Data Art: Art, Tech, or Somewhere in Between?
Despite the questionable science behind online quizzes that tell you whether you are a right or left brain person, one thing is sure: they reflect a widespread belief that some people are creative and others rational. I fall on the ‘right’ side : I have a vast imagination, and god, am I terrible at maths.
For that reason, when a friend of mine told me about Refik Anadol’s Machine Hallucinations exhibit, I was thrilled. I had been searching for an activity to do with a computer scientist friend. This exhibit seemed like the pot of gold at the end of the rainbow.
At the exhibition, I was amazed by something other than the images flashing across the walls. Instead, I was intrigued by our different reactions. While I enjoyed the colours and movement, my friend focused on decoding the algorithms. My curiosity followed me outside of Artechouse, and I decided to delve deeper into data art.
What is data art?
According to Sey Min, “data consists of facts that can be analysed or used in an effort to gain information.” Data visualisation is thus “the graphical display of data.” (Mahoney) Using this simple definition, one can say that data visualisation has existed for a very long time with charts and graphs (Grugier). However, since the 1990s, data visualisation has been used as a creative approach. Data art uses computer software to convert numeric data into visual or audio forms. During this process, the data is “reinterpreted according to the artist’s creative purposes.” (Li 299–300) Though some scholars equate data visualisation and data art, others claim there is a difference between them. Stephen Few, for example, believes that data art is mainly an aesthetic experience that has entertainment as its goal. “It is art that is based on data. As such, we can judge its merits as we do art in general.” Data visualisation, on the other hand, aims to help a group of people understand the data — no matter how aesthetically pleasing it is (Few). No matter what we call it, the transformation of numbers into aesthetic forms has become a trend in the age of big data (Min). Data art can be sub-divided into static data art and dynamic data art. Static data art uses digital technology as a means to create images. Dynamic data art, on the other hand, aims to create an interactive experience — where the piece is updated as new data emerges (Li 303).
Data Visualisation: Information, or Entertainment?
Data visualisation has historically been associated with the need to share information with the public. The use of aesthetics was nothing more than a tool to allow the understanding of this data. “To visualise data is to understand it,” explains Sey Min. Through that lens, data art is a mere tool to “make sense of the copious amount of information with which we are confronted with daily.” Edward Tufte realised the importance of aesthetics for data visualisation decades ago. He emphasised the fact that visual attractiveness may push viewers to find deeper meaning in it. Today, Michael Mahoney explains to data scientists why and how aesthetic decisions are essential to data visualisation. Despite the use of artistic tools, Few believes that data visualisation remains a purely informational tool: “whatever else it does, it must inform.”
Many others view data visualisation as an art form whose purpose is to entertain, rather than inform. It does not necessarily have a functional role, Li argues. Instead, it can be used to create an emotional experience (309). She claims that these works can do more than simply identify patterns: they can generate enjoyment and increase user engagement (308). “These artworks transcend the mere function of conveying information,” confirms Sey Min. He cites the Flickr Flow piece as one which has no functional purpose — it exists purely as a piece of art.
A third school, of scientists and artists alike, has highlighted the importance of collaboration between both fields to reach a happy middle ground. Michael Holh, for example, notes how data artworks can create evocative, memorable and meaningful experiences if they come as a result of successful interdisciplinary collaboration (1, 7).
All about the process?
Whether audiences view the works as informational tools or artworks is a whole different question. Although we live in a world governed by algorithms, their understanding remains limited to a small group of people, such as computer scientists and programmers (Hutahmo). Recently, the hiding of code has been privileged, creating a significant divide between those who understand coding languages, and those who do not. This disparity in knowledge creates different experiences for viewers: “The different background of people causes what they see in the visualisations and the kind of knowledge they obtain from them,” confirms data artist Kim Albrecht. A music visualiser is a great example: while an average user may simply enjoy the visuals, a more informed viewer may be able to understand the data behind them (Holh). A similar phenomenon occurs in Machine Hallucinations: while an average visitor may sit in the main room and enjoy the immersive experience, a more informed viewer may go into the backroom and gain access to the code and algorithms behind the piece.
Data and Storytelling
Despite these divisions, there is one way to make data equally accessible to everyone: stories. At first glance, data and narrative seem incompatible. Lev Manovich goes so far as to call them “natural enemies, competing for the exclusive right of how to make meaning out of the world.” (Manovich 85) However, a closer look shows that the two have begun to work hand in hand. Terms usually linked to narrative have increasingly made their way into the world of data. “A good graphic tells a story,” says Mahoney. Refik Anadol claims that his goal with Machine Hallucinations was to turn machine learning into a narrative. Angela Schöpke, an employee at IB5K, claims that one of the biggest goals of their company is to understand the stories that data sets tell, and subsequently find engaging ways to tell them. “Transforming raw datasets into visual narrative helps us understand relationships, correlations, and stories in our data in a way that we may not immediately be able to see in looking at raw datasets,” she explains. Famous data artist McCandless also claims that his work unearths the stories behind data. “I’m interested in how designed information can help us understand the world, cut through BS & fake news, and reveal the hidden connections, patterns and stories underneath,” he writes (Haridy). Data visualisation is thus no longer merely a tool to help with analytics; but also a powerful storytelling tool. “We now aim to tell a story that provides insight or allows you to take action,” says data analyst Caitlin Willich.
Considering its grounding in reality, data art does not help tell just any story, but especially stories that relate to the current state of the world. Many data artists use their works as a way to critically engage with topics from the environment to migration. “Data artists are not content with making legible the mesh of information from which it is formed, they also take a critical look at our society,” says Grugier. Debates surrounding climate change and sustainable development and ecology will probably propel this trend even further, as the need to critically reflect upon them increases. In 2008, for example, the WWF commissioned a project related to species endangerment. Other artists have used data art as a way to critically engage with data itself. Kim Albrecht has decided to focus her future works on raising awareness about data collection, after realising that people are not adequately educated about how to manage their data trails.
“Today, we are not the customers of these tech giants; we are the producers of data for them (…) It’s unpaid work we put into these pages with every search, every like and comment. Awareness of these processes will be the first step, and I think visualisation can play a tremendous role here,” says Albrecht.
Moving Forward
Though there is still a long way to go before data art can have both its process and final state understood by both artists and computer scientists, it is clear that it is moving in the right direction. Both scientists and artists have realised that their collaboration can lead to meaningful works that create engaging experiences for audiences, while also sharing essential messages, and making sense of data that may otherwise never see the light. Data art can thus be seen as a way to democratise data, making it available to wider audiences. The next step is to make the ‘scientific’ side of data art more readily available to non-scientific audiences — so that ‘right brains’ like myself can understand both the artistic and scientific sides of it.
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