I have spent a lot of time recently with the website Artsy.com. That’s in part because I find it fascinating, and in part because it is trying to accomplish so many things at once. At one level, it’s a fine art discovery service. Artsy has partnered with art galleries nationwide to allow them to post images and descriptions of artwork for sale. That’s a great convenience for someone looking to buy art, but it’s not a new idea. A number of services are already offering similar services.
Where Artsy separates itself from the pack is with its “Art Genome Project.” Essentially, Artsy is categorizing artwork against a taxonomy much as Pandora has done for music. And that’s where the magic happens. If you find a piece of art you like, you can easily explore other artwork with similar characteristics. That’s no small feat when you consider that Artsy already catalogs over 125,000 pieces of art from over 25,000 artists – and it’s still only scratching the surface of what’s available.
Artsy is what might be characterized as a next generation discovery tool. Certainly, there’s value in aggregating artwork from galleries all around the country. But the big breakthrough here is being able to point to a single piece of art and say “I’d like to be able to see art in a similar style by different artists.” That’s a powerful step forward on discovery that benefits both the art buyer and the artist. If you’ve got a market where there are huge numbers of manufacturers, little standardization and prodigious output (music, art and wine are great examples), there’s a next generation discovery opportunity waiting for you.
But what about Amazon and Netflix, you may ask? These companies too have done a lot to improve discovery, but in a very different way. These companies don’t look so much at the product itself as who bought the product and in what combinations. This is a powerful and effective approach, but what Pandora and Artsy did was build taxonomies based on inherent product characteristics and then committed to manually classifying products against them, a significant exercise in metadata creation, but one that yields powerful, proprietary results.
The other interesting aspect of Artsy is one that I initially viewed as an overreach. Artsy includes not only artwork for sale, but artwork in museums and private collections. Certainly this makes artsy more attractive to art lovers, art students and others, but it seemed to confuse somewhat the art buying experience. This is largely explained by the fact that Artsy has a mission-driven aspect to it, but there may be a huge business opportunity here as well.
If Artsy is doing all the work of classifying and posting images of artwork held in museums and private collections, why not go one step further and become an international registry of artwork? Much of the value of art is tied to its provenance – its history of ownership. Artsy could become a central registry, collecting a small fee to re-register a piece of art every time it changed hands. It could then sell subscription-based access to this database to auction houses and galleries. Lots of details to be worked out to be sure, but this notion is only a small jump from Artsy’s current ambitions.
That’s what makes the data business so fascinating and lucrative: there are infinite opportunities to make money. All it takes is some creativity, and in this case, re-framing an existing business model.