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ISSN 2690-3644 (Print) ISSN 2690-3652 (Online) 

Vol. 6, No. 2, 2025 

www.scholink.org/ojs/index.php/elsr 

149 
 

Original Paper 

Data Visualization Design: Strategies for Optimizing 

Information Delivery and Decision Support 

Yingquan Wang1* 

1 College of Design and Fine Arts, Qingdao Huanghai University, Qingdao, China 

* Corresponding Author: Yingquan Wang, Email: wangyq@qdhhc.edu.cn 

 

Received: April 27, 2025         Accepted: May 13, 2025      Online Published: May 27, 2025 

doi:10.22158/elsr.v6n2p149        URL: http://dx.doi.org/10.22158/elsr.v6n2p149 

 

Abstract 

This paper explores the fundamental concepts, methods, and principles of data visualization design. It 

provides a detailed analysis of key steps, such as data preprocessing, graphic design, interaction design, 

and multidimensional data visualization. The paper discusses how scientific design can enhance the 

readability and comprehensibility of data. Furthermore, it examines the role of data visualization as a 

crucial tool for information delivery and decision support in modern society, highlighting the importance 

of design principles such as simplicity, clarity, and consistency in improving data presentation. Finally, 

the paper looks ahead to the future development trends of data visualization design, particularly the 

application of augmented reality, virtual reality, and artificial intelligence technologies, which are 

expected to greatly advance the intelligence, personalization, and interactivity of data visualization. 

Keywords 

data visualization, interaction design, graphic design, data preprocessing, multidimensional data display 

 

1. Introduction 

With the advent of the information age, the rapid increase in data volume has made it increasingly 

difficult to extract valuable information from vast datasets. Data visualization, as a method of presenting 

data graphically, not only helps individuals identify underlying patterns and trends from complex data, 

but also facilitates the data analysis and decision-making process. Data visualization is more than just a 

simple presentation of data; it integrates elements of artistic design and emphasizes the depth and 

accuracy of information delivery. In fields such as business, policy analysis, and interdisciplinary 

communication, data visualization provides a more intuitive and interactive way to present data, 

significantly improving decision-making efficiency and accuracy. 

 



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2. Definition of Data Visualization 

Data visualization is a method of converting complex, abstract data into easily understandable 

information through graphical representation. Its core approach utilizes visual forms such as charts, 

graphs, and maps to display the structure, patterns, trends, and relationships of data, thereby providing a 

clearer way to communicate information to the audience. This method not only helps individuals uncover 

hidden patterns in complex data but also facilitates data processing. 

In the intersection of art design and data visualization, the presentation of data is not merely about 

information expression; it also requires consideration of how to convey the depth and accuracy of 

information through visual forms. Data visualization, as a means of information communication, 

emphasizes visual design and application elements. By carefully selecting and combining graphics, 

colors, layouts, and other elements, data visualization enhances the comprehensibility and interactivity 

of data. The goal is to break the traditional boundaries of textual and numerical expression, using 

graphical representations to make the underlying meaning of data more clearly communicated to the 

audience. 

In addition to serving as an analytical tool, data visualization also plays a powerful role in decision 

support. In fields such as business decision-making and public policy analysis, data visualization helps 

relevant individuals quickly grasp key data, gain insights into potential trends and risks, and support a 

more efficient and accurate decision-making process. Furthermore, data visualization plays a crucial role 

in promoting interdisciplinary communication. It allows data analysis results to transcend academic 

disciplines, languages, and cultural barriers, providing a unified understanding platform for users from 

different backgrounds. 

 

3. Methods of Data Visualization Design 

3.1 Data Preprocessing 

Data preprocessing plays a crucial role in data visualization design, as it directly affects the quality and 

accuracy of the data. Effective preprocessing not only improves the comparability and stability of the 

data but also enhances the scientific nature and expressiveness of the visual presentation. The first step 

in data preprocessing is data cleaning, which involves handling outliers, missing values, and duplicate 

data. Outlier identification and correction typically rely on statistical methods or interpolation techniques, 

aiming to eliminate disruptions to the overall structure of the dataset. Missing data can be managed either 

by removing incomplete records or applying interpolation methods to fill the gaps, ensuring the integrity 

of each data record. Removing duplicate data not only optimizes storage space but also ensures the 

accuracy of data analysis. 

Data transformation is another critical step, which involves converting raw data into a format suitable for 

effective presentation and analysis. This process includes standardizing data types, such as converting 

textual data to numerical data or transforming date data into a consistent format. Additionally, 

normalization helps eliminate scale differences across different data dimensions, allowing for reasonable 



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comparisons and analyses of various data on the same scale, thereby enhancing the uniformity and 

precision of the visualization. Data transformation is not just a superficial adjustment; it aims to make 

data more intuitive and understandable when presented visually, enabling the audience to quickly 

recognize and comprehend the information. 

Finally, feature engineering is also an essential aspect of data preprocessing. It involves extracting 

representative and highly relevant features from large datasets, further reducing the dimensionality of the 

data and improving the clarity and depth of the visualization. During this process, designers often create 

new feature dimensions through mathematical transformations or feature combinations, which better 

reveal the underlying relationships and patterns within the data. For large datasets, sampling techniques 

(such as random sampling or stratified sampling) can effectively reduce the complexity of data processing 

and accelerate the computational speed of visualization. Through these preprocessing techniques, the 

quality, reliability, and visualization effectiveness of the data are significantly enhanced, laying the 

foundation for subsequent analysis and presentation. 

3.2 Graphic Design 

In the graphic design phase of data visualization, the core task is to transform abstract data into intuitive 

and easy-to-understand graphical forms, enabling the audience to quickly capture and analyze complex 

data relationships. Choosing the appropriate type of chart is a crucial step in this process. Designers must 

consider the type, structure, and the message the data is meant to convey when selecting a chart. For 

example, bar charts are suitable for displaying comparisons of discrete data, line charts are ideal for 

showing trend changes, pie charts help display the proportion of parts to the whole, scatter plots 

demonstrate relationships between different variables, and radar charts are useful for presenting 

multidimensional data. 

In addition to selecting the appropriate chart type, designers need to arrange the layout of the chart 

reasonably based on the data’s dimensions and complexity, ensuring the chart has a clear, concise, and 

easily understandable structure. A key aspect of the design is avoiding information overload by properly 

organizing titles, axis labels, legends, and other elements to help users quickly capture the core 

information from the chart. 

Moreover, the proper use of color and visual encoding in graphic design cannot be overlooked. Color not 

only serves to attract attention but also helps distinguish different datasets or emphasize specific 

information. In the design process, the chosen color scheme should balance both the aesthetic appeal and 

functionality of the chart. Designers often use vibrant colors to highlight important data and softer or 

grayscale tones for auxiliary information to avoid visual distractions. In terms of visual encoding, 

designers should skillfully use multiple channels, such as shape, size, color, and position, to visually 

represent relationships and patterns in the data. Different visual channels should be optimized according 

to data attributes and user cognitive patterns to enhance the readability and understanding of the data. In 

conclusion, through scientific graphic design, data can be effectively communicated, and users’ analytical 



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capabilities can be enhanced, thus achieving the ultimate goal of data visualization: making information 

more transparent, easier to interpret, and facilitating decision-making. 

3.3 Interaction Design 

Interaction design in data visualization refers to the interaction between users and visual charts, allowing 

users to explore and understand data in greater depth. By enhancing user interaction with visual charts, 

data is transformed from a static presentation into a dynamic tool that can be explored and understood 

more thoroughly. Interaction design not only enhances the user experience but also effectively supports 

data analysis from multiple perspectives and levels. Common interaction design methods, such as hover 

effects, click interactions, and zoom functions, significantly increase the operability of charts. For 

example, dynamically displaying relevant data labels or detailed information when hovering the mouse 

allows users to gain deeper insights without interrupting the overall view. By clicking on specific 

elements within a chart, users can not only view more detailed data but also filter or select data according 

to their needs, enabling them to focus on subsets of data that are of interest. Zoom and pan functions 

offer users greater flexibility, especially when dealing with large datasets, allowing them to adjust the 

view and explore data trends in different regions. 

Another key value of interaction design is in enhancing users’ understanding and engagement with the 

data. Through linked chart designs, dynamic updates between multiple views help users intuitively 

recognize relationships between data points. For instance, when users make a selection or perform an 

operation in one chart, other related charts can be updated simultaneously, greatly improving the 

comprehensiveness and coherence of data analysis. Additionally, editable features enable users to 

customize charts based on their needs, further enhancing the flexibility and personalization of the 

visualization. These interaction design methods not only promote active user engagement but also 

provide personalized experiences, such as allowing users to adjust axes, modify chart types, or update 

charts in real time to adapt to dynamic data environments. By offering features like data export, 

interaction design also allows users to save and share analysis results, enabling the continued use of 

visualized data in multiple contexts. These rich interactive techniques not only improve users’ ability to 

grasp and analyze data but also significantly enhance the practical value of data visualization. 

3.4 Multidimensional Data Visualization 

Multidimensional data visualization focuses on displaying and analyzing data with multiple dimensions 

or attributes. It helps users gain a more comprehensive understanding of the internal relationships and 

patterns within complex data structures through various graphical methods. Different types of graphic 

methods play an important role in visualizing multidimensional data, catering to different needs in data 

expression. For example, parallel coordinate plots are an effective tool for displaying relationships among 

multiple numerical variables using parallel axes, allowing users to intuitively identify trends and 

correlations between dimensions. Radar charts, on the other hand, present the values of different 

dimensions in a polygonal form, highlighting relative differences between dimensions, making them 

suitable for comparing the interactions among various attributes. The advantage of these charts lies in 



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their ability to concisely display both the differences and consistencies between multidimensional data, 

offering users a clear overall view. 

In addition to parallel coordinate plots and radar charts, flat matrices, bubble charts, multidimensional 

scatter plots, and 3D charts are also common methods for visualizing multidimensional data. Flat 

matrices display different dimensions of data in a matrix format, with each dimension mapped to a row 

or column in the matrix. Visual encoding such as color or size is used to reflect the relationships between 

data, helping users quickly identify data distribution. Bubble charts represent multiple dimensions of 

information through bubble size, color, and position. This method not only shows the relative 

relationships between data but also reveals data distribution and trends. Multidimensional scatter plots 

map multiple dimensions to the positions and colors of data points, highlighting relationships between 

data points and facilitating the analysis of correlations between dimensions. 3D charts, as tools for 

visualizing three-dimensional or higher data, help users identify complex interactions and spatial 

distributions of three-dimensional data by presenting graphs in three-dimensional space. 

Through these diverse visualization methods, designers can present rich, well-structured data information 

to users, allowing them to analyze and understand the underlying patterns and trends in multidimensional 

data more comprehensively and accurately. 

 

4. Principles of Data Visualization Design 

In data visualization design, adhering to a set of fundamental principles is key to ensuring that charts are 

not only visually appealing but also effectively communicate information. The principles of simplicity, 

clarity, consistency, readability, interactivity, and aesthetics work together to form the core framework 

for data visualization design. Simplicity emphasizes the removal of redundant elements to ensure that the 

chart presents only the most critical data, avoiding information overload. Consistency enhances the 

coherence and contrast of data interpretation by maintaining a unified design style. Meanwhile, 

readability and aesthetics improve the clarity and visual appeal of the chart through appropriate layout, 

color coordination, and visual balance. Interactivity gives users control over the data exploration process, 

adding depth and functionality to data visualization. The integration of these design principles optimizes 

the user experience and ensures the precision and efficiency of data communication, making data 

visualization not only a tool for display but also an important support platform for user analysis, 

understanding, and decision-making. 

4.1 Simplicity Principle 

The simplicity principle emphasizes the removal of unnecessary decorations and complex elements to 

ensure that the chart highlights the most critical data. By eliminating redundant information, designers 

can effectively reduce visual clutter, making the chart more intuitive and easy to read, thereby helping 

users quickly capture key data and trends. In addition, the clarity principle requires designers to ensure 

that elements such as titles, labels, and scales are sufficiently legible and distinguishable so that users 

can accurately interpret the information presented in the chart. Choosing appropriate fonts and color 



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combinations is crucial to achieving this goal. A clear, simple visual style not only prevents confusion 

during interpretation but also enhances the overall comprehensibility of the chart, improving the 

effectiveness of data communication. Simplicity and clarity complement each other, working together to 

optimize the user experience, ensuring that data visualization is not only visually appealing but also 

effectively conveys information. 

4.2 Consistency Principle 

The consistency principle requires that various elements of the chart, such as colors, shapes, and sizes, 

maintain a unified design style. This consistency helps users quickly identify similar information within 

the chart and promotes the relationships and contrasts between data, making the communication of chart 

content clearer and more intuitive. Additionally, the overall style and layout of the chart should remain 

consistent to ensure visual harmony and unity. Whether in the title, labels, or axes, designers should 

ensure that these elements are presented in a consistent manner throughout the chart, reducing the 

cognitive load on users when interpreting the data. Through this design approach, the consistency 

principle effectively enhances the readability and understandability of the chart, allowing complex data 

to be presented to users in a more unified and clear manner. 

4.3 Readability Principle 

The readability principle requires designers to choose appropriate chart types and layouts that match the 

characteristics of the data to ensure a clear structure and easy-to-understand information. To avoid overly 

complex or vague presentations, designers should simplify visual elements and optimize how data is 

presented, enabling viewers to quickly obtain key insights. Additionally, the size and proportions of the 

chart need to be appropriately controlled to ensure that the chart remains clear and readable across 

different display devices and resolutions. Whether viewed on desktop computers or mobile devices, the 

chart should adapt to varying viewing conditions to prevent issues such as being too small or 

proportionally distorted, which could affect the user’s reading experience. By adhering to these design 

principles, the readability of the chart can be significantly improved, ensuring that data is communicated 

accurately and efficiently. 

4.4 Interactivity Principle 

The interactivity principle adds a dynamic and exploratory dimension to the data analysis process in data 

visualization design. By providing a variety of interactive features, designers enable users to engage 

actively with charts, allowing them to explore data more flexibly and uncover potential patterns and 

trends. Interactivity goes beyond simple visual presentation and transforms into an experience, where 

users can adjust the data view based on specific needs through various interactive operations such as 

clicking, hovering, filtering, and zooming, thereby revealing more layers of information. This interactive 

design not only enhances the user’s sense of control over the data but also meets the diverse needs of 

users regarding the depth and breadth of data analysis, thus improving the functionality and adaptability 

of the data visualization. Users are no longer passive recipients of data but active participants in data 

exploration and analysis, greatly stimulating interest in and the depth of data discovery. 



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Effective interactive design requires the creation of a clear and intuitive interface that allows users to 

perform operations seamlessly, reducing the cognitive burden caused by interface complexity. Designers 

must ensure that interactive controls and features are user-friendly and that they follow a consistent 

design language and logical structure so that users, even when encountering the interface for the first 

time, can quickly understand and navigate the operations. An intuitive interface should not only facilitate 

ease of use but also include a feedback mechanism to ensure that users receive immediate and clear 

responses to their actions, further enhancing their sense of involvement and achievement in the data 

analysis process. By implementing such designs, the interactivity principle can increase user engagement 

and improve users’ understanding and application of data, ultimately making data visualization not only 

a tool for information transmission but also a path for deep insights and decision support. 

4.5 Aesthetics Principle 

The aesthetics principle in data visualization design is not only about visual appeal but also a key factor 

in enhancing the effectiveness of data expression. Well-designed charts can effectively capture users’ 

attention, and through the appropriate selection of visual elements such as color, shape, and layout, the 

aesthetic quality of the chart can be enhanced, thereby improving users’ experience and understanding of 

the data. In this process, color combinations should not only align with visual harmony but also serve 

clear functional purposes, such as distinguishing data categories or highlighting key information, while 

avoiding excessive decoration that could cause visual fatigue. The design of shapes and layouts should 

focus on balance and symmetry to ensure visual harmony in the chart, avoiding the excessive clustering 

or disorder of elements. This approach helps users quickly capture the important information within the 

chart. 

Although aesthetics is a subjective sensory experience, its impact on information transmission and user 

understanding is profound. By adhering to the aesthetics principle, designers can create visually pleasing 

charts that not only enhance the clarity and readability of the chart but also stimulate user interest and 

participation. Ultimately, this results in a perfect integration of data and visual design, enhancing both 

the transmission of information and the user’s engagement with the content. 

 

5. Future Development Trends of Data Visualization Design 

With the rapid advancement of technology, the future of data visualization design is set to witness a series 

of innovations and transformations. The rise of augmented reality (AR) and virtual reality (VR) 

technologies indicates that the presentation of data visualization will no longer be limited to traditional 

two-dimensional charts and graphs. With the aid of AR/VR devices, users will be able to interact with 

data in a more intuitive and immersive manner within virtual environments, enhancing the depth and 

experience of data analysis. By dynamically displaying data in three-dimensional spaces, users can 

explore and manipulate the data in real time through gestures, voice commands, or other forms of 

interaction, discovering potential relationships and trends. The application of these technologies will not 

only break through the limitations of traditional data presentation but also create entirely new data 



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analysis scenarios, making complex datasets easier to understand and work with. Future visualization 

design will no longer be a one-way information output but will evolve into an interactive and dynamic 

analytical platform, enhancing user engagement and immersion. 

At the same time, artificial intelligence (AI) and machine learning (ML) technologies will play an 

increasingly important role in the field of data visualization. With the support of intelligent algorithms, 

data visualization will not only enable automatic recognition of data patterns but also provide 

personalized analysis recommendations, helping users quickly uncover trends and anomalies within the 

data. For example, AI can automatically recommend the most appropriate chart types based on historical 

data and user preferences, even generating automated reports, greatly improving the efficiency and 

accuracy of data analysis. Additionally, by incorporating natural language processing (NLP) technology, 

users can generate corresponding charts and analysis reports simply by entering natural language 

commands, lowering the threshold for data analysis and making it easier for non-expert users to perform 

data analysis. This trend towards automation and intelligence will make data visualization more aligned 

with user needs, offering more personalized and flexible services. Moreover, as concerns about data 

privacy and security continue to grow, future data visualization design will place greater emphasis on 

data interpretability and transparency. Ensuring that users not only understand the meaning of data but 

also have clear explanations of data sources, processing methods, and analytical results will foster greater 

trust in the data. In conclusion, the future of data visualization will move towards smarter, more 

customized, and interactive approaches, becoming a powerful tool integrating advanced technologies and 

user needs, offering users richer, more accurate, and more convenient data analysis and decision-making 

support. 

 

6. Conclusion 

Data visualization design is not only the combination of technology and art but also an essential tool in 

modern data analysis and decision-making processes. Through scientifically sound evaluation, graphic 

design, interactive design, and multi-dimensional data presentation, data visualization not only aids users 

in understanding complex data and discovering underlying patterns but also facilitates efficient 

information transmission and decision-making support. With the development of technology, especially 

the real-world applications of augmented and virtual reality and artificial intelligence, the future of data 

visualization will exhibit smarter, more dynamic, and personalized trends. Future data visualization will 

break through the confines of traditional two-dimensional charts, offering more immersive and 

interactive user experiences, while also integrating intelligent algorithms and natural language processing, 

making data analysis more precise and accessible and opening the door to data exploration for non-

professional users. As these new technologies continue to evolve, data visualization will gradually 

become a multifunctional platform for information transmission, decision support, and interdisciplinary 

communication, driving deep insights and decision-making across various fields. 

 



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Acknowledgments 

This research was funded by the 2024 horizontal project matching funds of Qingdao Huanghai University 

(KYH2024112). 

 

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