Medialab Prado

What kind of images are appropriate for the needs of a global informational networked society – the society which in all of its areas needs to represent more data, more layers, more connections than the preceding its industrial society? The complex systems which have become super-complex; the easy availability of real-time information coming from news feeds, networks of sensors, surveillance cameras; – all this puts a new pressure on the kinds of images human culture already developed and ultimately calls for the development of new kinds.
Lev Manovich

According to the well-known blogger Jason Kottke, worldwide newspapers publish over 6,000 terabytes of data every day. Technorati, the weblog search engine, keeps up to date with 54 million blog users. Flickr, the famous photo sharing website, has amassed a file of two billion labelled and classified photos in only two years. 70,000 videos are uploaded to YouTube every day. Google now has access to almost nine billion documents and websites. Wikipedia's volunteer encyclopaedia authors have written one million, four hundred thousand articles, fifteen times more than fit in the Encyclopaedia Britannica. We are flooded with information. And there is no end in sight.

At the start of the 21st century, the generation of endless masses of data has become one of the primary scientific, economic, and social activities. However, our cognitive capacity has not grown to match this exponential increase in information to be interpreted. Attempting to read these new texts with our limited vocabulary can be terribly frustrating.

Masses of data are such vast, complex structures that perhaps the best way to make everyone understand the relations and see the meaningful patterns hidden among them is not to use words. Maps, signage, and statistical graphs have been traditional ways of showing the relationship among specific items visually. However, now that we generate and gather much more data practically in real time, we need systems that represent them dynamically and the answers they hide. Thus, the art and science of data visualization was born.

Data visualization is a cross-discipline which uses the vast communicative power of images to offer a comprehensible explanation of the relationship among meaning, cause, and dependence that can be found among large abstract masses of information generated by scientific and social processes. Arising from the field of science two decades ago, InfoVis and DataVis combined strategies and techniques from statistics, graphic design and interaction and computer analysis to create a new communication model more suitable for clarification in the emerging Age of Complexity.

In recent years-- mostly thanks to the work of artists at the crossroads of art, science, and technology— data visualization strategies and aesthetics have gradually gained acceptance in ever-broader cultural areas. Artist-researchers like Martin Wattenberg and Bradford Paley have taken them into the stock market, designing the systems used by Wall Street brokers to show the constant variations in the stock market. Activists like Josh On have used it to show the complexity of the economic interdependencies of global capitalism. Projects that are essential to understanding how digital art has evolved over the last decade, such as Listening Post by Ben Rubin, or Carnivore by RSG, are based on the visualization of data flows as a way to generate new representations of invisible layers of reality, like social activity in communication networks.

In addition, generating and classifying data has become a daily social activity for millions of Internet surfers in the era of blogs, wikis, map maker systems, and social networks based on services exchanging photos or videos. The scope of social interactions in digital space is as rich in information as genetic maps, stock markets, or organizational knowledge charts for huge multinational corporations. For that reason, it is no surprise that we can analyze and interpret it with the same techniques.

The analysis of the relationship between data and their visual representation has transcended its scientific origin and can be seen as a language with great potential in a context where data bases are fast becoming, as Lev Manovich has suggested, an influential cultural form. Freakonomics, the recent best-seller about the work of controversial economist Steven Leavitt, brings these strategies into everyday life, applying analyses of great masses of information to apparently trivial matters like the influence on job success of certain first names. And there are indications elsewhere: information analysis experts like Adrian Holovaty address the need to forge a new type of journalism which is based less on reporting on events than on correlating data so that instead of “uncovering covert information (as occurred in Watergate), journalists would trawl through the vast amount of public data in existence to find interesting stories there (as occurred with the Enron case)”.

The dynamic representation of information flows is taking place on other surfaces as well: on huge media facades that transform buildings into liquid architecture and in the design of spaces where lighting and data representation systems begin to fuse into a sort of hybrid.

The creation of a new kind of images that makes it possible to interpret all this complexity in an intuitive, map-like way, enabling us to extract a deep understanding out of this mass of information, is one of the greatest challenges facing contemporary creators.

The VISUALIZAR Project aims to explore the social, artistic and cultural applications of data visualization through a broad program of activities including reflection, research and the production of knowledge. The aim is to bring these interpretive keys to new fields of work where their potential uses are many (from investigative journalism to social or environmental activism) and to take an in-depth look at present day artistic production, which has taken on the role of creating this new kind of images suitable for a culture of complexity.

José Luis de Vicente (translated by Karen Neller)