Data Art vs. Data Visualization: A Deep Dive

Data surrounds us like invisible digital oxygen. Often, it can feel impersonal when presented in statistical charts. As a researcher, I've witnessed how powerful data visualization can be for conducting scientific, unbiased analysis. But what if we could bridge the gap between cold, hard facts and the human heart? What if, instead of just informing, data could be used in artistic pieces that move us? 

Data visualization has long been viewed as a purely analytical tool that supports scientific tasks. However, the rise of accessible software tools and the democratization of data sources have empowered artists to break free from traditional visual analytics. This has blurred the lines between analytical methods and artistic expression, giving rise to data art. This genre goes beyond mere representation to provoke emotion, critique, and deeper engagement.

I find it fascinating how creators are expanding our conception of data—how it can be understood and experienced—and I'm excited to share that with you in this blog post.

Purpose: Function vs Expression

Figure 1) Purpose: Impartial Data Visualization vs Expressive Data Art (AI-generated, Adobe Firefly).

What is Data Visualization?

Data visualization applies visualization techniques for data analysis [1]. Its primary purpose is communicating complex data in a simple, accessible format, such as charts, graphs, and dashboards.

Data Visualization includes both explanatory and exploratory approaches [2]:

• Explanatory data visualization focuses on communicating a specific message or story that the creator already knows. The visuals are designed to present information clearly and effectively, ensuring the viewer understands the key takeaway points.

• Exploratory data visualization is primarily used for discovering insights and patterns within data. The creator uses these visuals as a tool to explore the data without a specific message to convey. The goal is to uncover hidden relationships, trends, and anomalies, facilitating a deeper understanding.

  
  

“An explanatory graphics tells a story, while an exploratory graphics is a tool for the reader to find their own story in the data.” [2]

Data visualization transforms raw data into meaningful visuals, allowing us to see patterns and trends that would be otherwise hidden. It's all about function.

What is Data Art?

Data art uses data as its raw material for artistic expression, prioritizing aesthetics, narrative, and emotional response over clear data interpretation.  It explores new presentation methods, creating experiences that are beautiful and thought-provoking [1, 2, 3, 4, 5]. It's all about expression.

The Spectrum of Data Representation

The following table summarizes the key characteristics of the discussed approaches:

Figure 2) Analogies: Explanatory Data Visualization is like a lawyer presenting evidence, Exploratory Data Visualization is like a detective searching for clues, while Data Art is like a painter expressing emotions and ideas (AI-generated, Adobe Firefly).

It is important to note that the line between data art and data visualization can be blurry. Generally, data visualization aims to clarify or discover the inherent structures within data, while data art uses data to generate entirely new visual forms [2]. A piece can blend both approaches using artistic techniques to enhance understanding or create visualizations that are themselves works of art.

In fact, rather than distinct disciplines, data visualization, and data art can be more accurately viewed as existing along a continuum, with some works leaning more towards functional clarity and others prioritizing artistic expression. While acknowledging this spectrum, the remainder of this article will focus primarily on contrasting the two ends: the functional clarity of data visualization and the expressive power of data art.

Design approach: Established charts vs. Experimental forms

Data visualization relies on established chart types [6] and design principles [2, 7, 8] to ensure accuracy and readability.  Bar charts, scatter plots, and heatmaps, among many other chart types [6], are all designed for quick comprehension. 

Data art often employs unconventional and experimental techniques. It's a playground for creativity! Artists are constantly finding new and innovative ways to use data as their artistic material, and this exploration often leads to exciting and unexpected results.

Tools: Visualization software vs. Expanded canvas

Data visualization frequently leverages established software and programming libraries.  Common tools include programming languages like Python with libraries such as Matplotlib [9] and Seaborn [10], as well as dedicated visualization platforms like Tableau [11] and Power BI [12]. These tools offer pre-built chart types, interactive dashboards, and statistical analysis capabilities, facilitating the efficient creation of clear and informative visuals. 

Data art, conversely, embraces a broader range of tools, reflecting its experimental and creative nature. Artists might utilize visual programming environments like TouchDesigner [13], vvvv [14], or Cables.gl [15], which allow for complex data manipulation and real-time interactive installations.  Custom code, often written in JavaScript using libraries D3.js [16] or p5.js [17], is also prevalent, enabling artists to create unique visual experiences.  Some artists even incorporate traditional design software like Adobe Illustrator [18] into their data art workflows. Data art isn't limited to the digital; crafting techniques using various physical materials can be a part of its creative process. These physical manifestations of data can offer a tangible connection to the information, further blurring the lines between art and data.

Artificial Intelligence: Automated Insights vs. Creative Generation

Artificial Intelligence (AI) 's role in data art and visualization is becoming increasingly significant.  In data visualization, AI algorithms can automate identifying patterns, trends, and anomalies within complex datasets, leading to more efficient and insightful visualizations. AI-powered tools can also suggest optimal chart types and layouts, further enhancing the clarity of data communication [19, 20].

AI provides data artists with a wealth of new creative tools and approaches. Generative AI models can be trained on carefully curated datasets to create unique and visually stunning artworks, transforming raw data into abstract forms, textures, and compositions.  AI can also be used to create interactive installations that respond to input in real time, blurring the lines between the physical and digital [21]. AI's ability to learn and adapt makes it a powerful tool for artists seeking to explore data's dynamic and ever-changing nature.

Figure 3) Role of AI: AI-powered software automates data analysis and visualization, while AI algorithms can assist in creating data art (AI-generated, Adobe Firefly).

Augmented / Virtual Reality: Navigating Information vs. Artistic Immersion

In data visualization, augmented reality (AR) and virtual reality (VR) environments can create immersive experiences that allow users to explore complex datasets in three dimensions, offering a more intuitive and engaging way to understand the information [22].  Imagine walking through a 3D scatter plot where each data point is an object that you can interact with. 

In data art, VR provides artists an entirely new canvas for creative expression.  VR installations can transport viewers into immersive worlds, transforming data into breathtaking visual and auditory experiences.  Imagine stepping inside a data sculpture that evolves and changes based on real-time data streams. 

Examples: Charts vs. Immersive Experiences

Data visualization examples include dashboards for business intelligence, interactive maps, and scientific charts in research papers. We use these tools daily to make sense of the world's data. Data visualization can also be aesthetically beautiful, as demonstrated by the work of information designer David McCandless, who distills information into stunning and easily digestible visuals [23].

Figure 4) Data Visualization Examples: Interactive maps (Photo by KOBU Agency on Unsplash), business dashboards (Photo by Luke Chesser on Unsplash), and scientific charts in research papers (Figures by Author published in [24, 25]).

Data art is a vibrant field encompassing everything from static 2D pieces to immersive, interactive, large-scale installations based on generative AI models, offering new ways to experience information.

Nadieh Brenner, for example, often blends data art with generative art, as exemplified by the collection "Wandering of Stars" [26]. This series of three static pieces explores the movement of stars across vast timescales, depicting their positions 400,000 years in the past, present, and future. Another artist, Kirell Benzi, merges data science, machine learning, and abstract aesthetics to create data art pieces, like "Circadian Rhythms" [27], which was created with Racecar Studio. This audiovisual work centers around data collected from sports watches.

Refik Anadol, in his large-scale installations, employs generative AI models trained on carefully curated datasets. An outstanding example of this approach is “In the Mind of Gaudí” (2021), the first AI-based immersive room [28]. This stunning installation, housed in a six-sided LED cube within Casa Batlló, utilized a dataset of approximately one billion images, including Gaudí’s sketches, historical visual archives of the building, academic resources, and publicly available photos from various internet and social media platforms, to craft a captivating 360-degree experience, which I truly enjoyed. Refik Anadol also creates “living” artworks that evolve with new information, transforming datasets into dynamic visual stories, like the “Interconnected” installation at Charlotte Douglas International Airport, which uses real-time operational data to generate a constantly evolving, abstract representation of the airport's activity [21]. 

Moving from pixels to physical form, Natalie Miebach translates scientific data, such as meteorological and oceanographic information, into physical sculptures. In this way, abstract information is transformed into tangible experiences, allowing for a deeper understanding of natural phenomena [29].

As you can see, the possibilities are endless! Data can be transformed into anything from beautiful 2D artwork or 3D sculpture to a fully immersive, interactive environment that responds to your presence and emotions.

Figure 5) Data Art Examples: From 2D via 3D static pieces to immersive large-scale installations (AI-generated, Adobe Firefly).

Audience: Analysts vs. Technology & Art Enthusiasts

Data visualization targets analysts, researchers, and decision-makers who need to understand the numbers quickly and make data-driven decisions in various fields, from business and science to healthcare and education. Data visualization also plays a crucial role in communicating information to a general audience, as seen in charts and graphs within news articles, educational materials, and public awareness campaigns. Such visualizations were particularly crucial during the COVID-19 pandemic, where clear and accessible data representations were essential for informing the public about infection rates, hospitalizations, and the effectiveness of preventative measures [30].

Data art's audience is remarkably diverse, encompassing art and technology enthusiasts, designers searching for inspiration, and anyone curious about the creative possibilities inherent in data.  The diverse locations where data art can be experienced—galleries, museums, public installations, and online platforms—further contribute to its broad appeal [31, 32, 33].

Interpretation: Objective vs Subjective

Data visualization prioritizes objective representation. The goal is to accurately and consistently depict the underlying data, revealing existing patterns and trends without introducing bias or ambiguity.  The focus is on the clarity and truthfulness to the data source, allowing the viewer to draw informed conclusions based on the presented information.  The interpretation is ideally singular and universally understood by anyone familiar with the visualization conventions.

Data art, on the other hand, embraces subjective interpretation.  While data serves as the foundation, the artist's perspective, choices, and creative vision are central to shaping the final piece.  The artist may intentionally abstract, distort, or reinterpret the data to emphasize specific narratives, evoke emotional responses, or explore conceptual ideas.  The goal is not necessarily to present a definitive "truth" but to offer a unique lens through which to view and experience the data.  This allows for multiple, equally valid interpretations, each influenced by the viewer's background and perspective. 

Impact: Understanding vs. Inspiration

Data visualization aims to improve understanding and facilitate informed decisions. For example, the New York Times "You Draw It" series challenges readers to predict trends in economic data before revealing the actual patterns, helping people understand their own biases and misconceptions about topics like income inequality [34]. Similarly, the Our World in Data platform's visualizations of global development metrics have helped policymakers and the public understand complex issues like poverty reduction and climate change [35].

Data art seeks to spark inspiration, raise awareness, and offer new ways of seeing the world. For instance, Refik Anadol Studio's data sculpture "Artificial Realities: Coral" draws attention to the impact of climate change, prompting viewers to consider the urgent need for environmental sustainability [36]. Similarly, Janet Echelman's “Earthtime 1.26” installation, inspired by data related to the 2010 Chilean earthquake and tsunami, highlights our interconnectedness with the people surrounding us and our planet [33].

The Future: Evolution vs. Revolution

While data visualization evolves with technological advances to deliver more precise insights more efficiently, data art is undergoing a revolution fuelled by experimental technologies and unconventional mediums. The rise of AI and massive computing power has dramatically expanded the possibilities for both fields, enabling the processing and artistic interpretation of previously unmanageable datasets. We're witnessing ambitious data visualization projects and data art installations that harness AI algorithms to transform petabytes of information into immersive experiences, creating environments where viewers can walk through data landscapes.

Interestingly, we're also seeing an enhanced convergence: traditional visualization tools incorporate artistic elements to boost engagement, and data artists integrate analytical rigor into their creative processes. This creates an excitingly rich landscape where both approaches thrive and inspire each other.

I'm excited to continue exploring this fascinating landscape on the Data Immersion platform, and I invite you to join me. For ongoing inspiration at the crossroads of data and art, subscribe to our newsletter.