The Data Painter and the Data Poet
What is a data artist ? A few thoughts on data as a material for creation.
In the 19th century in Europe appeared a new genre of poetry called symbolism. It was initially built as a reaction to naturalists and their vision considered too pragmatic and too rational of the world. The poets who joined this movement believed that poetry should be more abstract than descriptive. They thought that poems have to evoke emotions from sonorities. However their base material did not make it easy for them.
Unlike painters who use color pigments as base material, poets use words. Pigments can at best evoke ideas or memories, but they don’t mean anything. A word however does have a significance which is linked to reality through its meaning. The issue for poets is that this significance can change and fade away with time. What will be left of their poems a hundred years later ? Realizing this made some of them doubt the language.
They decided to solve this problem by using rare words whose meaning had already faded away. This way, the images they would create in the poems would stay abstract and not be impacted by time.
In the digital society, flows of data have to be transformed continuously into flows of information along the way from the sensors to our eyes. Thus the filed or data visualization grew as fast as the need for optimal understanding of that data.
As a reaction to the ubiquity of these data visualizations and their pervasive rationality, a new form of art called data art is appearing. Its base material is not the pigment or the word but numbers, lists or files.
Yet data have, as well as words, a special status. They can have a significance. And this significance is a bridge to reality. But unlike the word, the datum disappears in the sea of data. While in the English language there are about 505,000 different words, every human being creates on average 1.5Mo of data a second. As meaningful as it can be, each piece of data’s meaning is being erased by billions of billions of other pieces of data. Just like the poets who eventually invented symbolism, the data artists realize that their base material is significant and that this significance fades away.
Data look more like pigments than words in the way they are represented. Each piece of it is rarely discernible from the whole. The bars of a bar chart are drawn just like a painter makes brush strokes on a canvas.
Therefore data artists face a duality in their material. They have to choose how much weight to give to the significance of data. If they decide to ignore it, they get closer to the painter. And if they embrace it and question it, they get closer to the poet. Just like the symbolists, data artists place themselves in-between their freedom of creation and the natural fading of the material.
At one end of the spectrum, data painters create sensorial impressions disconnected from the meaning of the data they use. They emancipate from the reality that lies into the material and make room for the expression of their artistic freedom. Nevertheless, the data and its meaning still impregnates their work. Their intention is elsewhere but their creation is still the result of the abstraction of real phenomenon. Refik Anadol and his Data Paintings or Kirell Benzi are good examples of this way of creating. They are still unjustly accused sometimes of using the data as a trendy marketing strategies.
At the other end of the spectrum are the data poets. Their art subject is our relationship to data and their meshing with reality. They give up a bit of freedom in the creative process, and accept limitations imposed by the meaningfulness of their material. They “show” the data and invite to a discussion upon their shapes. For example, data poets can address topics such as the nature of data, privacy in the digital communities, manipulation of representations, data literacy or the gap between data and reality.
Regardless of the position the data artists choose on the spectrum, they all seem to share the same doubt of the scientific codes established by the data visualization and data science communities.
It’s hard to say if data painters and data poets really exist. Most data artist would define themselves somewhere in-between and would add some more dimensions to this classification. Here are a few examples of data paintings and data poems:
Data paintings
Imagined Time, Laurie Frick
Laurie Frick gives her vision of the passing of time through a series of collages using collected paper pieces as data. She does not try to ask or to answer any question. She felt something about time that she tries to transmit to us. She writes this simple sentence on her website:
Imagined Time, found cut and handmade paper on panels, a series based on the memory and experience of time.
Wildfire Progression , Adrien Segal
In her project, Adrien Segal finds new ways of representing Californian wildfire propagation data. She sculpts variations in propagation in the form of aesthetic ashes. With it, she touches the destructive impact of humans actions on the environment. She says:
Sculpture is an aesthetic language I use to bridge the gap between reason and emotion […] my artwork synthesizes scientific research from information into knowledge through an intently subjective human experience […]
Data poems
Interactive projects, Ying Gao
The Canadian artist Ying Gao explores the intangibility of data. In Incertitudes, she tries to revitalize the conceptual dresses she creates through the invisible flows around us. Some of her dresses respond to sight and start moving when watched, some others start moving when unable to detect any activity around. The artist questions our relationship with the digital world that creates a permanent and invisible outside eye on us. She said:
It’s a way to invite people to stay stoic, stolid. I think it’s the opposite of what society wants from us. […] We are expected to react all the time.
A hypermodern individual is a being of the here-and-now, pressured by a logic of urgency, and worried about the future.
Collection of data visualizations, Mona Chalabi
As a data journalist and artist, Mona Chalabi creates data visualizations drawings. In her representations of data, she tries to include the subject in the drawings. The subject being the spectator of the data visualization. She plays with illustrations to combat data illiteracy and misinformation. Like Giorgia Lupi or Stefanie Posavec, Mona Chalabi takes a data humanist approach and criticizes several aspects of classical data visualization. She said:
Part of the purpose of creating hand drawn illustrations is that I want people to question it. Because the truth is that there is a high degree of imprecision in data. […] In each statistics you see, the truth lies somewhere in the perimeter around that number.
On their way to symbolism and emotions, data artists often broke traditional rules and killed the axes and labels, like the poet killed the language by using incomprehensible words. They don’t want to describe, they want to evoke through abstraction of the world.
About the author
🙋♂️ Hello there! Thanks for reading through! I’m Guillaume Meigniez, a Data Visualization Society member obsessed with understanding what aesthetic things are made of! Please share your thought with me about what you just read 🙂
