Common Data Visualization Mistakes
Data visualization allows readers to quickly analyze and interpret data. But how can we build visualizations that correctly communicate accurate information about our data? By understanding what people perceive when they observe a visualization, we can design them in a way that leads to truthful analysis while avoiding biases. In this article, we will cover common data visualization mistakes that may hinder successful analysis and how to avoid them.
Keep it Simple
First, it is important to discuss how data visualization errors come to be. In the same way it is important for the visualization creator to keep the reader in mind, it is equally important for the reader to think about the motivation of the person building the visualization. For example, often visualizations are overly flashy and attempt to draw in the reader through engaging imagery and appealing colors. The creator wants readers to be drawn in, which is understandable, but flashy elements can distract from the real insights of the data, and at times, mislead. The following are visualization elements that can make a visualization more eye-catching but are best to avoid.
3D visualizations appear to have depth and look like they are jumping off the page. They can be very appealing to the eye, and they are included in visualization software such as Microsoft Office and Matplotlib for Python. The problem with 3D visualizations, however, is the way they can distort or hide the elements of the chart. For example, the bars in the rear of ‘Chart 1’ are obstructed by the bars in the front. The viewer does not have the ability to rotate the view to see the hidden elements and even if they did, rotating would hide other elements. Another reason to avoid 3D visualization elements is the way projection alters the sizing of the chart elements. 3D visualizations use sizing of objects to make things appear closer or further away, therefore achieving the illusion of three dimensions. However, this makes it more difficult for the viewer to determine size comparisons between bars or pie slices. For example, in ‘Chart 2’ and ‘Chart 3’ Indiana and Illinois both have $55,000 in sales but the sizing of the elements distorts this information.
When building a visualization, avoid 3D elements to ensure details are not hidden and components are sized in a way that is obvious to the viewer.
Too Much of a Good Thing
It can be tempting to add as much detail as possible to a visualization to make it look exciting and to give the reader all the data in one view. However, overcrowding is a real issue in many analytics dashboards, and it can hinder the quick and accurate data analysis for which visualizations are meant. While the line graph below looks colorful and exciting, it is nearly impossible to drill down to view trends at the state level due to overcrowding. Also, using color to distinguish between 50 states is sure to result in a very similar color for numerous states, making it even harder to distinguish what you are seeing.