Data Visualization Mistakes: Tips for Clearly Presenting Your Findings

It’s easier than ever to stay informed about the topics and subjects that matter to you in a connected world. But as we’ve increasingly found ourselves taking in vast quantities of data, the ways we’ve consumed it have evolved. 

Specifically, the graphical representation of data (often referred to as data visualization), has managed to open our eyes to understand information in ways that couldn’t be achieved through words alone.

From the clay tokens of Mesopotamia to the Minard Map’s representation of Napoleon’s ill-fated Russia campaign, data visualization has been around for literally thousands of years. 

The Minard Map, arguably “the best statistical graphic ever drawn” 

Although data visualization has been around as long as the written word, common mistakes in presenting data persist.

When done properly, data visualization can help you drive home your core premise in a way that clearly, and succinctly, articulates your findings. However, seemingly small mistakes can take away from your data’s effectiveness or worse, unintentionally mislead the reader. 

In order for your data visualization to be effective, you need to tell the right stories, with a clear interpretation of the information that is accessible to the reader.

Here are five common missteps to avoid to ensure your data visualization is well-positioned to express your data, and tell your story in a way that is clear, concise, and engaging.  

Good Data Visualization Is More Than a Cool Design

With advances in software, programs, and design, it’s possible to make your information jump off the page.

Literally. 

That’s great, because dynamic, fun, and generally cool data visualization is more visually interesting, shareable, and compelling than a simple graph or chart.   

Sometimes, a graphic that’s too flashy can distract from the story your data is trying to tell. Consider this the “all sizzle, no steak” approach to visualizing data. 

While the end result looks great on the page, it doesn’t actually convey information that’s useful or relevant to your larger narrative. The data gets lost in the weeds, leaving you with a piece of beautifully designed data that doesn’t actually tell the reader anything. 

In an article for Smashing Magazine, Vitaly Friedman writes —

“To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information.”

It’s important to remember his final point—the purpose of data visualization is more than making your page look amazing (though that is a nice perk). 

Data visualization should engage the user, but it should also inform them. Otherwise, it’s just a pretty design, independent from the information surrounding it. 

Don’t Assume the Reader Knows What You Know

Good data visualization, just like most good content, starts with one important focus: the reader. The best content is created with the user’s experience in mind. We should be more sensitive to that experience when incorporating data visualization.

Let’s face it—you know more about your topic than just about anyone. But your desire to share all that knowledge can overwhelm and confuse the reader. 

Can you learn anything from this graph?

When you get too nuanced with your data points, without the context of the bigger picture, you can lose the reader’s attention. Remember, they are not consuming your content with the requisite background knowledge that you possess. 

It’s surprisingly easy, when you’re intimately familiar with your information, to assume readers know facts that you consider “basic” but in fact need to be addressed in your data for the reader’s benefit.

Sarah Walker, Director of Analytics and Insight at Nexstar Media Group, says, “Know your audience. Is this for experts, or everyone? If it’s for everyone, send it to a friend who doesn’t know what it is and see what conclusions they make.”

Before committing to visualizing a certain data point, take a step back and ask yourself— 

• Is this relevant to the user’s experience?
• Is there a less complicated way to get this point across? 

It might save you from making an unnecessary graph or chart, while driving home your data points more efficiently. 

But Don’t Oversimplify, Either

Just as it’s best to avoid posting overly complicated, expert-level data points, you don’t want to be too simplistic with your data points either. 

Familiarity with the data can cause you to overshare information, but focusing solely on presenting simplified data can go too far, leading to the omission of important nuances. 

Without proper context, your reader might take away the wrong message from your data, or not understand it at all. By leaving out crucial context points, overly-simplified data can actually lead to misinformation that can compromise your content. 

This chart is simple and clean, but what can you take away from it?

Expressing your data in an overly-simplified manner can strip its context, making it less effective, accidentally misleading, or it could fail to tell a story at all. 

Try to include as much relevant data as possible, and present it in a way that’s easy to access and understand. When data is too complicated, you risk losing your reader’s focus. But if you err on the side of simplification, the reader could walk away with false assumptions. It’s a balancing act.

Unclear Data Can Lead to False Interpretations

Data visualization works best when it is clear and understandable. But occasionally, oversight can result in data that doesn’t quite meet this criteria. 

Unclear data, like unlabeled or mislabeled charts and graphs, can inadvertently lead the reader astray, pushing them to assumptions and interpretations that might not be supported by the data.

As Walker pointed out, there are several ways your data might be unclear. On a very basic level, unlabeled data visualization is a no-no right off the bat. “Label everything,” she said. “I’m sorry if it’s not aesthetic.” 

What is this chart trying to tell you? What data points are you looking at? 

Additionally, a common mistake that leads to unclear data occurs when people try to make more than one point through a chart or graph. Placing unrelated data points on the same graph can lead readers to see correlations and connections that don’t actually exist. 

Walker provided an example— “If you designed a graph showing Oreo consumption throughout America, and overlaid it with a graph of the U.S. death rate, you might see shared patterns that lead you to think that the two are connected.” 

But they’re not, they’re simply two data points, presented in an unclear way, allowing users to come up with false interpretations of their own.

Avoid Fixed Data, When Possible

If you really want your data visualization to shine, you should strive to have data that you can manipulate in real time. 

This includes allowing the users to adjust year, time or other parameters, so they have context on how the data changes over time. 

Admittedly, some charts don’t really require this flexibility—a graph that tells you how many people bought houses in Chicago in 2010 versus 2011 won’t change over time. But it could be enhanced with the addition of other data points that add context. For example, the ability to toggle between those sales by income, gender, race, or other factors could help users  come away with a more complete understanding of the data.  

A static, fixed data point can tell one part of a story. Adjustable data points can tell you the whole tale. If you combine that with relevant context, and engaging, beautiful design, your data visualization can empower the reader.

Why Is Data Visualization Important

When readers are presented with data, it’s sometimes difficult to fully grasp the implication through words alone.

Data visualization presents information in a way that makes it easier to spot patterns and trends that would otherwise be lost in a sea of thousands of words.

Data visualization helps data analysts see patterns they might have otherwise missed, but more importantly, it helps them present their findings in a way that’s easily digestible to their reader. Simply put, it helps them tell a richer, fuller story. 

The importance of data visualization helps us see the importance of getting data visualization right. Ultimately, the best data visualization is elegant in its function, beautiful in its form, and efficient in its purpose. It elevates your discourse to new heights, and engages your readers to interact with the story you wish to tell. At its best, it is the final brush stroke on a masterpiece of data. Which is why it is essential that you represent that data the best way you possibly can.