We all have faced problems when we interacted with large databases and numbers in tabular format. Data visualization is the perfect solution to get over the headache. Data visualization is the art and science of representing data in a visual format, such as charts, graphs, maps, and infographics.
Using this, it becomes easier to make decisions, get engaging and accessible data, identify patterns and trends, and understand complex data. As a designer and developer, you know the power of data visualization to increase user conversion rates. However, when it comes to mobile apps, simplicity, and clarity are key.
In this article, we’ll explore the best practices for developing websites and mobile apps that effectively leverage data visualization to improve user engagement and conversion rates. We will also discuss the best practices and modern data visualization examples to improve user engagement and enhance conversion rates.
We promise to try to preach to you with the most accurate factors for efficiently implementing Data visualization in web and mobile apps. So, let’s dive in and explore more!
Data visualization before and after web and mobile apps
Before smartphones and web apps were in trend, data visualization processes were made specifically for desktops. Usually, they were delivered using browsers.
However, when smart devices started to enter the market, data visualization techniques needed an update. But, when viewed on smart devices, data visualizations in PC-specific apps are difficult to read, navigate, and use.
So, designers who implement data visualization help in creating data visualization that works well in the constraints of apps, resolution, lighting, screen size, etc., requiring testing.
So, here we will explore data visualization best practices by keeping in mind the web and mobile apps approach.
Best data visualization practices
Let’s begin with discussing primary data visualization techniques for implementation while using visual components in web & mobile interfaces.
Know your audience
Data visualization with accurate design can communicate the real meaning to the audience. Moreover, you should be clear with who will be your target audience and their expertise.
Your data visualization components should be compatible with your target audience and allow them to view the processed data quickly. If the audience is experienced enough with the basic principles of the represented data, make sure unnecessary data isn’t shown in the visuals and only necessary data is displayed.
The purpose of the data to be represented should be clear
Data visualization components that you use in your app should solve complex strategic queries, assist you with real-time value, and solve real-time problems.
Moreover, it is used to track performance, monitor customer behavior, and calculate process effectiveness. Though it takes time to decide and define clearly the actual purpose of data visualization, it’s important.
When you discuss and make the purpose of the visuals to be clear, you prevent wasting time on making visuals that aren’t necessary.
Touchscreen user controls
Using the touchscreen controls one can integrate highly interactive components in data or web app data visualization. For instance, the user can zoom and touch the chart’s information to see additional data, slide through graphs, and zoom out to view the complete component. All these functions increase the possibilities to build interactive experiences. Moreover, there’s still more space to bring in design, innovations, and interactive experimentation.
Keeping things organized
Coherence and organization are essential while compiling complex data into data visualization components. A coherent design is one that easily matches the background and users can process the data efficiently.
Cleanly organized visualizations help the users reach conclusions on what that visual component is trying to represent. An organized component will highlight the data easily.
Making a data hierarchy can also help you keep the data organized and easy to read. You can sort it from highest -> lowest to highlight the larger values that are more important on the top.
Additionally, you can use brighter colors to display the important data, as it will attract the user’s attention prominently.
Avoid data distortion
Data visualization is a process of telling a complex story in precise narration and avoiding distortions. Minimize the usage of visual components that do not accurately represent the data such as 3D pie charts.
Data visualizations lead users to particular conclusions while avoiding data distortion. It can be used well in designing things like infographics used for public consumption, made for supporting conclusions rather than just conveying the data.
Facts like color choices and calling specific data points can be used in the end without making misleading graphics that could put the designer’s credibility in question.
Using analytics to bring innovation
What is unique about the term data visualization is its design, prerequisites, and features needed to be iterative and exceptional. Currently, clients want in-depth knowledge of the data being displayed.
They can also demand better design if they think it requires any change. Ever-changing design is the main situation that arises in marketing and journalism. The main objective is to allow the users to develop, design, and bring out the visual components without the support of developers and technologies.
That’s why visualization libraries aren’t that challenging to use for developers and they may not become a good alternative for development processes where constant iterations are necessary.
Applying text accurately
Once the appropriate visual component is selected to display your data, put all the important points at the top of the upper left corner. It’s because human eyes tend to start analyzing things from there.
You can add 3-4 views in one dashboard. It’s one of the most-implemented data visualization best practices followed by every designer.
If we add clumsy and too many graphs or charts, it gets difficult for the user to understand. While applying various filters, you need to group them and add one border to the group. This process makes the group more attractive and transparent.
Choosing an accurate data visualization tool
Here are some of the most popular data visualization tools present in the market, you can choose the most suitable one after discussing with your software development partner:
Here are some Power BI Data Visualization tools:
Using straightforward and attractive dashboards
As we are aware of the fact that the dashboard contains different graphs, you should try to add a maximum of four graphs or charts for easy understanding. Try using multiple colors for various figures for easy knowledge of the viewers as the dashboard is the primary thing that helps the users to view the results and make better decisions
Following this best practice and keeping your dashboards clean helps you grab users’ attention and keep them engaged with your information.
Keep the users engaged
Design dashboards that keep the users engaged and clear. Keeping users engaged is considered to be one of the most essential data visualization strategies. For gathering data into visual components with proper consistency is necessary. A great visual component helps the users to understand the meaning faster.
They perfectly show data that is necessary for the user to consume. Moreover, displaying data hierarchy supports the users in making decisions efficiently.
Designers can arrange the information from highest to least priority to show the most important factor on the top and let it have an impression on the user’s mind.
So, these are popular and primary data visualization best practices that every developer and designer should follow for better visualizations.
Final verdict
Data visualization practices when implemented correctly help you to manage huge amounts of data and represent it in graphs and charts. Designers can get help from some of the best visual tools like Tableau, Power BI, and more for performing data visualization with ease.
Your device should support different tools and practices that you implement. Make sure to maintain a clean and accurate dashboard for making digital versions of your data more understandable.
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