We are immersed in an ocean of vast information, where images and data flow in from countless different sources every second. In an era where attention has become a scarce resource, transforming dry numbers into visual, easy-to-understand stories is no longer optional—it is a necessity. Data visualization is the bridge between the complex world of information and the limited capacity of human comprehension.
Alongside this, user experience design (UX Design) focuses on creating products that are not only easy to use but also make users feel engaged, even… "unable to stop".
In this article, I want to explore with you the power of Data visualization and UX Design—how it not only addresses the challenge of conveying information but also creates experiences that make users nod: "Oh, that's how it is!". Let’s see how numbers can "tell lively stories" when designed correctly.
1. What is Data Visualization?
Data visualization is the art of turning dry numbers into meaningful images. Instead of leaving users to sift through a multitude of data, visualization helps them “see” the story hidden behind the numbers—in a clearer, faster, and much more understandable manner.
From charts, tables, graphs, maps to diagrams and infographics, each format carries its own unique strength. Some help in comparison, some show relationships, some emphasize trends or distributions. Choosing the right type of visualization not only helps present data effectively but also shapes the user experience—from management dashboards to health tracking applications or product reports.
When designing, it is important not just to "display" data but to convey the right message—through images, colors, structures, and appropriate context. That's when data visualization becomes not just a supplementary tool but a core part of the product experience.
2. Why is Data Visualization Important in UX/UI Design?
Data itself does not tell stories. Numbers only truly make sense when presented correctly—clearly, attractively, and understandably. Data visualization is the bridge between the complexity of data and the natural understanding capability of humans.
In digital products, showing all data to users is not enough. Placing oneself in the role of the viewer, data needs to be organized strategically—leading users to important insights and supporting decision-making. A timely chart, a well-designed dashboard can convey more than hundreds of lines of description—and that is the advantage of products that know how to tell stories with data.
Data visualization is incredibly important for the following reasons:
2.1 Helps Users Understand Information Faster
The human brain processes images faster than text. A good chart allows users to grasp information in just seconds.
2.2 Improves User Experience
Visually presented data helps users easily recognize trends and relationships without needing to analyze.
2.3 Supports Data-Driven Design Decisions
Heatmaps, funnel analysis help designers identify bottlenecks and optimize real user flows.
2.4 Increases Persuasiveness When Presenting Solutions
Wireframes presented alongside visual data help stakeholders easily visualize design value and quickly reach consensus.
3. The History of Data Visualization: From Clay Tablets to High-Tech Graphs
As mentioned above, data visualization is the art of turning data into images—from dry numbers into easily understandable, memorable, and actionable information. It helps clarify what is happening and opens up a deeper perspective on the overall picture.
Interestingly, data visualization is not a modern invention. Clay tablets inscribed with symbols from ancient Sumerian civilization—over 4,000 years ago—were used to record and track financial silver amounts. This is one of the first pieces of evidence for the need to "see" information, not just read or hear it. However, it wasn't until the 17th century that visualization truly flourished—when fields such as mathematics, geography, physics, and later statistics, began to converge. From heat maps to graphs, from evolutionary charts to network diagrams—we began to sketch data rather than merely analyze it with words.
The 20th century marked a turning point: the advent of computers allowed data to be processed faster and, more importantly, presented more dynamically. From simple charts in Excel to real-time dashboards integrated into Business Intelligence systems, data visualization became an essential bridge between big data and humans.
Today, with the explosion of data and AI, visualization does not stop at “displaying”; it also supports strategic decision-making, even in real-time. A good dashboard is not simply about beauty—it guides attention, prioritizes important information, reduces cognitive load and makes users feel they are in control of the data.
Data visualization is not just a tool; it is a new language—a language that tells stories with data. And if you want your product to be understood, trusted, and acted upon—learn to tell stories in that language.
4. Useful Principles for Visualizing Data in UX/UI Design
Data visualization is not just about “drawing pretty charts”. It is 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 clearly who your users are, their roles (managers, operational staff, clients…), what their goals are, and what information they need to make decisions.
4.2 Understand the Business – Understand the Context of the Data
No data “speaks for itself”. Designers need to grasp the business logic and 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 cannot clarify: “What do users need to know here?”, then that data does not 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 may waste the viewer's time decoding.
4.5 Enhance Interactivity – When Appropriate
Tooltips, filters, drill-downs, or segmentation help users explore deeper based on personal needs without overloading the interface.
4.6 Optimize for All Devices
Data does not only reside on desktops. A good chart needs to be clear, readable, and easily actionable on tablets and mobiles.
5. Challenges of Combining Data and User Experience (UX)
Combining data visualization and UX is not just about “drawing more charts”—it is a strategic design challenge that requires careful consideration. Here are four common challenges:
5.1 Information Overload
One of the biggest challenges of data visualization is presenting complex datasets in an understandable and visually appealing way. If you present too much data at once, it can overwhelm and be difficult to interpret, defeating the purpose of data visualization. UX designers must find 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 influenced by biases from the development team. For instance, if UX designers have a specific, preconceived notion of how the data should look in their mind, they may present the data in a way that supports that bias rather than accurately reflecting it. UX designers must be aware of this risk and strive to eliminate bias from the design process.
5.4 Technical Limitations
Combining UX design with data visualization can also be a technical challenge. Ensuring that the available 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 scientists, to ensure that the final product meets the needs of all stakeholders.
6. Building a User Experience Design Strategy that Integrates Data Visualization
Combining data visualization and UX is not just about “drawing more charts”—it is a strategic design challenge that requires careful consideration. Here are four common challenges:
6.1 Information Overload
First, you need to clarify the goals and objectives of the project. Determine which data is truly necessary and beneficial for viewers to achieve their goals. This helps guide the design process and ensures that the final product is relevant and useful.
6.2 Analyze 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 objectives, then use this information to inform the design process.
6.3 Information Overload
Visualization requires accuracy, UX requires simplicity. Designers must know how to represent complex data in a visual, understandable way without sacrificing reliability or user experience. If too much data is presented, users won't see anything important. Designs need to prioritize information order, reduce noise, and only display actionable data.
6.4 Choose the Right Chart for Data Representation
Create a prototype or trial sample to test the implementation of visualization and user experience. This allows you to adjust and improve the design before deployment.
6.5 Create Prototypes
Designing beautiful charts is one thing; displaying them effectively in a real product is another. This requires close collaboration between UX, Data, Dev, and Product to ensure performance, interactivity, and data accuracy.
6.6 Testing and Adjusting
Conduct usability testing to gather feedback on the design and use this feedback to implement iterative improvements. You may need to experiment with multiple design options to determine which solution is best for the target audience.
6.7 Maintenance and Updates
Continuously monitor and update your dashboards or other user interfaces to ensure they remain relevant and useful. This may involve updating both the data and the types of visualizations you have used, as well as enhancing the user experience.
7. Tools for Supporting 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 knowledgeable in front-end.
Notion, Canva, Excel: For internal reports, quick mockups.
8. Conclusion
The combination of data visualization and UX design is both necessary and important in providing users with an enhanced experience that leads to higher loyalty and conversion rates.
Leading businesses are leveraging this power to turn complex data into intuitive, easy-to-use interfaces. This is the key to increasing conversion rates and building customer loyalty.
By effectively implementing the right UX strategies and data visualization practices, you can reap the benefits of creating meaningful digital products or services that not only attract new customers but also retain existing ones with long-term engagement.
We are immersed in an ocean of vast information, where images and data flow in from countless different sources every second. In an era where attention has become a scarce resource, transforming dry numbers into visual, easy-to-understand stories is no longer optional—it is a necessity. Data visualization is the bridge between the complex world of information and the limited capacity of human comprehension.
Alongside this, user experience design (UX Design) focuses on creating products that are not only easy to use but also make users feel engaged, even… "unable to stop".
In this article, I want to explore with you the power of Data visualization and UX Design—how it not only addresses the challenge of conveying information but also creates experiences that make users nod: "Oh, that's how it is!". Let’s see how numbers can "tell lively stories" when designed correctly.
1. What is Data Visualization?
Data visualization is the art of turning dry numbers into meaningful images. Instead of leaving users to sift through a multitude of data, visualization helps them “see” the story hidden behind the numbers—in a clearer, faster, and much more understandable manner.
From charts, tables, graphs, maps to diagrams and infographics, each format carries its own unique strength. Some help in comparison, some show relationships, some emphasize trends or distributions. Choosing the right type of visualization not only helps present data effectively but also shapes the user experience—from management dashboards to health tracking applications or product reports.
When designing, it is important not just to "display" data but to convey the right message—through images, colors, structures, and appropriate context. That's when data visualization becomes not just a supplementary tool but a core part of the product experience.
2. Why is Data Visualization Important in UX/UI Design?
Data itself does not tell stories. Numbers only truly make sense when presented correctly—clearly, attractively, and understandably. Data visualization is the bridge between the complexity of data and the natural understanding capability of humans.
In digital products, showing all data to users is not enough. Placing oneself in the role of the viewer, data needs to be organized strategically—leading users to important insights and supporting decision-making. A timely chart, a well-designed dashboard can convey more than hundreds of lines of description—and that is the advantage of products that know how to tell stories with data.
Data visualization is incredibly important for the following reasons:
2.1 Helps Users Understand Information Faster
The human brain processes images faster than text. A good chart allows users to grasp information in just seconds.
2.2 Improves User Experience
Visually presented data helps users easily recognize trends and relationships without needing to analyze.
2.3 Supports Data-Driven Design Decisions
Heatmaps, funnel analysis help designers identify bottlenecks and optimize real user flows.
2.4 Increases Persuasiveness When Presenting Solutions
Wireframes presented alongside visual data help stakeholders easily visualize design value and quickly reach consensus.
3. The History of Data Visualization: From Clay Tablets to High-Tech Graphs
As mentioned above, data visualization is the art of turning data into images—from dry numbers into easily understandable, memorable, and actionable information. It helps clarify what is happening and opens up a deeper perspective on the overall picture.
Interestingly, data visualization is not a modern invention. Clay tablets inscribed with symbols from ancient Sumerian civilization—over 4,000 years ago—were used to record and track financial silver amounts. This is one of the first pieces of evidence for the need to "see" information, not just read or hear it. However, it wasn't until the 17th century that visualization truly flourished—when fields such as mathematics, geography, physics, and later statistics, began to converge. From heat maps to graphs, from evolutionary charts to network diagrams—we began to sketch data rather than merely analyze it with words.
The 20th century marked a turning point: the advent of computers allowed data to be processed faster and, more importantly, presented more dynamically. From simple charts in Excel to real-time dashboards integrated into Business Intelligence systems, data visualization became an essential bridge between big data and humans.
Today, with the explosion of data and AI, visualization does not stop at “displaying”; it also supports strategic decision-making, even in real-time. A good dashboard is not simply about beauty—it guides attention, prioritizes important information, reduces cognitive load and makes users feel they are in control of the data.
Data visualization is not just a tool; it is a new language—a language that tells stories with data. And if you want your product to be understood, trusted, and acted upon—learn to tell stories in that language.
4. Useful Principles for Visualizing Data in UX/UI Design
Data visualization is not just about “drawing pretty charts”. It is 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 clearly who your users are, their roles (managers, operational staff, clients…), what their goals are, and what information they need to make decisions.
4.2 Understand the Business – Understand the Context of the Data
No data “speaks for itself”. Designers need to grasp the business logic and 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 cannot clarify: “What do users need to know here?”, then that data does not 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 may waste the viewer's time decoding.
4.5 Enhance Interactivity – When Appropriate
Tooltips, filters, drill-downs, or segmentation help users explore deeper based on personal needs without overloading the interface.
4.6 Optimize for All Devices
Data does not only reside on desktops. A good chart needs to be clear, readable, and easily actionable on tablets and mobiles.
5. Challenges of Combining Data and User Experience (UX)
Combining data visualization and UX is not just about “drawing more charts”—it is a strategic design challenge that requires careful consideration. Here are four common challenges:
5.1 Information Overload
One of the biggest challenges of data visualization is presenting complex datasets in an understandable and visually appealing way. If you present too much data at once, it can overwhelm and be difficult to interpret, defeating the purpose of data visualization. UX designers must find 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 influenced by biases from the development team. For instance, if UX designers have a specific, preconceived notion of how the data should look in their mind, they may present the data in a way that supports that bias rather than accurately reflecting it. UX designers must be aware of this risk and strive to eliminate bias from the design process.
5.4 Technical Limitations
Combining UX design with data visualization can also be a technical challenge. Ensuring that the available 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 scientists, to ensure that the final product meets the needs of all stakeholders.
6. Building a User Experience Design Strategy that Integrates Data Visualization
Combining data visualization and UX is not just about “drawing more charts”—it is a strategic design challenge that requires careful consideration. Here are four common challenges:
6.1 Information Overload
First, you need to clarify the goals and objectives of the project. Determine which data is truly necessary and beneficial for viewers to achieve their goals. This helps guide the design process and ensures that the final product is relevant and useful.
6.2 Analyze 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 objectives, then use this information to inform the design process.
6.3 Information Overload
Visualization requires accuracy, UX requires simplicity. Designers must know how to represent complex data in a visual, understandable way without sacrificing reliability or user experience. If too much data is presented, users won't see anything important. Designs need to prioritize information order, reduce noise, and only display actionable data.
6.4 Choose the Right Chart for Data Representation
Create a prototype or trial sample to test the implementation of visualization and user experience. This allows you to adjust and improve the design before deployment.
6.5 Create Prototypes
Designing beautiful charts is one thing; displaying them effectively in a real product is another. This requires close collaboration between UX, Data, Dev, and Product to ensure performance, interactivity, and data accuracy.
6.6 Testing and Adjusting
Conduct usability testing to gather feedback on the design and use this feedback to implement iterative improvements. You may need to experiment with multiple design options to determine which solution is best for the target audience.
6.7 Maintenance and Updates
Continuously monitor and update your dashboards or other user interfaces to ensure they remain relevant and useful. This may involve updating both the data and the types of visualizations you have used, as well as enhancing the user experience.
7. Tools for Supporting 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 knowledgeable in front-end.
Notion, Canva, Excel: For internal reports, quick mockups.
8. Conclusion
The combination of data visualization and UX design is both necessary and important in providing users with an enhanced experience that leads to higher loyalty and conversion rates.
Leading businesses are leveraging this power to turn complex data into intuitive, easy-to-use interfaces. This is the key to increasing conversion rates and building customer loyalty.
By effectively implementing the right UX strategies and data visualization practices, you can reap the benefits of creating meaningful digital products or services that not only attract new customers but also retain existing ones with long-term engagement.
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
