Art is Data – Guest Post by Juan Luis Suarez

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Juan Luis Suarez
Juan Luis Suarez

I’m honored to have Juan Luis Suarez, the head of CulturalPlex, as a guest writer on The Artian.

We met each other at IE Business school and our common passion for art and technology led us to many conversations around these topics and how we can bring them to life. The projects I got familiar with through Juan Luis are interesting, eye opening and exciting. You should visit CulturalPlex’s website.

Juan Luis is a Professor as well as the Director of the CulturePlex Lab at Western Ontario University in Canada. His research deals with cultural complexity and complexity theory, digital humanities, technologies of humanism, Hispanic Baroque, as well as globalization and new literature. CulturePlex’s team research digital innovation, content analytics, lean big data, social media, and digital humanitie.

As The Artian has shown many times, art, business and entrepreneurship belong together in the innovation ecosystem. Looking at them as nodes in the same network of creation offers enlightening insights on how the world is changing around us. Data, Big Data, is just one more way of discovering many of these multi-directional pathways.

Art startups whose business model revolves around data abound these days. Artsy, one of the most preeminent companies in this domain, connects the data produced by users while surfing art –digital images– with the metadata collected about the artworks. Once you have those two types of data and the connections –connections are as important–, the next step is to create a probabilistic model that will “predict” what the user will look for, and present it to her as a recommendation. The goal is to create a personal experience around art surfing that will end up in a percentage of cases in purchases (first revenue stream). Even if there are no purchases, the data about the user’s behavior is used to offer targeted digital ads (second revenue stream). Finally, the reselling of users’ data may become another (third) powerful revenue stream. All these while promoting art and creating an exhibition place for artist to showcase their work and, in a few cases (all these models are based on the principle of “the winners take it all”), make some money.

Node Map that tracks Ruben's painting influence
Node Map that tracks Ruben’s painting influence

I like a lot the approach of Wondereur. In Wondereur you find curated photo-reportages about specific upcoming artists. They are in-depth, beautifully visualized, explorations of the lives and works of very interesting artists. The experience is very personal for both the profiled artist and the reader that gets to know the artist and value her work in a special manner. In commercial terms, the goal of Wondereur is to entice the reader through curiosity and making this the bridge into buying the works they present at the end of the experience. Young professionals and art devotees who are starting their collections are the main target, but also professionals and companies that invest in art are using Wondereur as a guide into new talent. But where is the data here. It is in the categorization of art works by topics (love, war, anger, sorrow, happiness) that will help the user to navigate across artists and works. By adding new doors into the art experience they are ready to explore the user’s behavior, and they know which users relate to which topics because they have recently implemented a login system.

These are just two examples of successful startups that have been able to combine the most personal side of art with the most objective and analytical ways of exploiting data. I think that they are also the avant-gard of a wave of companies that will disrupt the art space in the next few years because there are so many things that you can do when mixing up art and data.

In the CulturePlex Lab at Western University we have been working on different aspects of this domain. For instance, we collected and analyzed a database of over 15,000 baroque paintings to study the transfers of models and ideas across the Atlantic during several centuries. You can explore this at the following link.

More recently, we have studied and quantified the evolution of beauty throughout the history of painting. We have been able to determine the moments and ways in which the representation of the human face has changed in paintings. By using data analysis, face recognition algorithms and symmetry measures we have discovered that the canon of beauty has change a lot over time and that at the turn of the 20th Century artists stay away from painting faces. Actually, with the avant-gards not only are faces depicted in non-figurative ways, they disappear almost completely from the artists’ mental frame.

During the course of this research project we decided to develop a web app that would allow people to compare themselves to faces in the paintings database. The result is an interactive tool that offers the user a different way to experience art. They just go to the computer (, take a selfie and the app will return a face that is the closest to the user’s measure of facial symmetry. We thought that this would be a fun way to get art lovers and users in general closer than ever to the history of art.

Data constitutes a great way to study art, to create unique business models for art-oriented companies, and to offer users both online and in museums and art galleries new experiences that will enrich and personalize how they live art. And on the way, they will collect more and better data to enhance their users’ tastes and preferences. As in other sectors of the economy, it is time to take advantage of Big Data in the art world.

-Juan Luis Suarez

Watch him talking about their projects:


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