The Do’s and Don’ts of Data Visualization

Data is most valuable when it’s easy to understand. Data visualization is a powerful communication tool that you can use to make data more accessible and easier to figure out at a glance.

Poor data visualization, however, may leave viewers more confused than when they started.

The right approach will be essential when visualizing data. These do’s and dont’s will help guide you as you build graphs, pie charts, plots, and other data visualizations for your business.

 

1. Do: Keep It Simple

The clearest data visualization is often the simplest. Keeping your data visualizations simple is a good way to make them more accessible and easier to understand — which will, in turn, help them provide more value to your visitors and customers.

Good visualizations minimize colors and attention-grabbing elements that aren’t directly related to the data being displayed.

They use familiar types of visualization, like line graphs, bar graphs, and pie charts, that most visitors will understand how to read. The overall design of the visualization also aims to tell its story quickly, providing visitors with the big message of the visualization within a few seconds.

By using these techniques to keep your visualizations simple, you’ll help ensure their accessibility to visitors. More accessible visualizations will provide value to the largest audience possible.

 

2. Don’t: Misrepresent Your Data

It can be easy to mislead audiences with data, even if you’re not trying to be dishonest. Small mistakes, framing decisions and even color choices can all come together to create misleading data visualizations.

These data visualizations will give your audience the wrong impression about certain facts or information. Certain audience members who are able to spot misinformation may also trust your brand less.

Most data misinformation occurs in one of three ways — transparent misinformation, white lies or misrepresentation.

Transparent misinformation is a visualization that doesn’t match up exactly with represented data. White lies are exaggerations or omissions that make parts of a data set seem like the whole story. Misrepresentations are small mistakes or errors that mislead viewers about the truth of a data set.

Actively avoiding and preparing for misinformation will help you create the most accurate and informative visualizations possible.

 

3. Do: Utilize Hierarchy

The order in which you display data can have a big impact on how easy it is to understand. Many bar graphs, for example, will order items based on value, so that the first item will be the one with the greatest or least value. Ranking items like this can make your visualization’s overall message much easier to follow.

You’ve probably also noticed that pie charts usually group their values so that viewers can easily identify which value is the biggest and which is the smallest.

Using hierarchical data visualization like this can help you avoid creating disorganized and hard-to-follow graphs or charts. When developing a new visualization, be sure to consider how hierarchy can help you organize data in a way that’s easy to understand.

 

4. Don’t: Show Too Much Data At Once

Single data visualization can only communicate so much information. Trying to represent as much data as possible in visualization can lead to cluttered and confusing designs that viewers may not be able to understand.

When building a visualization, try to decide which information is the most important to your audience, or the story you’re trying to tell. At the same time, be sure to avoid creating a misleading graphic through the omission of important information or context — leaving out certain data can turn an otherwise effective visualization into misinformation.

Simplicity is especially important for data visualizations that you’ll share on social media or similar platforms. Viewers will find these visualizations while scrolling through a feed of other pieces of bite-sized content. Trying to communicate too much data may encourage a viewer to scroll past your visualization.

 

5. Do: Use the Right Visualization Type

When creating your visualization, you have options as to how you’ll organize your data. Pie charts, bar graphs, histograms, scatter plots and line charts can all be useful for representing different types of data.

Not every visualization will be right for your particular data set, however, or the story you’re trying to tell.

Pie charts, for example, are a great way to show how sales of different products made up a business’s overall sales numbers. Line graphs, however, are a good way to show a change in sales volume over time.

Familiarizing yourself with the most common visualization techniques and how to use them will help you pick out the perfect plot or graph type for your next visualization.

 

6. Don’t: Use Bad Data

No matter how good your visualizations are, there’s nothing you can do about bad data. If your data is incorrect, misleading or incomprehensive, your visitors aren’t going to get much out of it, even if it’s well-visualized. If the visualization is obviously off or misleading, these visitors may even trust you less.

Before you begin creating data visualization, make sure that the underlying data is high-quality, interesting and relevant to your audience. Fact-check the data if possible. You can compare your data to data from other sources, if necessary, to double-check that it is accurate.

With high-quality and accurate data, you’ll spend less time cleaning up your visualizations and won’t have to worry about misleading your audience accidentally.

 

Here’s How to Create the Best Data Visualizations

A data visualization can be a powerful story-telling tool for businesses. With the right combination of data, graphic design, and visualization techniques, you can quickly communicate information to your audience, providing significant value.

Creating a misleading visualization, however, can actually make your audience less informed (or encourage visitors to trust your brand less).

Knowing what techniques to use — and which mistakes to avoid — will help you create visualizations that deliver value without risking misinformation.