Top 10 Data Visualization Best Practices

You’ll agree that creating (or selecting) data visualization that’s interesting, relevant, and clear to the audience is not an easy undertaking.

Well, it turns out there are best practices that seasoned data pros follow to create visualizations that complement data stories seamlessly.

And get the audience nodding with unison shouting, yes!

In today’s blog post, we’ll take you through 10 tested and proven data visualization best practices that seasoned pros follow religiously to create irresistible data stories.

Yes, you read that right.

So take your time and steal these golden tips to take your data storytelling skills to the A-level.

Bonus: we’ve also rounded up data visualization color best practices to ensure charts in your story are 100% clear and informative to the audience

Top 10 Data Visualization Best Practices Every Professional Working with Data Must Follow

Remember, data is much easier to understand when presented in a visually compelling way. Besides, we process visuals 60,000 times faster than text.

Yes, you read that right.

And it gets better. Visuals are far more memorable than text.

A research study found that we’re likely to retain 10-20% of written or spoken information and a whopping 65% of visual information three days after a seminar.

Let’s dive into the meaty part of the blog. Are you ready?

#1: Use Clean Data

Before converting your raw data into a chart, you need to clean it. Yes, sparkling clean. So you need to do what we call data cleaning.

What’s data cleaning?

Well, it’s the process of filtering out any anomalies or inaccuracies within your data. This process is essential because inaccuracies can distort your visualization.

A research survey conducted by New York Times discovered that data scientists spend about 50 to 80% of their time cleaning and organizing data. And all these are done before the actual visualization.

This shows that cleaning data is incredibly important.

You don’t want to use skewed data to generate insights that can mislead your audience. It takes one question from your audience to discredit your data story. And not to mention the mistrust barrier you’ll have created.

Cleaning data is one of the proven data visualization practices you don’t want to skip.

#2: Be Audience-Specific

What does this mean?

You need to create visualizations that resonate with your audience. And this means you have to roll-up your sleeves and do in-depth research on them. Specifically, focus on their interests, fears, and motivations to win them.

Use visualizations that are custom-specific for your audience. By doing this, you’ll create charts with a strategic purpose that answers a specific question and can be easily understood by the audience.

Let’s put the above into perspective.

Imagine you’re creating a data story for a non-technical audience.

Should you use technical charts?

No. You need to use charts that are easy to read and understand. You don’t want to subject your audience to cognitive overloading.

Another issue you need to avoid is using charts cluttered with trend lines.

Why?

Bombarding your charts with multiple trend lines and other unnecessary stuff will divide the attention of the audience. And this is the last thing you want. Stick to the keep it stupid simple (KISS) principle.

How?

Select charts that are simple and easy to interpret without struggle. Also, avoid overloading your chart with unnecessary information that might confuse the audience.

And you can achieve this by spelling your objectives for visualization clearly. Remember, before selecting the best visualization for your data story; ask yourself what the audience will be looking for in the chart. Like we said earlier, understand the requirements and preferences of your audience.

Know their background.

• Do they have enough time for a detailed visualization?
• How aware are they of the context of the visualization?
• What additional information are they looking for?
• Are they familiar with the graphs used? And so on.

Besides, your goal is to inform people and give accurate results.

Make your visualizations more transparent and explanatory so that your audience can understand your conclusions better. More so, remember who is in your audience and the context of your presentation. You can achieve this by answering the following question:

What’s the best way to make them understand your findings?

Build your design based on the answer.

#3: Select the Right Chart

Once your data is clean and ready, select the best graph or chart to visualize that complements your data story. The charts you use in a data story are incredibly significant because they bear insights.

And this means you want charts that not only suit your data story seamlessly but are easy to read and interpret.

Note: The purpose of data visualization is pretty clear. It is to make sense of the data and use the information for the businesses’ benefit. That said, data is complicated, and it gains more value when it gets visualized.

Essentially, without visualization, it’s challenging to communicate the data findings quickly. Or identify patterns to pull insights and interact with the data seamlessly.

Select the best chart for your data story based on the attributes of the data and, most importantly, your goals.

We have tons of charts at our disposal.

But each chart is best used to visualize data with specific attributes. Don’t forget the overall objective of the data story also plays a hand in selecting the best visualization. For instance:

• Tables present a lot of information in an organized fashion, but they can confuse users searching for high-level patterns.
• Line graphs measure variations or patterns over time and display the interaction between two or more variables.
• Bar charts assess the varying amounts and totals of different groups.
• Scatter plots display the correlation of two variables plotted along two axes, where the pattern of the corresponding points indicates an association between them.
• Pie and donut charts are best used to visualize compositional data that adds to a whole number, which could be a percentage.
• Sankey charts are best used to visualize flow in your data story, as shown below.

Let’s analyze the chart above real quick.

As you can see, the customer flow in a restaurant is segmented based on gender, menu, and sentiments to provide insights on a micro-level.

Selecting the best chart for the data story is one of the proven data visualization practices that seasoned pros follow religiously.

#4: Label your Chart

It’s needless to reiterate that charts empower us to identify patterns, trends, and outliers in our data quickly.

So you need labels to describe your insights. You don’t want your audience to struggle to determine the head or tail of your chart. You want them to cherry-pick insights right away as they flow with your data story.

Irrespective of whether you’re describing an experimental setup, introducing a new model, or presenting new results, data cannot speak for itself.

You need captions to help your audience understand the context of your visualization right away. The caption explains how to read and interpret your chart. More so, it provides additional information about missing variables.

Labeling your chart should now be part of your checklist. Remember to double-check this component before parading your data story in front of an audience.

We’ve compiled a small list of proven data visualization best practices for you to follow when labeling your chart.

You don’t want to skip them.

• Labels should be legible. If your labels are not clear, they won’t help your data story. So they should be easy to read and, most importantly,
• Give a title to the graph. A title provides the context of your charts. You don’t want your audience to struggle to put insights into perspective.
• Use a legend wisely. Legends make it easier for your audience to spot the difference between data points in your chart.
• Label your axes. The title cannot always communicate the labels of the axes (x and y).

So, label them accordingly.

Remember, everything you’re doing is for the audience. You already know the labels of your x and y-axis but is your audience familiar with them?

You don’t want to leave anything to chance.

#5: Use Stories to Explain your Chart

Data visualization is not just about numbers.

What is it about?

Well, it’s about creating compelling data stories that get the audience nodding with unison. Stories are more powerful than facts and numbers.

Remember, words can clutter your charts, primarily when used excessively.

Use words that support the core insights you intend to communicate to the audience through a data story.

Another critical aspect to keep in mind is clutter and noise in your chart. Data visualization is all about keeping everything simple and clear for the audience. So avoid unnecessary information that can draw attention away from the crucial details.

Use headings, sub-headings, and annotations to provide descriptive information about your visualization, including critical insights.

We’ve rounded up some tips for you to steal and use, especially when creating data stories. Check them out.

• Use simple phrases. The aim is to allow the visualization to speak for itself.
• Keep only those annotations that provide relevant information. Putting up annotations for every data point will distract your audience and unnecessarily clutter your chart.
• Use bold or italic text to highlight important parts of the graph. But avoid using them excessively. Otherwise, there will be no difference between regular and emphasized text.
• Avoid text reiterating the same message. For example, the heading and subheading repeating the same message should be avoided. Nobody likes redundant information.
• Avoid using distracting hard-to-read fonts. Your audience should be able to grasp the message in the graph instantly without much struggle.

If you follow the data visualization best practices we’ve compiled for you, you’ll end up with charts that are easy to read and interpret. It’s that easy.

#6: Use Predictable Pattern for Layouts

We naturally have an eye for patterns and trends. And this means we can easily differentiate uptrends from downtrends.

Besides, our eyes are drawn to indicators that tell us important information at a glance.

We naturally seek patterns.

And if patterns are random or don’t make sense, it becomes tough to understand what the visualization communicates. To capitalize on our natural tendencies, ensure the order in which you present data makes sense to audiences.

Note: we naturally read from left to right. And this means you need to orient your visualization to adhere to the aforementioned.

Again, if you’re using multiple graphs, ensure the order is consistent and connections between the data points are clear. You don’t want your audience to get lost as they track a data point or metric.

Take a look at the chart below. It’s about the sentiments of the market towards a brand.

From the diagram above, you can easily track how customer satisfaction has changed over the course of time. And there’s a line curve connecting bars to show the overall trend of the product sentiment across time.

#7: Select the Right Tool

Data in today’s world is gold, but only when you visualize it for in-depth insights. And this means the tool you use can make or break your data story.

You don’t want a tool that’s complex to use or time-consuming. A massive chunk of the tools available in the market are either expensive or complex to use. In other words, they require detailed knowledge of coding to run them.

To create simple and easy charts to read, even for non-technical audiences, we recommend ChartExpo. And this is because the tool is amazingly easy to use and affordable. Besides, it produces simple and clear charts.

If you want a tool that keeps you confined within data visualization best practices, go for ChartExpo.

You can use this tool to visualize business data to improve product interest, marketing strategies, and sales.

#8: Data Visualization Color Best Practices

Color plays a significant role in communicating insights in the absence of words.

Besides, it affects the way our brains process information. Using color strategically can increase memory, aid pattern recognition, and attract attention to key insights.

The goal of data visualization is to help audiences quickly digest information and remember it. While other design principles have a role to play (including white space, contrast, grouping, etc.), color is one of the easiest to apply to data visualization.

Using color strategically can help your audience to understand the meaning and impact of the information presented. On the flip side, poorly used colors can distract your audience from your data story.

Avoid heavy and low contrasting colors if you want your audience to understand your charts without struggle. More so, use colors to highlight critical insights that form the backbone of your story.

For instance, to distinguish profitable and loss months in a bar chart, use high contrasting colors, such as navy blue and light blue colors.

We’ve rounded up data visualization color best practices for you to follow when highlighting insights in your charts. Check them out below:

• Avoid using the same color.
• Use color to create associations between data points.
• For the same variables, use the same color.
• Avoid using a single color to show continuous data.
• Use contrasting colors to show comparison/contrast.
• Clarify what the colors represent to readers.
• Take into account the needs of color-blind people when selecting colors.
• Avoid using less distinguishable colors
• Avoid using TOO many colors in a single chart

Note: There’s always room for creativity, even with the “rules” of color in data visualization. The guiding principle of visualization is to use every element to aid in communication.

You’ll find that there’re, in fact, many ways to communicate information using color.

Take a look at the chart below.

From the chart above, you can easily distinguish two critical insights due to the strategic use of high contrasting colors:

• The best and worst-performing month of the year
• The best and worst-performing flavor

This color combination shows the overall performance of the ice cream shop. The insights generated by the chart above can inform the marketing strategy to pursue.

#9: Highlight Key Insights

If you want your audience to cherry-pick key insights effortlessly, you have to highlight them.

Yes, you read that right.

You don’t want their eyes to struggle to pick the key takeaways.

To direct your audience’s attention, use specific visual cues, such as reference lines or highlighted trends. Remember, our eyes are drawn to symbols that send us valuable details at a glimpse.

For instance, when using a bar chart, highlight the significant bars to help the audience gain perspective of the data story.

Seasoned data visualization experts highlight the key insights they want their audiences to take home. Employ this practice religiously if you want your readers to align with the objectives of your chart.

Let’s dive into the last tested and proven data visualization best practice.

#10: Look for Business Insights

Today’s world is highly competitive.
And this has forced businesses to run back to data for answers. Marrying the insights from your data with high-level business objectives is one of the visualization best practices.

You need data-backed insights before you make critical business decisions, such as marketing budget allocation, product design, etc. Leveraging data can help you stay on top of problems even before they arise.

How?

Remember, you can use data to forecast future trends in your niche market.

So have you tried the ChartExpo visualization library yet to give your data colors and distinctions?

(FAQ)

What are Data Visualization Best Practices?

Data Visualization Best Practices set the standard for the practical presentation of data in charts. If you’re creating charts for audiences, you need to adhere to data visualization best practices, namely:

• Use colors strategically
• Use the proper data visualization tool
• Ensure your data is clean
• Use predictable patterns for layouts. And so on.

What is data visualization and why does it matter?

Data visualization provides your data story’s audience with in-depth meaning by giving it visual context through charts or maps.

And this makes the data more natural and compelling for the human mind to comprehend. Your audience can easily identify trends, patterns, and outliers within large data sets.

What are the benefits of data visualization?

The benefits of data visualization include the following:

• Data visualization strengthens the impact of messaging for your audiences and presents results in the most persuasive manner.
• Visualization lets you comprehend vast amounts of data at a glance and in an in-depth manner.
• Data visualization empowers businesses to make accurate and profitable decisions.

What makes data visualization more powerful and useful?

• Choose the best chart for your data
• Adhere to data visualization best practices
• Avoid clutter because it obscures key insights
• Use high contrasting colors strategically to guide your audience
• Use labels and legends sufficiently in your chart to offer descriptive information
• Provide context for your audience
• Use charts that lend clarity with ease

Wrap up

Congratulations if you’ve read successfully until this point.

We’ve rounded up the top 10 data visualization best practices for you to take your business reports and presentations to the next level.

But only if you put the pointers to active use.

If you’ve jumped straight to this section, below is the recap of the data visualization best practices that seasoned experts use to create compelling data stories:

1. Use clean data
2. Use data visualizations that are audience-specific
3. Select the right chart
4. Label your chart
5. Use stories to explain your charts
6. Use predictable patterns for layouts
7. Select the right visualization tool
8. Data visualization color best practices
9. Highlight key insights
10. Look for Business Insights

Follow these best practices religiously if you want to create compelling and irresistible data stories.

Bonus: use ChartExpo visualization to create charts that are simple, clear, and easy to read. This tool will help you adhere to the best practices we’ve just discussed.

Which one of the 10 data visualization practices do you follow frequently?