The latest episode of Data Chats featured a conversation between Chris Richardson and Jose Berengueres, a professor in design thinking, data visualization and computer science at the United Arab Emirates University. He is also the author of Data Viz and Sketch Thinking.
They discuss
- Why you should think beyond excel for creating graphs and charts
- The problems with pie charts and color decisions
- Why storytelling isn’t a skill every data scientist possesses.
The key takeaways from the conversation are outlined below, or listen to the full episode.
Key Takeaway #1: Don’t Choose the Wrong Tool
The biggest mistake in data visualization is choosing the wrong tool.
Many charts are crafted in Microsoft Excel. While this tool is brilliant, it’s not easy to customize colors or easily tweak the information to ensure it’s not overloaded.
Choosing Excel makes the task of data visualization unnecessarily complex and doesn’t have some of the data visualization capabilities other softwares feature
For example, Tableau has done a fantastic job at providing better color palettes and cleaner interfaces. As a result, many companies are adopting this platform to make their charts, screenshot the design and then paste them into PowerPoint presentations.
Some organizations opt to use a tool like Excel to build a chart based on the data, but then finish the work with a design tool like Adobe Illustrator. This works because the problem with Excel isn’t creating accurate charts—it’s creating appealing visuals.
Key Takeaway #2: Know the Purpose of Your Chart or Graph
Successful data visualization requires knowing what you’re trying to accomplish.
You need a specific answer to the question, “Who’s going to read the chart?” If you don’t know the audience, then it’s probably best to stop what you’re doing until you do know who it is you are targeting.
Is it for yourself or the data team to better understand the problem, or is it meant to influence someone else? If it is the latter, then the more complex the chart is the less likely it’ll be understood by the audience.
Key Takeaway #3: Put Down the Pie Chart
We love round shapes because they are harmonious. But we should put away the pie chart because they are usually bad at making data easier to understand for a couple of reasons.
The first problem is that pie charts have angular slices. So, if you rotate, the audience’s perception might be different. Most people are more likely to quickly notice differences in rectangular shapes. Additionally, you might struggle to connect labels to the right slices.
Second, pie charts unconsciously create a scarcity narrative. Pie charts are the go-to for showing budgets. So, you’ll often find them describing expenses and revenue in annual reports.
In addition to being hard to understand, they also tell a story about limited resources. So, when we are thinking about revenue, we’ve unintentionally created a ceiling to growth. Essentially, every department feels they have to fight for their “slice of the pie.”
Key Takeaway #4: Carefully Consider Your Color Choice
The other early mistake with data visualization is with color choice because colors are loaded with symbolism.
There are numerous books and blogs just about color psychology.
For example, in the United States, bright yellows evoke feelings of energy, intellect or happiness. Therefore, a chart demonstrating a spike in hospitalizations during the peak flu seasons could be unsettling in a bright yellow color.
But the symbolism of color changes in cultures, so knowing your audience is critical in color selection.
More commonly, the mistake we make with color is using too many. It’s best to start a chart in black, and then, if you must, choose one accent color to highlight a specific trend—that’s it.
If you choose to use more than one color, make a case for it to justify your decision. Because overloading a chart for no specific reason will make it harder for the audience to decipher meaning from your work.
Key Takeaway #5: Think Strategically About the Skills on Your Data Team
The problem with data visualization is that companies today hire so many data scientists with computer science backgrounds who don’t speak the same language as the non-technical stakeholders in an organization.
These data employees are great. They have a hard job just organizing the data and ensuring data privacy.
The mistake of management is to think that creating information from data is easy.
But creating knowledge from information—that’s not that easy. So, you have to hire the right data professionals or offer training.
This knowledge gap exists because the basics of graphic design and the art of storytelling aren’t core principles of most data curricula. So while there are expectations to create stories from the data, they’ve never been given the tools or the background to do so.
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