1. What is Data Visualization?
Data visualization is the process of converting numerical data into visual images such as charts, graphs, maps, or infographics. The goal is to help users quickly grasp information, detect trends, and make accurate decisions.
From charts, tables, graphs, and maps to diagrams and infographics, each form has its own strength. Some help with comparison, some represent relationships, while others emphasize trends or distributions. Choosing the right type of visualization not only helps present data effectively but also shapes the user experience — from administrative dashboards to health monitoring apps or product reports.
For example, a line chart can show revenue growth over time, while a heatmap can indicate the areas where users interact most on the website.
2. Why is data visualization important in UX/UI design?
The human brain processes images faster than text. Presenting data in visual form helps users understand information more quickly and accurately.
In digital products, showing all data to users is not enough. Putting oneself in the viewer's role, data needs to be organized strategically — guiding users to important insights and supporting decision-making. A well-timed chart or a well-designed dashboard can convey more than hundreds of lines of text and figures — and that is the advantage of products that know how to tell stories with data. Data visualization is crucial for the following reasons:
2.1 Helps users understand information faster
The human brain processes images faster than text. Presenting data in visual form helps users understand information more quickly and accurately.
2.2 Improves user experience
Data visualization helps users easily recognize trends, relationships, and anomalies in the data without deep analysis.
2.3 Supports data-driven design decisions
Heatmaps and funnel analysis help designers identify bottlenecks and optimize the actual user flow.
2.4 Increases persuasiveness when presenting solutions
Tools like heatmaps or user behavior analysis provide insights into how users interact with the product, thereby helping designers optimize interfaces and user flows.
3. The history of data visualization: From clay tablets to smart dashboards
As mentioned above, data visualization is the art of turning data into images — from dry numbers into easy-to-understand, memorable, and decision-friendly information. It not only clarifies what is happening but also opens up deeper insights into the bigger picture.
Interestingly, data visualization is not a modern invention. Clay tablets inscribed with symbols from the ancient Sumerian civilization — over 4,000 years ago — were used to record and track financial silver. More than 4,000 years ago, the ancient Sumerians engraved symbols on clay tablets to monitor silver and goods traded. Those symbols were not just for记忆 but also to "see" — to manage finances and communicate information. This is considered one of the most primitive forms of data visualization.
A turning point came in the 17th century, when science and data began to overlap significantly. Fields like mathematics, astronomy, geography, and statistics respectively created tools to visually represent data: line graphs, heat maps, scatterplots, network diagrams… At this point, we no longer just wrote about data — we started to "draw" it.
The 20th century witnessed the rise of computers. With the ability to process data quickly and accurately, visualization moved into the digital age. From simple charts in Excel to complex dashboard systems in BI (Business Intelligence) platforms, data is not just presented — it is also designed to update in real-time.
Today, data visualization is the right hand of business strategies, product design, organization management, and personalizing user experiences. In the context of big data and AI development, the role of visualization is no longer peripheral — it is central. A well-designed dashboard not only displays information — it guides attention, highlights what is important, reduces cognitive load, and increases confidence in decision-making.
It can be said that data visualization has evolved from a tool into a language. This language does not use words — it uses images. It does not tell stories with sentences — but with insights. And if you want your product to be understood, be trusted, and be acted upon, you need to learn how to speak that language.
4. Useful principles for visualizing data in UX/UI design
Data visualization is not just about "drawing pretty charts." It's the art of transforming raw data into meaningful experiences — helping users see what matters, understand quickly, and act correctly.
4.1 Know who your users are
Don't design charts for "everyone." Understand who your users are, what roles they play (managers, operational staff, customers…), what their goals are, and what information they need to make decisions.
4.2 Understand the business — understand the data context
No data "speaks for itself." Designers need to grasp the business, the operational logic, and the decision-making processes behind the data to know what to show, what to omit, and what to highlight.
4.3 Convey the right message
Each chart must answer a specific question. If you can't clearly say: "What does the user need to know here?", then that data doesn't need to be displayed.
4.4 Simple but focused
Choose familiar, intuitive charts (bar, line, pie...) to enhance comprehension. Avoid being overly creative or complex, which might cause viewers to waste time decoding.
4.5 Increase interactivity — when appropriate
Tooltips, filters, drill-downs, or segmentation help users explore deeper based on personal needs, without overwhelming the interface.
4.6 Optimize for all devices
Data is not just on desktops. A good chart needs to display clearly, be readable, and be easy to interact with on tablets and mobiles.
5. Challenges when combining data and user experience (UX)
Combining data visualization and UX is not just about “drawing an extra chart” — it is a strategic design challenge that requires careful consideration. Here are 4 common challenges:
5.1 Information overload
One of the biggest challenges of data visualization is presenting complex datasets in a way that is easily understandable and visually appealing. If you present too much data at once, it can overwhelm and become difficult to interpret, defeating the purpose of data visualization. UX designers must strike a balance between providing enough meaningful data without overwhelming users.
5.2 Compatibility in design
Data visualization and user experience both require unique skills and knowledge. Integrating both can be a challenge as designers must understand how to convey data effectively through visual signals while also making the user experience seamless and intuitive.
5.3 Bias
Data visualization can be affected by biases from the development team. For instance, if UX designers have a preconceived notion of what the data should look like in their minds, they may present the data in a way that supports their bias rather than accurately reflecting it. UX designers need to be aware of this risk and strive to eliminate bias from the design process.
5.4 Technical limitations
Combining UX design and data visualization can also pose technical challenges. Ensuring that existing technology and infrastructure can effectively support both design and data visualization can be a complex task. This requires collaboration between relevant departments, including designers, software development engineers, and data science engineers to ensure that the final product meets the needs of all stakeholders.
6. Building a User Experience Design Strategy to Integrate with Data Visualization
Combining data visualization and UX is not just about “drawing an extra chart” — it is a strategic design challenge that requires careful consideration. Here are 4 common challenges:
6.1 Information overload
First, you need to identify the goals and objectives of the project. Determine which data is truly essential and helpful for the viewer to achieve their goals. This helps guide the design process and ensures that the final product is relevant and useful.
6.2 Analyzing user needs (User-centric design)
Understanding the specific user is crucial to creating an effective design. Conduct user research to gather information about user needs and goals, then use this information to inform the design process.
6.3 Information overload
Visualization needs accuracy; UX requires simplicity. Designers must know how to represent complex data in a visual, understandable way without sacrificing reliability or the user experience. If too much data is presented, users will fail to see what is important. The design needs to prioritize information order, reduce noise, and display only actionable data.
6.4 Choose appropriate charts to represent data
Create a prototype or trial sample to test the implementation of visualization and user experience. This helps you adjust and improve the design before deployment.
6.5 Create a prototype
Designing a beautiful chart is one thing; displaying it on a real product is another. This requires close coordination between UX, Data, Dev, and Product to ensure performance, interactivity, and data accuracy.
6.6 Testing and adjusting
Conduct usability tests to gather feedback on the design and use this feedback to make iterative improvements. You may need to experiment with multiple design options to determine which solution best fits the target audience.
6.7 Maintain and update
Continuously monitor and update your dashboard or other user interfaces to ensure they remain relevant and useful. This may involve updating both the data and the type of visualization you have used, as well as improving the user experience.
7. Tools to Support Data Visualization for UX/UI Designers
Figma Plugins: Chart, Datavizer, Google Sheet Sync.
Tableau / Looker Studio: For advanced reports or dashboards.
Recharts / D3.js / Chart.js: For developers & designers with front-end knowledge.
Notion, Canva, Excel: For internal reports, quick mockups
8. Conclusion
Data visualization is not just a supportive tool; it is an essential part of user experience design. By turning data into visual stories, we not only help users understand information but also create memorable and effective experiences.
Leading businesses are harnessing this power to transform complex data into intuitive, user-friendly interfaces. This is the key to increasing conversion rates and building customer loyalty.
1. What is Data Visualization?
Data visualization is the process of converting numerical data into visual images such as charts, graphs, maps, or infographics. The goal is to help users quickly grasp information, detect trends, and make accurate decisions.
From charts, tables, graphs, and maps to diagrams and infographics, each form has its own strength. Some help with comparison, some represent relationships, while others emphasize trends or distributions. Choosing the right type of visualization not only helps present data effectively but also shapes the user experience — from administrative dashboards to health monitoring apps or product reports.
For example, a line chart can show revenue growth over time, while a heatmap can indicate the areas where users interact most on the website.
2. Why is data visualization important in UX/UI design?
The human brain processes images faster than text. Presenting data in visual form helps users understand information more quickly and accurately.
In digital products, showing all data to users is not enough. Putting oneself in the viewer's role, data needs to be organized strategically — guiding users to important insights and supporting decision-making. A well-timed chart or a well-designed dashboard can convey more than hundreds of lines of text and figures — and that is the advantage of products that know how to tell stories with data. Data visualization is crucial for the following reasons:
2.1 Helps users understand information faster
The human brain processes images faster than text. Presenting data in visual form helps users understand information more quickly and accurately.
2.2 Improves user experience
Data visualization helps users easily recognize trends, relationships, and anomalies in the data without deep analysis.
2.3 Supports data-driven design decisions
Heatmaps and funnel analysis help designers identify bottlenecks and optimize the actual user flow.
2.4 Increases persuasiveness when presenting solutions
Tools like heatmaps or user behavior analysis provide insights into how users interact with the product, thereby helping designers optimize interfaces and user flows.
3. The history of data visualization: From clay tablets to smart dashboards
As mentioned above, data visualization is the art of turning data into images — from dry numbers into easy-to-understand, memorable, and decision-friendly information. It not only clarifies what is happening but also opens up deeper insights into the bigger picture.
Interestingly, data visualization is not a modern invention. Clay tablets inscribed with symbols from the ancient Sumerian civilization — over 4,000 years ago — were used to record and track financial silver. More than 4,000 years ago, the ancient Sumerians engraved symbols on clay tablets to monitor silver and goods traded. Those symbols were not just for记忆 but also to "see" — to manage finances and communicate information. This is considered one of the most primitive forms of data visualization.
A turning point came in the 17th century, when science and data began to overlap significantly. Fields like mathematics, astronomy, geography, and statistics respectively created tools to visually represent data: line graphs, heat maps, scatterplots, network diagrams… At this point, we no longer just wrote about data — we started to "draw" it.
The 20th century witnessed the rise of computers. With the ability to process data quickly and accurately, visualization moved into the digital age. From simple charts in Excel to complex dashboard systems in BI (Business Intelligence) platforms, data is not just presented — it is also designed to update in real-time.
Today, data visualization is the right hand of business strategies, product design, organization management, and personalizing user experiences. In the context of big data and AI development, the role of visualization is no longer peripheral — it is central. A well-designed dashboard not only displays information — it guides attention, highlights what is important, reduces cognitive load, and increases confidence in decision-making.
It can be said that data visualization has evolved from a tool into a language. This language does not use words — it uses images. It does not tell stories with sentences — but with insights. And if you want your product to be understood, be trusted, and be acted upon, you need to learn how to speak that language.
4. Useful principles for visualizing data in UX/UI design
Data visualization is not just about "drawing pretty charts." It's the art of transforming raw data into meaningful experiences — helping users see what matters, understand quickly, and act correctly.
4.1 Know who your users are
Don't design charts for "everyone." Understand who your users are, what roles they play (managers, operational staff, customers…), what their goals are, and what information they need to make decisions.
4.2 Understand the business — understand the data context
No data "speaks for itself." Designers need to grasp the business, the operational logic, and the decision-making processes behind the data to know what to show, what to omit, and what to highlight.
4.3 Convey the right message
Each chart must answer a specific question. If you can't clearly say: "What does the user need to know here?", then that data doesn't need to be displayed.
4.4 Simple but focused
Choose familiar, intuitive charts (bar, line, pie...) to enhance comprehension. Avoid being overly creative or complex, which might cause viewers to waste time decoding.
4.5 Increase interactivity — when appropriate
Tooltips, filters, drill-downs, or segmentation help users explore deeper based on personal needs, without overwhelming the interface.
4.6 Optimize for all devices
Data is not just on desktops. A good chart needs to display clearly, be readable, and be easy to interact with on tablets and mobiles.
5. Challenges when combining data and user experience (UX)
Combining data visualization and UX is not just about “drawing an extra chart” — it is a strategic design challenge that requires careful consideration. Here are 4 common challenges:
5.1 Information overload
One of the biggest challenges of data visualization is presenting complex datasets in a way that is easily understandable and visually appealing. If you present too much data at once, it can overwhelm and become difficult to interpret, defeating the purpose of data visualization. UX designers must strike a balance between providing enough meaningful data without overwhelming users.
5.2 Compatibility in design
Data visualization and user experience both require unique skills and knowledge. Integrating both can be a challenge as designers must understand how to convey data effectively through visual signals while also making the user experience seamless and intuitive.
5.3 Bias
Data visualization can be affected by biases from the development team. For instance, if UX designers have a preconceived notion of what the data should look like in their minds, they may present the data in a way that supports their bias rather than accurately reflecting it. UX designers need to be aware of this risk and strive to eliminate bias from the design process.
5.4 Technical limitations
Combining UX design and data visualization can also pose technical challenges. Ensuring that existing technology and infrastructure can effectively support both design and data visualization can be a complex task. This requires collaboration between relevant departments, including designers, software development engineers, and data science engineers to ensure that the final product meets the needs of all stakeholders.
6. Building a User Experience Design Strategy to Integrate with Data Visualization
Combining data visualization and UX is not just about “drawing an extra chart” — it is a strategic design challenge that requires careful consideration. Here are 4 common challenges:
6.1 Information overload
First, you need to identify the goals and objectives of the project. Determine which data is truly essential and helpful for the viewer to achieve their goals. This helps guide the design process and ensures that the final product is relevant and useful.
6.2 Analyzing user needs (User-centric design)
Understanding the specific user is crucial to creating an effective design. Conduct user research to gather information about user needs and goals, then use this information to inform the design process.
6.3 Information overload
Visualization needs accuracy; UX requires simplicity. Designers must know how to represent complex data in a visual, understandable way without sacrificing reliability or the user experience. If too much data is presented, users will fail to see what is important. The design needs to prioritize information order, reduce noise, and display only actionable data.
6.4 Choose appropriate charts to represent data
Create a prototype or trial sample to test the implementation of visualization and user experience. This helps you adjust and improve the design before deployment.
6.5 Create a prototype
Designing a beautiful chart is one thing; displaying it on a real product is another. This requires close coordination between UX, Data, Dev, and Product to ensure performance, interactivity, and data accuracy.
6.6 Testing and adjusting
Conduct usability tests to gather feedback on the design and use this feedback to make iterative improvements. You may need to experiment with multiple design options to determine which solution best fits the target audience.
6.7 Maintain and update
Continuously monitor and update your dashboard or other user interfaces to ensure they remain relevant and useful. This may involve updating both the data and the type of visualization you have used, as well as improving the user experience.
7. Tools to Support Data Visualization for UX/UI Designers
Figma Plugins: Chart, Datavizer, Google Sheet Sync.
Tableau / Looker Studio: For advanced reports or dashboards.
Recharts / D3.js / Chart.js: For developers & designers with front-end knowledge.
Notion, Canva, Excel: For internal reports, quick mockups
8. Conclusion
Data visualization is not just a supportive tool; it is an essential part of user experience design. By turning data into visual stories, we not only help users understand information but also create memorable and effective experiences.
Leading businesses are harnessing this power to transform complex data into intuitive, user-friendly interfaces. This is the key to increasing conversion rates and building customer loyalty.
Hello 👋 With a passion for designing digital products, focusing on user research, optimizing experiences, and building effective interfaces. Here, I share knowledge from real projects, ideas, and my stories in the field of UX/UI - Product Design 📚 I hope these articles will provide practical insights, supporting you in your work and your product development journey ✨✨✨
Nguyen Tan Toan
Product Designer
Product Designer
