Fact or Fiction: Common Downfalls of Data Visualizations

Common data visualization downfalls

To understand just how easily visualizations can deceive, take a look at the following examples and how they can be corrected.

1. Truncated graphs

One of the most common manipulations used to control the narrative is to omit baselines or begin the y-axis of a graph at an arbitrary number instead of zero. This creates the impression that there is a significant difference between data points when the disparity is small.

To illustrate how a truncated graph can misrepresent the information at a cursory glance, consider the two graphs below. Both contain identical data, but the truncated graph appears to show a massive difference from A to E.

Source: Sanket Shah

Source: Sanket Shah

For a real-life perspective, CNN used a similar graph to show political party support for the controversial court decision surrounding the Terry Schiavo right-to-die case in 2005. Here, it appears as though almost three times as many Democrats supported the decision as Republicans and independents when, in reality, there’s only about a 14 percent difference.

Source: Western Reserve Public Media

Setting the baseline to zero would have better represented the data.

Source: Sanket Shah

Additionally, clearly placed labels help tell the complete story further and decrease the amount of time it takes to process the information.

2. Exaggerated scaling

Line charts, often used to show rates of change over time, are notoriously simple to skew in favor of a chosen narrative. Unlike the bar graphs above, it’s not necessary to begin the baseline at zero to portray the facts accurately. 

However, exaggerating the scale of a line graph can minimize or maximize the change shown easily. A higher y-axis value will cause a graph to reflect less volatility or growth. Conversely, a lower y-axis maximum will result in a steep line seemingly indicating increased volatility or growth.

Source: Sanket Shah

Source: Sanket Shah

If you take into account that by nature, a line graph is far more subtle than a bar graph, narrowing in on the y-axis value may help provide a more precise visualization. That way, the message can be readily and more accurately understood in seconds.

Consider this representation of global warming over time. In the first image, the y-axis has been blown out, causing the graph to indicate almost no change over time.

Source: National Review @NRO on X

However, if you zoom in, minimizing the scale of the y-axis, you’ll see a different, more factual representation.

Source: Sanket Shah

3. Improper extraction

Extracting only a portion of data to align with a particular narrative, rather than letting the full story speak for itself can drastically alter perception. It’s like telling a white lie with the data.

This data visualization of General Electric Company’s performance on the stock market makes it appear as if they’re on an upward swing.

Source: NYSE

But if you were to pull back and look at the complete picture, that would tell a different story.

Source: NYSE

Without providing all the information it’s impossible to make an informed decision. But only providing a snippet of information results in misinformation leading to poor decision-making.

4. Going against the norm

It’s common knowledge that when talking about finances, green indicates profits and red denotes losses. Deviating from these long-held conventions can create confusion and possible misinterpretation of the facts when taken at a glance.

Here, the original visualization uses standard colors to illustrate profits and losses by state. With a quick look, you can see there are more wins than losses.

Source: Sanket Shah 

However, simply by switching green for red, it would appear that losses far exceeded profits across the majority of the country.

Source: Sanket Shah 

Likewise, we’re accustomed to viewing numbers that increase vertically in a chart, making the following example intentionally misleading.

Source: Florida Department of Law Enforcement

Since the purpose of including data visualizations is to convey information quickly, altering norms such as colors or order drastically alters perception as well. To avoid confusion, stick with convention.

5. Too much pie

With pie charts, the sum of each slice must add up to the whole. When the numbers don’t add up, you know there’s an issue ― whether it be sloppy mathematics or an intentional misrepresentation. A pie chart should always add up to 100 percent, so check your math every time.

Source: Fox News

Furthermore, while 3D pie charts may look appealing, they do little to help convey accurate information and, more often than not, can cause a misinterpretation of the data. 

Designers often get caught up in the design element of 3D charts, prioritizing design and technology over the readability of the chart. Returning to the simplicity of a 2D pie chart may be in the best interest of both the reader and the data.

Source: Sanket Shah

Source: Sanket Shah

The following example further illustrates the distortion of angles that often occur in 3D pie charts. 

Source: Visually

Tips for better data visualization

Data visualizations may look easy to create but a lot of effort goes into creating an effective one. Finding the right balance of elements and avoiding common mistakes are key. Here are four tips you can use to create better data visualizations: 

• Choose the graph that fits your data: When you’re coming up with a data visualization, there are many different types of graphs you can select. You need to find the one that fits your data and the message you’re trying to share with your audience. For instance, bar graphs are a good option if you’re trying to compare two or three different values within the same category. 
• Keep it simple: Don’t try to share too much information in your data visualizations. That will distract the viewer from the real message you’re trying to convey. Avoid adding illegible text, additional grid lines or other unnecessary visual elements. 
• Label everything clearly: Proper labeling will help you convey the message you’re sharing. For instance, you should title your graph and label each of your axes. Make sure the labels are easy to read. 
• Pay attention to color: Use colors effectively in your data visualizations. Employ the same colors to convey similar types of data or use different shades to convey waning intensities. At the same time, avoid using too many colors in your visualizations, since this can easily become overwhelming. 

How businesses can use data visualization

As businesses continue to collect more and more data, it can be hard to tell what is important and what isn’t. Data visualizations are a great way to cut through the noise and highlight the trends and patterns you want to focus on. 

Let’s look at different ways businesses can use data visualization:

• Identify patterns: As a business owner, you can use data visualizations to uncover relationships that aren’t otherwise easily discernible. For instance, you can use data visualizations to create sales forecasts, explain customer trends or outline bottlenecks within your operations. 
• Share information: Data visualization gives you a powerful way to share information with your team. For example, visualizing customer data can help your team understand customer demographics and purchasing patterns better.
• Sales forecasting: Data visualization can make it easier to forecast how much revenue your business can realistically expect to bring in over the next year. This information makes it easier to budget and plan your marketing strategy. 

Thanks to technology, there is a wealth of data at our disposal. But if we represent that data falsely, we do a disservice to those who would benefit from its information. 

Beware of making misleading visualizations and take care not to complicate the message with overly complex or inaccurate images. Although the numbers do not lie, the visualizations representing them may.

Jamie Johnson contributed to this article.