Crowdsourcing: Cool, Not Magic

If you missed it over the weekend (as I did), there's a very good column in Saturday's New York Times about crowdsourcing.

Until recently I had only a vague notion of what crowdsourcing was all about. But the Internet lets you catch up on this stuff pretty fast, so I've learned a lot in the past few weeks. Steve Lohr's piece takes a look not at just what crowdsourcing is, but at what makes it work, for there's more to it than assembling a crowd and turning it loose on your problem.

"There is this misconception that you can sprinkle crowd wisdom on something and things will turn out for the best," said Thomas W. Malone, director of the Center for Collective Intelligence at the Massachusetts Institute of Technology. "That's not true. It's not magic."

What you need to sprinkle on something instead is planning, methodology and strategy.

A look at recent cases and new research suggests that open-innovation models succeed only when carefully designed for a particular task and when the incentives are tailored to attract the most effective collaborators.

One case Lohr cites is Netflix, the online DVD rental service (fanboy note: I'm a huge fan of Netflix. It has removed for me the sting and stigma of not having premium cable stations). Netflix's approach to crowdsourcing was pretty bold, and should not be tried at home:

In October 2006, Netflix announced that it would pay $1 million to the contestant who could improve the movie recommendations made by Netflixs internal software, Cinematch, by at least 10 percent. In other words, the company wanted recommendations that were at least 10 percent closer to the preferences of its customers, as measured by their own ratings.

Naturally, with that kind of stake, Netflix attracted some serious intellectual firepower to the competition:

The frontrunner is a seven-person team, and its members are statisticians, machine learning experts and computer engineers from the United States, Austria, Canada and Israel. It is led by statisticians at AT&T Research. ... The teams in close pursuit are similar collaborations of skilled researchers and engineers.

Many more examples of crowdsourcing can be found at what is perhaps the most famous Internet project relying on collective crowd knowledge: Wikipedia.