Content and data now go hand in hand—the world of business practically runs on data. Analytics exists at the core of strategic business growth, no matter the size of the organization.
But data on its own doesn’t tell a story—it needs some context to be understood, or otherwise it could be misinterpreted by the audience.
With the number of charts and graphs being shared online increasing over the past year, there is now an even stronger emphasis on the importance of data literacy and visualization.
Visuals are powerful tools that attract audiences and showcase messages faster. When combined with data, these visuals tell brand stories that are appealing and memorable.
That doesn’t mean that every data visual does what it sets out to—there are numerous errors that designers and data analysts make when creating visuals.
We share the most common data visualization mistakes and how best to avoid them.
What is Data Visualization?
Data visualization combines data points and graphic elements—graphs, charts, maps, diagrams, and infographics—to communicate data in a more visually appealing way.
This method of presenting data makes it more attractive to readers and viewers—it also makes the data easier to understand. The diagram below shows what makes a good data visualization.
Source: Venngage
Why is Data Visualization Important?
Data can be very interesting for anyone collecting data but the intended audience may not understand it as well. This is especially true when there is a lot of data to share.
To make these facts easier for audiences to absorb—and act on—businesses need to use data visualization tools that highlight the most important information.
Visuals also tap into the power of data storytelling, which is defined below, to share a succinct message around the data. This also makes the information relatable and meaningful.
Source: Lydia Hooper
What Are the Common Data Visualization Mistakes?
Data analysis and design aren’t skills that often overlap. This can lead to data visualization mistakes that misrepresent information and confuse audiences.
The first step in avoiding such errors is recognizing them. We share seven mistakes that are easy to circumvent if you know what to look out for.
Data Distortions
One of the biggest data visualization mistakes is caused by data distortions. This is an easy error to make if someone isn’t comfortable with designing.
Distortions occur when visual elements with different shapes are scaled improperly, like in the below graph.
Using logos with different shapes makes it hard to scale equally—it looks like McDonald’s is making disproportionately higher sales in comparison to Burger King than is the reality.
On the other hand, the data distortion below is an intentional attempt at misleading audiences.
Source: Times Now
By attributing a larger chunk of the pie chart to a smaller number, the data visualization distorted the information, breaking a cardinal rule of graphic design.
Here are the best ways to avoid making these data visualization mistakes:
- Don’t design visuals against design norms
- Choose charts that suit your data
- Avoid skewing axes, and always include both axes
- Add a data scale to your visuals
- Baselines for data should always begin at zero
- Include all relevant data in the visualization
Mistakes are easy to make but following the above points will reduce the number of design errors made.
Misleading Data
The best visualizations can still be a mistake if the data source was incorrect—or interpreted wrong. Whatever the intention, if the audience receives incorrect information, that is a mistake.
When collecting data, analysts should only source data from trusted sources. If a data story references interesting information, it is worth taking the time to track down the original source.
Once you have your facts, verify them against two other trusted sources—that means the data points have been successfully duplicated and weren’t a fluke.
The data visual also needs to reflect the original point being made, or otherwise, organizations risk amplifying bad infographics like the example below.
Source: The Sun
This map was widely shared as a predictive chart for the novel coronavirus’ global reach but in actuality, the visual depicted global flight patterns unrelated to the pandemic.
In this case, the data was correct but the way it was framed was misleading, leading to panic among audiences online.
Before designing or sharing a data visualization, note how it will be perceived by audiences who aren’t familiar with the subject.
Missing Axes
To avoid creating a bad data visualization, always include both axes of a graph—the X-Axis (horizontal) and the Y-Axis (vertical).
Without an axis, data is easy to misconstrue leading to incorrect assumptions about the message.
Additionally, both axes should start at zero as that is the presumption that audiences will be working with. Spacing between data points should be equal to the numbers they correspond to.
The graph below includes both axes but they aren’t spaced according to the numbers they are representing so it looks like the curve is being flattened when it isn’t.
Axes must be included in graphs, but they also need to be used correctly to avoid misrepresenting the data.
Lack of Context
As a natural progression from the previous point, one of the common data visualization mistakes is sharing the data visualization as is, without giving it context.
Everyone will have seen a graph like the one below—several iterations were shared online and discussed on news channels. But this graph is also wrong because it has no context.
According to this graph, New York had a disproportionately higher rate of positive COVID-19 infections as compared to other areas in the United States.
But what the graph didn’t contextualize was that New York was also testing more and finding cases faster. On its own, the visual tells an incomplete and incorrect story.
Add context to data visualizations either by choosing a different graphic to represent the data, by adding a legend, or by including a text box in the graph.
Bad Visualizations
A common mistake is choosing the wrong graph, and inadvertently designing a bad visualization that misleads or confuses readers, which can lose a business their customers.
Here are some of the kinds of graphs and charts that designers can create:
- Bar charts
- Bubble charts
- Fishbone diagrams
- Flow charts
- Infographics
- Line charts
- Mind maps
- Pictographs
- Pie charts
- Scatter charts
- Wireframes
- Wordgraphs
Those are a lot of options and it can be confusing how to pick the right charts to accurately depict your data.
The wrong chart can skew the data and the message being transmitted—this is a common mistake that even experienced designers can make.
Use the below guide to choose the right chart to represent data. It explains how charts can be used to inform, compare, show change over time, depict groupings, or relationships.
Source: Venngage
Decide on the focus of your data and the intended message—this will make choosing the right graph to tell your data story easier.
Cluttered Graphics
Data can be exciting—and for anyone sharing their data with an audience, there is a strong temptation to include as much data as possible into a single graphic.
That is a mistake, but it happens more often than not. Below is one of the more confusing data visualization examples shared online.
Multiple graphic layers and text boxes make this visual nearly impossible to understand. Even experts in the field struggle to comprehend it.
This is a clear case of clutter in data visualization and must be avoided at all costs. If a visual is becoming so busy that it is rendered unreadable, a narrower focus is required to share the story.
The information could be divided into multiple graphs to tell a more cohesive and comprehensible story, like this data visualization example.
Source: Venngage
It is worth having non-experts look at a graphic before publishing it. Without advanced knowledge, third parties will have an objective perspective on the visuals.
Incorrect Color Use
One of the most common mistakes in data visualization is the misuse of color. The color palette is huge which can lead to designers using too many or too few colors.
Whichever colors are used should be done so with purpose. Here are a few ways to include colors in visuals:
- To highlight information
- Compare or contrast data points
- Show a gradual or sudden change
Color isn’t for decorative purposes in data visualization—it should serve the data story and help clarify or amplify information to audiences.
This graph shows a gradual color contrast from purple to green for the first three data points which are closer in terms of percentage.
Source: Venngage
The top point, which is more than double the amount of its predecessor, is a brighter, contrasting color to differentiate it from the other points.
This is how designers can incorporate color in their data visualizations—to make the visual tell a comprehensive story on its own.
Conclusion: Choose Your Core Message to Avoid Making Data Visualization Mistakes
Whether one is a designer or not, understanding the focal point of the data story will help to avoid designing a data visualization that misleads audiences.
Understand what mistakes can happen—unintentionally and otherwise—before beginning the designing process.
Verify data sources, get third parties to review the visualizations, and ensure that both axes are included.
Following these steps will make it easier to design visuals that tell data stories that are comprehensive and accurate.
About the Author: Ronita Mohan is a content marketer at Venngage, the online infographic maker and design platform. Ronita regularly writes about marketing, design, and small businesses. Twitter: @Venngage