9/7/2018 Five Common Downfalls of Data Visualizations

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Visualizations and data representationsare often misleading, deceptive orovercomplicated. It is important to avoidmaking common mistakes when trying toaccurately convey data throughvisualizations.

In an age where information is consumed in mereseconds, we're seeing more and more visuals usedto relay facts and figures – whether it be inbusiness, politics or socioeconomics. Datavisualizations, like graphs, charts, and tables, arecommonplace in the news or businesspresentations. They can quickly establish trust, tella story and illustrate complex messaging with littlemore than a glance. And as the saying goes,numbers don't lie, or do they?

Just like a photo can be altered to removeimperfections, visuals can be manipulated tosupport the narration an author wants to portray.Likewise, overly complicated visualizations are justas deceptive. Readers should not have to spendmore than 30 seconds looking at a visualization tounderstand the message. Graphs should be easy tointerpret, with clear lines and labels. As notedstatistician Edward Tufte stated, "Excellence instatistical graphics consists of complex ideascommunicated with clarity, precision andefficiency."

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Fact or Fiction: Five Common Downfalls of Data Visualizations

BY SANKET SHAH

Business.com / Insurance / Last Modified: July 23, 2018

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9/7/2018 Five Common Downfalls of Data Visualizations

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Misleading and confusing visuals skew data orideas and can lead to misinformation guidingimportant decisions. The overall implications ofdeceptive data visualizations can have cascadingresidual effects, leading to repetitivemiscommunication and ultimately a lack of trust.

To understand just how easily visualizations candeceive, take a look at the following examples andhow they can be corrected:

1. Truncated graphsOne of the most common manipulationsused to control the narrative is by omittingbaselines or beginning the y-axis of a graphat an arbitrary number instead of 0, thuscreating the impression that there is asignificant difference between data points,when in fact, there is relatively littledisparity.

To illustrate how a truncated graph canmisrepresent the information with just acursory glance, consider the two graphsbelow. Both contain identicaldata; however, the truncated graph appearsto show a massive difference from A to E.

Source: Sanket Shah

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9/7/2018 Five Common Downfalls of Data Visualizations

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Source: Sanket Shah

For a real-life perspective, CNN used a similargraph to show political party support for thecontroversial court decision surrounding the TerrySchiavo right-to-die case in 2005. Here, it appearsas though Democrats supported the decision byalmost three times more than Republicans andindependents, when in actuality, there's only abouta 14 percent difference.

Source: Western Reserve Public Media

A better representation would have been to set thebaseline to 0.

9/7/2018 Five Common Downfalls of Data Visualizations

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Source: Sanket Shah

Additionally, clearly placed labels help further tellthe complete story and decrease the amount oftime it takes to process the information.

2. Exaggerated scalingOften used to show rates of change over time, linecharts are notoriously simple to skew in favor of achosen narrative. Unlike the bar graphs above, it'snot necessary to begin the baseline at 0 toaccurately portray the facts. However,exaggerating the scale of a line graph can easilyminimize or maximize the change shown. A highery-axis value will cause a graph to reflect lessvolatility or growth. Conversely, a lower y-axismaximum will result in a steep line indicatingincreased volatility or growth.

Source: Sanket Shah

9/7/2018 Five Common Downfalls of Data Visualizations

https://www.business.com/articles/data-visualization-downfalls/ 5/13

Source: Sanket Shah

Taking into account the nature of a line graph,which is far more subtle than a bar graph,narrowing in on the y-axis value may help toprovide a more precise visualization so that themessage can be readily and more accuratelyunderstood in seconds.

Consider this representation of global warmingover time. In the first image, the y-axis has beenblown out causing the graph to indicate relativelyno change over time.

Source: National Review @NRO Twitter 

However, if you zoom in, minimizing the scale ofthe y-axis, you'll see a different, more factualrepresentation.

9/7/2018 Five Common Downfalls of Data Visualizations

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Source: Sanket Shah

3. Improper extractionCommensurate to a white lie, extracting only aportion of data to align with a particular narrativerather than letting the full story speak for itself candrastically alter perception.

Given this data visualization of General ElectricCompany's performance on the stock market, itappears they're on an upward swing.

Source: NYSE

But if you were to pull back and look at thecomplete picture, it would tell a different story.

9/7/2018 Five Common Downfalls of Data Visualizations

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Source: NYSE

Without providing all the information it'simpossible to make an informed decision, but,worse, only providing a snippet of informationresults in misinformation leading to poor decision-making.

4. Going against the normIt's common knowledge that when talkingfinances, green indicates profits and red denoteslosses. Deviating from these long-held conventionscan create confusion and possiblemisinterpretation of the facts when taken at aglance.

Here, the original visualization uses standardcolors to illustrate profits and losses by state. Witha quick look, you can plainly see there are morewins than losses.

Source: Sanket Shah 

9/7/2018 Five Common Downfalls of Data Visualizations

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However, simply by switching green for red, itwould appear that losses far exceeded profitsacross the majority of the country.

Source: Sanket Shah

Likewise, we’re accustomed to viewing numbersincrease vertically in a chart, making the followingexample intentionally misleading.

Source: FL Dept. of Law Enforcement

9/7/2018 Five Common Downfalls of Data Visualizations

https://www.business.com/articles/data-visualization-downfalls/ 9/13

Seeing as the purpose for including datavisualizations is to quickly convey information,altering norms like colors or order drasticallyalters perception as well. To avoid this, stick withconvention.

5. Too much pieWith pie charts, the sum of each slice must add upto the whole. When the numbers don't add up, youknow there's an issue – whether it be sloppymathematics or an intentional misrepresentation.

Source: Fox News

A pie chart should always add up to 100 percent,so always check your math.

Furthermore, while 3D pie charts may lookappealing, they do little to help convey accurateinformation and, more often than not, can cause amisinterpretation of the data. As Edward Tuftenotes in Chartjunk, designers often get caught up inthe design element of 3D charts, prioritizingdesign and technology over the readability of thechart. Returning to the simplicity of a 2D pie chartmay be in the best interest of both the reader andthe data.

9/7/2018 Five Common Downfalls of Data Visualizations

https://www.business.com/articles/data-visualization-downfalls/ 10/13

Source: Sanket Shah

 

Source: Sanket Shah

The following example further illustrates thedistortion of angles that often occur in 3D piecharts.

Source: Visually

Thanks to technology, there is a wealth of data atour disposal. By falsely representing that data, wedo a disservice to those that would benefit from its

9/7/2018 Five Common Downfalls of Data Visualizations

https://www.business.com/articles/data-visualization-downfalls/ 11/13

information. Beware misleading visualizations andtake care not to complicate the message withoverly complex or inaccurate images. As it turnsout, the numbers do not lie, but the visualizationsrepresenting them may.

Sanket Shahhttps://healthinformatics.uic.edu/resources/faculty/sanket-shah/Sanket Shah is an instructor for theUniversity of Illinois at ChicagoDepartment of Biomedical and HealthInformation Sciences (BHIS) at theCollege of Applied Health Sciences.Professor Shah has created a coursecurriculum focusing on healthcarebusiness intelligence, healthcare data,knowledge management and consumerinformatics. Professor Shah bringsconsiderable strategic management andtechnology innovation experience, andhas a wide range of experiences inhealthcare information technology on theprovider, payer, government and vendorsides of the healthcare business. He hasserved in positions in management,system design, logical databasearchitecture, product management,consulting and healthcare valuemeasurement for the past 10 years in thehealthcare industry.

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