Bad Data Visualization: 5 Examples of Misleading Data

Data visualization, the process of creating visual representations of data, offers businesses various benefits. One of the most powerful is the ability to communicate with data to a wider audience both internally and externally. This, in turn, enables more stakeholders to make data-driven decisions.

In recent years, a proliferation of data visualization tools has made it easier than ever for professionals without a data background to create graphics and charts.

While this has primarily been a positive development, it’s likely given rise to one specific problem: poor or otherwise inaccurate data visualization. Just as well-crafted data visualizations benefit organizations that generate and use them, poorly crafted ones create several problems.

Data Visualization Mistakes to Avoid

When generating data visualizations, it can be easy to make mistakes that lead to faulty interpretation, especially if you’re just starting out. Below are five common mistakes you should be aware of and some examples that illustrate them.

1. Using the Wrong Type of Chart or Graph

There are many types of charts or graphs you can leverage to represent data visually. This is largely beneficial because it allows you to include some variety in your data visualizations. It can, however, prove detrimental if you choose a graph that isn’t well suited to the insights you’re trying to illustrate.

Some graphs and charts work well for communicating specific types of information, but not others. Problems can arise when you try visualizing data using an unsuitable format.

The nature of your data usually dictates the format of your visualization. The most important characteristic is whether the data is qualitative (it describes or categorizes) or quantitative (meaning, it’s measurable). Qualitative data tends to be better suited to bar graphs and pie charts, while quantitative data is best represented in formats like charts and histograms.

2. Including Too Many Variables

The point of generating a data visualization is to tell a story. As such, it’s your job to include as much relevant information as possible—while excluding irrelevant or unnecessary details. Doing so ensures your audience pays attention to the most important data.

For this reason, in conceptualizing your data visualization, you should first seek to identify the necessary variables. The number of variables you select will then inform your visualization’s format. Ask yourself: Which format will help communicate the data in the clearest manner possible?

A pie chart that compares too many variables, for example, will likely make it difficult to see the differences between values. It might also distract the viewer from the point you’re trying to make.

3. Using Inconsistent Scales

If your chart or graph is meant to show the difference between data points, your scale must remain consistent. If your visualization’s scale is inconsistent, it can cause significant confusion for the viewer.

For example, if you generate a pictogram that uses images to represent a measure of data within a bar graph, the images should remain the same size from column to column.

4. Unclear Linear vs. Logarithmic Scaling

The easiest way to understand the difference between a linear scale and a logarithmic one is to look at the axes that each is built on. When a chart is built on a linear scale, the value between any two points along either axis is always equal and unchanging. When a chart is built on a logarithmic scale, the value between any two points along either axis changes according to a particular pattern.

While logarithmic scaling can be an effective means of communicating data, it must be clear that it’s being used in the graphic. When this is unclear, the viewer may, by default, assume they’re looking at a linear scale, which is more common. This can cause confusion and understate your data’s significance.

For example, the two graphics below communicate the same data. The primary difference is that the graphic on the left is built on a linear scale, while the one on the right is built on a logarithmic one.

This isn’t to say logarithmic scaling shouldn’t be used; simply that, when it’s used, it must be clearly stated and communicated to the viewer.

5. Poor Color Choices

Used carefully, color can make it easier for the viewer to understand the data you’re trying to communicate. When used incorrectly, however, it can cause significant confusion. It’s important to understand the story you’re hoping to tell with your data visualization and choose your colors wisely.

Some common issues that arise when incorporating color into your visualizations include:

• Using too many colors, making it difficult for the reader to quickly understand what they’re looking at
• Using familiar colors (for example, red and green) in surprising ways
• Using colors with little contrast
• Not accounting for viewers who may be colorblind

Consider a bar graph that’s meant to show changes in a technology’s adoption rate. Some of the bars indicate increases in adoption, while others indicate decreases. If you use red to represent increases and green to indicate decreases, it might confuse the viewer, who’s likely accustomed to red meaning negative and green meaning positive.

As another example, consider a US map chart that shows virus infection rates from state to state, with colors representing different concentrations of positive cases. Typically, map charts leverage different shades within the same color family. The lighter the shade, the fewer the cases in that state; the darker the shade, the more cases there are. If you go against this assumption and use a darker color to indicate fewer cases, it could confuse the viewer.

The Impact of Poor Data Visualization

While an inaccurate chart may seem like a small error in the grand scheme of your organization, it can have profound repercussions.

By their nature, inaccurate data visualizations lead your audience to have an inaccurate understanding of the data that’s presented in them. This misunderstanding can lead to faulty insights and poor business decisions—all under the guise that they’re backed by data.

Taken to the extreme, a chart or graph that’s improperly formatted could lead to legal or regulatory issues. For example, a misleading data visualization included in a financial report could cause investors to buy or sell shares of company stock.

For these reasons, a firm understanding of data science is an essential skill for professionals. Knowing when data is accurate and complete, and being able to identify discrepancies between numbers and any visualizations created from them, is a must-have in today’s business environment. An online course centered around business analytics can be an effective way to build these skills.

Do you want to further your data literacy? Download our Beginner’s Guide to Data & Analytics to find out how you can leverage data for professional and organizational success.