Tim O’Reilly, founder and CEO of O’Reilly Media, talked about the tensions between individuals wanting to monetize their work and the broader value they can create by making their content available to everyone.

“I think there’s an area of our economy that’s really not studied enough, or it’s not thought about enough. What happens when people give things to each other without getting paid? Think about the revolution with YouTube,” he said, pointing to children choosing between a Disney cartoon or a video created by another child that was uploaded to the site. “From the point of view of a director who wants to get paid, that’s a negative thing, but from the point of view of the consumer, that’s a positive thing.”

O’Reilly pointed out that some people might think that free material, once widely circulated, would make it hard for others to eventually make money, but that might not be the case:

“Businesses do arise. The world wide web and open source software turned into Google and Facebook and Apple building all this incredible technology that they were able to monetize. So I’m interested in this economic activity that comes from this open source and open sharing. What does it tell us about the possibility of new jobs?”

These issues are very much at play when looking at companies like Twitter, which started with a free product embraced by a geeky, early-adopter audience and is trying to become a mainstream media business. O’Reilly cited the idea that once an item becomes free something else becomes necessarily more valuable. But Jeremy Howard, president and chief scientist at Kaggle, said he thinks for most creative types, there doesn’t have to be high demand for a product to keep them going:

“I’ve been coding every day for 30 years,” he said. “The idea that unless you create scarcity around intellectual property or creators will stop creating, is just crazy.”

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(c) 2012 Pinar Ozger. pinar@pinarozger.com

That’s why Huls and his team worked with Kaggle, a marketplace for data science competitions. Huls told the audience that the cooperation with Kaggle was the result of an interesting discovery: Some of Allstate’s own engineers participated in Kaggle competitions in their spare time. “My first reaction was: Clearly, we are not giving these people enough work,” he joked.

But then he realized that outside expertise could help Allstate build better predictions, which can directly impact Allstate’s bottom line. “How well you predict risk is the difference between being a successful and an unsuccessful insurance company,” explained Kaggle’s president and chief scientist Jeremy Howard.

However, there are also some risks in outsourcing this kind of data science. One of the biggest ones: Data scientists are smart folks, and they might figure out some things that you didn’t really want them to know, or include in their models. That can lead to data leakage and privacy issues, as Allstate experienced first-hand. The company’s data spanned across a number of years, and participants were able to figure out which customers had been driving which cars over a period of time. If you’re an insurance company, you might want to contain that data, admitted Huls: “In our competition, we didn’t do it very well, quite honestly.”

Watch the livestream of Structure:Data here.

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