easily follow. Use design to tell a story that guides your viewers, from start to finish. This helps you keep people interested and leads them to what you want them to know.
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How you visualize data can affects how people understand it. When you visualize it accurately and tell a story, you convey meaning. However, if you visualize it poorly, you can change the message and lose the data's value.
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