In this post, you will get familiar with 10 best data visualization practices that you should be armed with.
Any data you represent on your blog must be not just informative but visually appealing. If you represent data via infographics, charts, or maps, it will make the data less visually poor and more attractive.
What’s more important, visualizing your data will make you an excellent communicator with your target audience. Data visualization is all about transforming boring numbers and stats into something eye-catchy.
The question is how to visualize data effectively?
Let’s start!
Why Data Visualization Practices Are Important
Right before you dive into these ten best data visualization practices, you should understand why they are worth your attention.
It has been already mentioned in the introduction that data visualization stands for creating any kind of statistics, numbers, and data in the forms of different visualizations.
As a result, if your readers of the blog can understand data easily, they will be more likely to spend more time with your content. That leads to a traffic boost.
However, in order to create easy-to-understand data, you will need to follow strict guidelines. Therefore, the next ten steps will help you transform your data into visually appealing forms of visualized content.
1. Think About Your Target Audience First
The very first step in creating data visualization is identifying a target audience. You must understand what kind of data your target audience will be interested in.
When you’re working on identifying the target audience, you should think about such an aspect of marketing as “buyer persona”. A buyer persona is a collective term that describes your ideal customer.
If you are aware of your buyer persona, you will know what data might be interesting for these people.
Consequently, your task is to make sure your data is visualized clearly and is understandable for your audience.
2. Start With Cleaning Your Data
First of all, data cleaning is the process when you filter out all possible anomalies and inaccuracies that might be presented in the data.
It is strictly important to clean your data right before you use it for another purpose. Keep in mind that these inaccuracies can skew the results of the data interpretation later on.
If you happened to publish a figure in a journal, you must make sure that your computation is correct and all relevant knowledge is transmitted to a broader audience.
For instance, if you visualize data for the students, you should try to clarify the theoretical knowledge through data visualization. Thus, it needs more detailed information to make sure the students understand the material easily.
The biggest challenge is to prepare data for a general audience. These visuals should be simple and show essential aspects of your analysis. Hence, it is pretty hard to find a happy medium here.
3. Choose the Right Chart
When the data is clean, you can start with the process of data visualization. And if you decided to visualize data via the chart, you should know that there are numbers of different charts to choose from.
Let’s review some of them:
- Line graphs – this type of chart measures patterns or variations over time and displays the interaction between them
- Bar charts – this type of chart is used to assess the varying totals and amounts of different groups
- Area charts – this type of chart reminds line graphs but it shades the region underneath the line
There are more other types of charts – heatmaps, scatter plots, pie charts, etc. You can create any kind of chart with a specific tool, like an organizational chart maker for example.
4. Don’t Forget to Label Your Chart
You can’t deny that charts and graphs allow us to identify patterns in the data pretty quickly. Unfortunately, it doesn’t mean that these visual elements can deal with specific values. That’s when labels are the best solution to represent this type of data visually.
If your task is to present some new results, introduce a new model or describe an experimental setup – you won’t be able to explain this data using the figure itself.
Likely, you can find a solution with the help of using a caption. It helps you understand how to read the figure and provides additional precision of what exactly you won’t be able to present graphically.
It works like a verbal clarification of the information. But the only difference here is that you will have to be prepared for the questions you might be asked beforehand.
For example, you have created a bar chart that illustrates programming languages that attained the Highest Growth Rates in 2021.
Further Reading: Visual Marketing: The Importance of Visual Content in Marketing
Your aim is to give numbers somewhere in the text or on the figure itself. Thus, your readers won’t make an assumption by looking at the relative heights on the figure.
5. Underline the Important Points
The purpose of data visualization is to guide your target audience through the statistics in the post. Charts act as a guide that suggests the data presented in a visual form.
And it is not a secret that people absorb more information visually. Thus, it makes more sense to present data in the form of symbols, numbers using visual elements.
In the example below, you can see the additional information has been added to underline the major increase in the line chart.
It is strictly important to take into account some specific nuances of the target audience you’re working with.
Let’s say, you need to create a data visualized chart with the information in the Arabic language. You know that this language people read from right to left. Hence, your data should be represented in the same way. It doesn’t mean what type of data you illustrate, it must follow a chosen standard.
Keep in mind that if your infographic contains several graphs, the ordering must be correct. Plus, make sure these graphs change each other logically.
6. Think About Choosing the Dashboard
Before you start making your data visualization more effective, you must think about what type of dashboard you’re going to offer to your audience.
Let’s review three major examples of dashboards you can provide to your users.
- Analytical
This example of dashboards is quite interactive. When it comes to analyzing stakeholder concerns, analytical dashboards work great. By using these dashboards, developers can experiment and research with the data
- Strategic
This example of dashboards is designed for C-Suite executives. With the help of these dashboards, you will be able to analyze the organization’s success concerning KPI metrics
- Operational
This example of dashboards illustrates the information that is not viewed by high-level management. These dashboards present frequently updated information that satisfies a very particular standard of everyday operations
7. Make Your Chart Accessible
Did you know that ensuring consistency in the chart’s design aspects (like grid lines or axis) can enhance the readability of the visualized data?
Another aspect you should pay attention to is the use of text. First of all, the text must point out the relevant information. It should be added where it is appropriate. And make sure you don’t overuse it in your charts. Hence, if you decided to make a logo of your brand and add it to your chart, it might look inappropriate. Plus, it makes your visual too promotional.
Furthermore, don’t forget to minimize noise and clutter in your visualizations.
8. Add Colors to Your Visuals
Data visualization tools vary. Some of them can be hardly taken as tools. Just like colors.
Despite this fact, colors can help your target audience to better perceive the data by using different color combinations. Let’s say, you want to represent sequential data. The ideal choice of colors would be the different shades of the same color. While categorical data is better visualized by using a distinct color of each category.
To make sure you are choosing the right colors for your data visualization, you should review the following tips”
- Use the same color for the same variables and icons
- Clarify what the colors represent to readers
- Analyze whether different colors compliment or clash with each other
- Use lightness to create gradients (as an alternative to hue)
- Working with gradients, use two hues
- Keep in mind that you should take into account the needs of color-blind people
9. Data Must Be Readable in Different Formats
Keep in mind that the charts you create must be easily displayed on both PC and mobile devices. It means the data you represent via charts must be equally readable whatever device you utilize.
It became vital to create mobile-friendly content after Google released its mobile-first indexing. Therefore, if you use data visualization elements in your content, they should be redesign accordingly.
Here are some pieces of advice to follow:
- If you want to look your photos incredible, you should use Cascading Style Sheets. CSS helps you configure the appearance of visuals on each device without any necessity of modifying HTML code
- Don’t shy away from using contrast in the layouts with the help of contrasting background colors. It helps people see the information that matters
- Don’t forget to optimize your photos
- While using a language that reads from left to right, make sure that people will read the information from the upper left to the bottom right
10. Be Open to Feedback
Data visualization aims to transform complex data into a simplified visual version of the same data that could be understandable by your target audience.
If your data visualization can’t build a bridge between the main idea behind your content and the data represented, it won’t make any sense to the audience.
It is a pretty clever idea to share the visuals with your colleagues right before giving them to the public. Let your colleagues give feedback. Take into account their suggestions and improve the visualizations accordingly.
By the way, if you don’t have experience or knowledge in data visualization, you can hire a designer who would perform this work instead of you.
Conclusion
Data visualization is an integral part of a modern-day way of working with any kind of data.
It brings an outstanding experience on different levels starting from standard statistics ending with sales and marketing.
By following these 10 practices introduced in this post you will be able to create data visualizations that look professional and are easy-to-understand.
Next read: Data Visualization in Business Intelligence
