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Data visualization UI: best practices and winning approaches

Data visualization UI: best practices and winning approaches

How can you increase the value of your apps for users? One powerful and potentially underrated tactic involves improving your approach to data visualization as part of the user interface.

What is data visualization? It's the practice of showing information through imagery instead of text, and there's a scientific reason to prioritize it in product design: 90% of information processed by the brain is visual and up to 75% of users judge credibility by aesthetics.

Poorly designed visual-focused user interfaces may fail to live up to their potential. That's why it's important to learn how to display data effectively and internalize data visualization best practices.

What are the best practices for data visualization UI?

When considering how to optimize the performance of your app's data visualization UI, it pays to focus on a few specific guiding principles. If you let these ideas guide your decision-making process, they'll keep your efforts on the right track.

More specific and granular suggestions that can help you master the art of data visualization UI design include:

Creating an application with a high-quality data visualization UI means confronting questions at every step of the design process. Every choice you make, on both technical and aesthetic levels, can contribute to the success of the finished product.

What's the best way to present data to your audience?

There is no one generic audience for every digital product. Your specific application will have a very specific target group of users. One way to determine whether the UI design process is on the right track is to ask, "Will my target audience be able to take value from this chart?"

The role of a data visualization UI is to tell a story with data. This means — instead of presenting raw figures with no context, the app will help users come to conclusions and make choices based on the information in the chart.

There are a few key questions that can help you center yourself during the design process, verifying that your chosen ways to present data visually are telling the correct story to the right audience. These include:

Incorporating user stories into the design process of data visualization UIs is essential for ensuring that the final product truly resonates with the target audience. User stories are brief, simple descriptions of a feature told from the perspective of the person seeking the new capability, usually a user or customer of the system.

This approach is incredibly useful in data visualization because it keeps the focus on the user's needs and goals. By framing requirements in the context of user stories, designers can more effectively choose visual elements, layouts and features that not only look aesthetically pleasing but also serve practical, user-centered purposes. User stories help in creating a UI that is not just visually engaging but also functional and intuitive for the end user, making the data both accessible and actionable.

Every piece of the design process feeds into the goal of telling a clear, useful data story to a specific audience segment. Rather than asking if a visual cue looks good in general, it's more helpful to ask whether it helps people understand what they're looking at.

What are some common data visualization mistakes to avoid?

Effective data visualization UI design doesn't just mean following best practices — it also involves avoiding common pitfalls. Some design problems are particularly dangerous because they might not become apparent immediately. The general through-line between data visualization mistakes is that they lead to products that don't serve the users' best interests.

Frequent issues with data visualization design include:

This is a donut chart, which is better for the ratio function, whereas here, the user just needs to know the count — in this case, it's best to redesign the visuals.

this visualization, on the stacked columns chart, some colors are just too similar to each other to distinguish.

This graph isn't following data visualization guidelines in one important way: its axes are not labeled, and users have to hover over to receive an informational tool tip.

Some problems aren't universal but are rather tied to the target user profile. Creating an interface that's too complex for a general audience or too simplistic for a specialized audience can make an application less useful for its intended purpose.

How has data visualization UI evolved, and what's next?

Technology's constant evolution has affected every aspect of design best practices, and the concept of what makes a good data visualization has shifted over time. Adapting UI design efforts to keep up with the march of new tech innovations is simply an intelligent approach.

So, what has this meant in recent years?

Designers are also grappling with philosophical questions about the best ways to present data. Can one graph tell the story you're focusing on, or is it better to combine multiple visualizations? This could mean:

In the years to come, recent trends, including the rise of generative AI, could have an impact on data visualization UI design. For instance, AI can help pick the right chart for a question or act as a companion when choosing what data to present. In the tech-saturated present and future, it can be valuable for you as a designer to pick up some development know-how alongside your design skills.

How can you master data visualization UI in your next project?

Working with expert consultants can help you with the data visualization elements of your next project. This assistance can take a variety of forms, including:

Transcenda's experts have worked with companies in all those capacities and more. Examples of these successful engagements include our collaborations with Savant, Medidata and Centric Software. In each of these cases, there was a need for an effective data visualization UI to address a very specific issue.

Contact Transcenda to delve deeper into the world of data visualization UI design.

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