Top data scientists often share three characteristics: they are creative, they are curious and they are competitive. Anthony Goldbloom, CEO of Kaggle, a company that hosts data prediction competitions, has figured out how to tap all three of these characteristics to help companies crowdsource their analytics problems.
During a 2008 internship at the Economist, economist-turned-journalist Anthony Goldbloom made an important discovery: many organizations that want to use predictive analytics don’t have the skills to do it well, if at all. In response, he came up with an interesting business idea: create a company that allows anyone anywhere to compete to solve other business’s analytics problems.
Today, hundreds of organizations, both public and private, have submitted their datasets and business problems to Goldbloom’s company, Kaggle Inc. Sixty five thousand data scientists from around the globe have signed up with Kaggle to compete to develop the best algorithm for a given problem, from predicting click-through rates on ads to predicting who will be admitted to a hospital within the next year (a current competition with a $3 million purse). Ongoing results are displayed in real time on a virtual leaderboard.
The leading data scientists are a motley crew. They include a French born actuary, a computational neuroscientist from Harvard, an Oxford physicist and a Russian cybernetics professor.
In a conversation with MIT Sloan Management Review data & analytics contributing editor Renee Boucher Ferguson, Kaggle founder and CEO Goldbloom talks about the motivation behind Kaggle and the outcomes of its data science competitions.
So where did the idea come from to make data science into a sports competition?
I used to be an econometric modeler at the Australia Treasury, and after that at the Reserve Bank of Australia. Then in early 2008, I won an essay competition. The prize was a three-month internship at the Economist magazine, writing for the finance and economic section.
I pitched a piece on predictive analytics and it turned out to be a fabulous way to do market research. Pretty senior people were saying, “yes, predictive modeling, I know we need to be doing more of this.” And my frustration was that I was talking through some really interesting problems with reasonably senior people from companies, and always in the back of my mind was, “wow, I do a bit of programming. I can understand business problems and also like playing with data and statistics. I could do a really good job on these problems.” But I realized if in a different context I applied for a job to tackle some of these issues, I wouldn’t get one.