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The Art of Data Visualisation describes a practice of displaying information effectively, attractively and also for artistic expression. In general, the term can be related to information design in connection with graphic design. The primary goal of data visualization art is to create a narrative to communicate a visual message that has greater power to explain, inform or persuade an audience. Since the creation process of data visualization involves different stages, several subjective decisions or artistic interpretations can be made. As a result, a visual representation that at the same time is aesthetically pleasing triggers a recipient’s emotional reaction and provokes what the artist intended to message. By making artistic decisions in the architecting of visualization, certain data is being prioritized. Therefore, certain context is being excluded and the design focuses on selected dimensions of scientific truth. Whereas data visualization is a data-centric, pragmatic and technical perspective, ‘data art’ aims to communicate a concern rather than showing the data at the core of the visualization.

Overview
Data Visualization in general involves the creation of visual representation of data and is used as a media literacy of visual communication. The presentation of data in a pictorial and graphical format serves to communicate information accurately by making complex data more accessible. By means of effective visualization, complex data is being simplified in order to communicate a lot of information more easily and quickly. Data Visualization, as a modern equivalent of visual communication, lies at the intersection of science and art. According to Edward Tufte, an excellent visualization involves "complex ideas communicated with clarity, precision and efficiency.”

Since the graphical component is the first element of a visualization, certain visual elements can effect the recepient's perception. In the context of artistic visualization, data art aims to improve the appearance and adds aesthetic aspects to the original essence of information. Research scientist Robert Kosara mentions in “Visualization criticism- the missing link between information visualization and art” two different types of visualizations: “[…] very technical, analysis-oriented work on the one, and artistic pieces on the other hand”. By proposing different types of information visualization based on aesthetic criteria, he bridges the gap between design, technical information visualization and art. As stated by Kosara, the notions of artistic visualization focus on communicating a concern and to ensure the message reaches the target audience. This concept can be complemented with Tufte: “[…] excessively original artwork just plays around with the information”. When it comes to the aesthetic aspect of data visualizations, artists tend to have a strong idea on the subject matter. By transforming certain phenomena that are beyond the scale of human senses into something visible and also beautiful, emotions are being evoked.

The Anti-Sublime Idea in Data Art
Media theorist Lev Manovich made observations of data mapping in art and discussed in his essay “The Anti-Sublime Ideal in Data Art” several projects of artists dealing with data visualization. As mentioned by Manovich, “data visualization artists transform the informational chaos of data packets moving through the network into clear and orderly forms”. When referring to the modernist artists of the early 20th century, such as Piet Mondrian, he makes a relativistic comparison with today’s visualization artists. Both engage in a similar reduction of “vast and seemingly random data sets”, which is why data visualization can be seen as a new form of abstraction. Instead of highlighting just minimal structures and patterns, the visualization of data ''“moves from the concrete to the abstract, and then again to the concrete”.

If Romantic artists thought of certain phenomena and effects as un-representable, as something which goes beyond the limits of human senses and reason, data visualization artists aim at precisely the opposite: to map such phenomena into a representation whose scale is comparable to the scales of human perception and cognition.

According to Manovich, data visualization art strongly aligns with modern science in order to make phenomenon outside of the scale of human senses visible. As a result of digital technology and the use of computer medium, new varieties of visualization techniques have been created. Computers enable to visualize much larger data sets and create dynamic visualizations by means of animations and interactive features. Data art is a showcase for the intersection of art, design and the interface, and should "portray human subjectivity - including its fundamental new dimension of being 'immersed in data'. In the Information Age, mankind leave its trace and history by means of digital devices and as a result huge amount of digital data can be collected. By visualizing the online activities of users and collecting these data sets, various facts about humanity can be represented and used for artistic purposes.

For me the real challenge of data art is not about how to map some abstract and impersonal data into something meaningful and beautiful - economists, graphic designers, and scientists are already doing this quite well. The more interesting and at the end maybe more important challenge is how to represent the personal subjective experience of a person living in a data society.

As stated by Lev Manovich, data art can be seen as the anti-sublime version of art. Compared to Romanticism, which emphasizes the subjective experience and emotion by visualizing the un-representable, data visualization art focuses on facts and truths about humans and their surroundings.

Characteristics of data art displays

 * Dealing with (live) data sets in various media formats
 * Finding a narrative (story) in complex rich data sets that are generated by people (data mining, examining relationships and patterns)
 * Visualizing how humans relate to culture, societies, technologies and to their environments
 * Combining different elements of various studies (visual art, programming, information design, storytelling)
 * Taking data out of the scientific and governmental context
 * Builidng artworks that cause to confront questions around data sourveillance, identity, privacy and data exploitation (data manipulation by large companies)

Data visualization art ranges from real-time information graphics systems to innovative sound installations or various forms of data mappings.

Examples
One of the thought leaders of data visualization is Aaron Koblin, an American digital media artist, best known for his artistic and innovative use of data visualization. In 2006, his “Flight Patterns” (an interactive visualization of a single day’s air traffic over the United States) received the National Science Foundation’s first place award for science visualization. By visualizing data about aircraft models, flight routes and altitudes, Koblin’s work has been exhibited internationally. Aaron Koblin at the TED conference in February 2011:

I think data can actually make us more human. We’re collecting and creating all kinds of data about how we are living our lives and it’s enabling us to tell some amazing stories.

Another example is the conceptual new media artist R. Luke DuBois, who constructed a census of the United States based on the analysis of the online dating profiles of 19 million single Americans, and created a re-labeled map of North America showing a lexicon of American self-identity in the 21th century. His work "The Perfect Union" has been exhibited worldwide.

Critics
One of the most discussed topics in this field of information visualization is based on the necessity or in-necessity of certain visual elements. As Chief Design Officer of Apple Inc. Jonathan Ive mentioned: ”We spend most of our time getting design out of the way”. The general debate questions if design shall be minimized and simplified, when the focus lies in the relationship of the viewer and how they reason about the content. In the context of data art the aesthetic aspects are the center of attraction in order to communicate a specific concern. Beautiful visualizations can be a byproduct of the truth and the goodness of the information, which is why artistic forms of visualization do influence the original essence of information, its data. As a result of subjetcive decision-making in the creative process, biases can never be totally avoided when working with data. Robert Kosara introduces the term Visualization Criticism to analyse the differences between information visualization and other forms of visual communication based on aesthetic criteria. Following Kosara's train of thoughts, new artistic forms of visualization can redefine what is common to data visualization.