When I teach data visualization classes and workshops, I present the wide array of graphs, charts, and diagrams that are available to us when we communicate our data. I argue that these “non-standard” graph types can help us be more effective at communicating our data for at least two reasons: First, they are sometimes inherently better at showing the data than the standard chart types like bar, line, and pie charts. Second, they are sometimes more engaging or fun to look at and explore than standard charts, and sometimes engagement can be a goal of our work. If you’ve read my book Better Data Visualizations, this argument won’t surprise you.
One of the ways people push back against learning new graph types is that they say their manager, colleague, reader, or audience member will never understand how to read them. “This is how my boss always looks at the data,” they’ll say, “and they know where to look to find the number they need. They’ll balk if I use a different or new graph that they don’t immediately understand.”
One of the problems with this approach, however, is that what if something important is going on somewhere else in the graph, chart, or table? What happens if the manager immediately goes to the fifth bar chart in the report and ignores everything else? Different, more engaging graphs can sometimes help show your data to more effectively highlight patterns, trends, and values they might otherwise miss.
How do you break through the “this is how I always look at it” mentality? My experience has always been to show your reader or user the difference between the original and your new, proposed graph. If you can help them see the improvement from one visualization type to another, they will then understand the value of changing. And once they understand how to read the new plot type, it becomes part of their data visualization toolbox.
Take this heatmap from Nathan Yau at Flowing Data, which he published back in 2012. He uses a heatmap arranged as a calendar to show auto fatalities in 2012. Here, January is at the top and December at the bottom; Sundays on the left and Saturdays on the right. In my experience, most people can quickly and easily pick out the pattern of more deaths on the weekends. Some, but not all, can also see the seasonal change (more deaths in the summer months) relatively quickly as well.
Now let’s look at the same data as a more standard line chart with orange dots denoting Saturdays. Here, it’s almost impossible to get the ‘more deaths on the weekend’ story, though perhaps a bit easier to see the seasonal pattern.
In this case, the heatmap appears to be a better visualization: You can see the patters more clearly, it is easier to add labels and annotation, and, personally, I find it to be more pleasing to look at.
We are not born knowing how to read a bar chart or line chart or pie chart. We learn how to read those graphs through experience and exposure. The only reason why someone might not immediately like a slope chart, dot plot, connected scatterplot, or so many other graphs not immediately available in your tool’s drop-down menu is because they haven’t learned how to read it.
So, use good annotation in your graph. Use good, active titles to help them understand what they are supposed to learn from the graph. And include additional pointers, highlights, and other elements to help them understand how to read the graph and then how to understand the content delivered in the graph.
I’m a firm believer that we can help our readers and users move beyond the “this is how I always look at it” mentality to create and use a wider array of graphs, charts, and diagrams to enhance understanding and be more effective data communicators. Demonstrate how your new approach yields better results and greater insights to your data.
