Data visualization used to be a rather vague yet fascinating topic for me, as it shares considerable overlap with UI/UX design. Therefore, during my undergraduate studies, I pursued relevant courses in this field. However, my focus back then revolved more around the visual and interactive aspects, placing form and functionality at the forefront. Nevertheless, as I delved deeper into learning and design, I realized that the essence of data visualization should not solely revolve around the visual elements but rather how we utilize data to “link numbers to what they really stand for: knowledge, behaviors, people” (Lupi, 2017).
From my perspective, the primary objective of data visualization is to convey a clear understanding of the data, and this aspect was further expanded in the WIL Cube project. In this project, we collected ratings for different charts from various angles and aimed to explain and present this data. While graphs and charts inherently provide a visual interpretation of data, incorporating sensory dimensions beyond sight can enhance the comprehension of information. By using materials instead of traditional tools to present data, we can effectively engage the senses, including touch, smell, and taste, thereby communicating the data to the audience in a different manner. The additional information conveyed through materials helps the audience better understand the data. This experience has made me aware of the expressive potential of real-world materials, broadening my inherent design thinking.
Another key aspect of data visualization is the storytelling element, as “Good data visualization should tell a story” (Braun, 2017). In the WED and BWS projects, we had to search for potential narratives within extensive datasets and present their correlations through compelling storytelling. I began to focus on the logical connection between data and storytelling to ensure that the visualizations produced have a substantial impact. This made me realize that the purpose of showcasing data goes beyond reaching a definitive conclusion; the process of reading and exploring data can also help us understand the significance behind it.
While attending Llewelyn’s data visualization programming course, I found that despite my programming skills not being sufficient to support complex data visualization projects, I encountered many unexpected and interesting results due to errors and bugs while writing code. These unexpected outcomes continually provided me with new inspiration. It deepened my understanding of the practical application of data visualization, enhanced my technical capabilities, and gave me the confidence to break through conventional boundaries during the exploration process, exploring the creativity and aesthetics of data visualization.
It must be acknowledged that my personal learning experiences and professional background in the field of UI/UX have greatly influenced my entry into the realm of data visualization. Right from the start, I understood how to investigate, analyze, and empathize with my target users in order to provide meaningful and relevant experiences. I applied this understanding to data visualization, for instance, by considering the visual literacy levels of the audience, as not everyone interprets data in the same way. Designers need to create personalized experiences based on the defined audience (Alocci, 2021). To achieve the best outcomes, it is crucial to “sneak context in” (Lupi, 2017). This refers not only to the data itself but also to the environment, atmosphere, and background in which the data is collected and analyzed. Conveying them in a truly personalized manner to the audience generates “more meaningful and intimate narratives” (Lupi, 2017). I further practiced this concept in the Sounds Market project, constructing visualizations from multiple dimensions such as sound, lighting, and interaction. This made me realize that data visualization is not solely about data; it can also encompass culture, experience, and emotions.
After engaging in numerous data visualization practices and experiments through tutorials, practical work, and workshops, they have helped me gain a deeper understanding of another facet of data visualization. Most importantly, they have led me to reconsider the concepts and theories we previously learned. All the theories we learn show us potential right directions, but without practical experience, we can never truly grasp their significance. When we propose a data visualization idea, we often test it by creating sketches and prototypes. Through this process, we often discover flaws and gain new insights that may lead us to disagree with our previous viewpoints. As Lupi (2017) mentions, data, like us, is imperfect. I believe the same attitude can be applied to data visualization practices: accepting that projects may have shortcomings and imperfections.
Reference:
Braun, S. (2017) Data Visualisation for Success. Victoria: Images Publishing Group.
Lupi, G. (2017) Data Humanism. Available at: http://giorgialupi.com/data-humanism-my-manifesto-for-a-new-data-wold (Accessed: Jun 08, 2023).
Alocci, T. (2021) “The Define Fase” [Lecture]. Unit 2 Visualisation tools, datasets and data stories. London College of Communication. 12 November