Data visualization is a logical way to communicate huge data in a simple and understandable form. Depending on the data type, it can be represented in many ways such as bar charts, line graphs, scatter plots, pie charts, etc.
Designers need to implement data visualization best practices and find the best way to display visual data sets. Data visualizations should be useful and visually appealing, ensuring that the information displayed is not misleading.
Moreover, when working with huge data sets, a structured and simplified format is essential to create visuals that are appealing, accurate, and aesthetic.
In this article we will discuss some top data visualization best practices, what this process is, and much more.
What is Data Visualization and why use it?
Data Visualization process helps in translating data into visual forms like maps, graphs, charts, etc. It quickly became famous over the web as it makes understanding that data easier.
It helps a wide range of industries from journalism to technological domains for easy and structured conveying of their data.
Visual data makes it easy to grasp, analyze, and understand the information. It helps in faster decision-making and finds modern patterns to understand big concepts in a better way.
When data is represented in visual form, a human can realize and understand it quickly. It doesn’t require any specific technical skills for data visualization and it’s easy to make and learn using different tools like Tableau, Power BI, etc.
As the number of users of digital devices is increasing, the data volume continues to grow. It gets difficult for humans to comprehend this data and the increasing number of users.
It is said that the human brain cannot take in data larger than a certain extent. So, with accurate data visualization practices, designers can play an important role in creating visuals of huge amounts of data.
Now, let’s learn about the Data Visualization best practices to implement for efficiently designing data visuals.
Data Visualization Best Practices to follow in 2024 and beyond
We saw that data visualization makes bulk data understanding easier, but without following best practices, designing accurate visual components isn’t easy. Let’s explore best practices:
Pick the right visual component
There are many types of visuals available to show your data. You have to pick the suitable component and it will show your desired information efficiently. Here are some best ways to express your information as data:
Bar chart
It is used when we compare data that has a vertical or horizontal bar chart. For instance, comparing time spent on a phone, paid and unpaid apps and their downloads, etc.
Line chart
It is used to represent continuous data with smaller updates. Line charts work accurately for higher values with independent time slots.
Pie chart
The pie chart represents the percentage or portion of data. It’s worth using if we have <7 categories. You can see what percentage a specific category holds out of 100%.
There are many different charts such as column charts, area charts, etc. Before picking anyone, make sure you are clear about what data you want to display digitally.
Clear UI and Easy to easy-to-understand information
App or web app users typically think of data visualization as a context that navigates the app. For instance, if you are visiting a bank account balance sheet, it is a good way to simplify the graph of balance in and out without adding any more graphs to it.
So, it gets easier for the user to see his income and expenses easily from the balance page. Always remember that a clear user interface with only needful information displayed on the visual will improve the user experience and make the data more readable.
Use straightforward and attractive dashboards
As we know, dashboards generally contain different graphs. So, try to add 3-4 charts for easy understanding. Try to use attractive colors to differentiate the information and help the viewers with better knowledge of the information.
If a dashboard is clumsy and full of unwanted information, users might find it unworthy to see. So, make sure it contains only the required graphs and is equipped with attractive and straightforward information.
Check their Skills and Experience
A primary factor to consider is choosing an outsourcing partner who is reliable and has relevant experience.
You should look at their proven work record and delivered software. If necessary, you can also connect with their previous clients and ask them about their experience working with that company.
You can also refer to client testimonials and understand whether they are satisfied with the software or not. Moreover, go through their portfolio and explore different types of domains for whom they have developed software. Such factors will assist you in understanding more about the company’s experience and expertise.
Use Predictable Patterns in Layouts
Eyes can catch the interactive indicators that help in learning the importance of information. Generally, you notice designs, if they are random, or do not make sense, sometimes it’s tough to learn what visualization needs to show you.
To understand what the end-user thinks, we must show the data that makes proper sense for viewers. No matter whatever visual component you use, ensure that it is appropriately visible, and displays data.
Do not represent the data confusingly, as it’s not a best practice.
Using Succinct Titles and Labels
It is essential to use titles and labels for different visualizations and ensure they are simple to read. These labels will provide context to readers who are trying to understand the graph.
Other essential practices to keep in mind while using labels and titles are using appropriate fonts. Accurate fonts with proper font size ensure that data is easy to read and understand.
Avoid using too many titles and labels together. It can create clutter and reduce the readability of the text. Moreover, avoid unnecessary abbreviations as well. When you use abbreviations, include their meaning in the key of the report.
Use up-to-date and clean Data
Maintaining data quality is essential for proper visualization. You will need to preprocess & clean the information to ensure that there are no errors in it. This process includes removing duplicate values, data normalization, etc.
When using uncleaned data, it could result in misinterpretation and it makes data visualization more complex and tiresome.
Outdated or dirty information makes your visual components suffer and makes them hard to understand. It’s also essential to use the relevant and recent data present in the market to ensure that your data visualization is accurate.
Use Text Carefully
Put different vital points at the upper left of the visual component as the human eye is drawn to that place easily. Now, try to add 3-4 views in one dashboard, as it’s one of the primary techniques for it.
As we add too many graphs, it will be hard to understand. When we apply different filters, add a border around them and group them to make them attractive and transparent.
Final Verdict
Implementing Data visualization best practices will ensure good communication of data and users. An accurate data visualization component makes it easy to comprehend the data at one glance.
This process takes in complex data and breaks it down in a manner that makes it simple to target the audience makes them understandable and helps them to make accurate decisions.
The main aim of this process is to enhance data using design and not to draw attention to the design itself.
Following these best practices simplifies the procedure of designing infographics that are useful to the audience.
