Monetizing a Crowdsourced Data Scientist Existence
Can project-oriented crowdsourcing initiatives put food on data scientists’ tables?
Industries may be succeeding beautifully, from a financial standpoint, while failing to enrich the majority of the people who actually do the work. If this concept sounds new, there are various economics texts—such as Das Kapital1—that explain how it’s been going on for many years.
Likewise, growth markets may be hiring people right and left, but that doesn’t mean all of those people can make ends meet without holding down a second or even a third job. The very fact that many people are competing for precious few glamour jobs gives employers a lot of leverage to pay less than a living wage. When jobs are project based, rather than steady clock-punching affairs, employees need to worry about how they’ll put food on the table between projects. And when project-based work is competitively bid, employees become contractors who may not have any of the cushy benefits associated with full-time, salaried work.
A job is any engagement that enables individuals to monetize their time. Professional data scientists seeking to monetize their time to the hilt have to wonder whether it makes more sense to hold a steady, salaried job or strike out on their own as independent contractors. And if the latter option is desired, there are crowdsourcing initiatives that offer data scientists the opportunity to win cash prizes from contests in an online project–oriented marketplace. Moreover, for those already participating in one or more of these marketplaces, such as Kaggle, the motivation may be either an attempt to pick up some spare cash on the side or take on a primary source of income.
If you are one of those people, how is that pursuit working out for you? In terms of paying the bills, are you also playing the lottery and placing bets on the ponies? Like no-guarantee moneymaking endeavors in which the odds are against you, these crowdsourcing initiatives are the equivalen—though Kaggle and similar communities lean a bit more toward the games-of-skill end of the luck-versus-skill equation. It’s all gamification.
Gamifying data science consulting
The recent article, “The Amazing Big Data World of Kaggle and the Crowd-Sourced Data Scientist,”2 compelled me to wonder if anybody, other than the company and its investors, is pulling in any serious ka-ching from its gamified data science community. We often hear about rock star data scientists, and very likely many of the participants in Kaggle aspire to that status, but the analogy is telling. Real-world multimillionaire rock stars are few and far between. Most musicians scrape to make a decent living, and even those who have a hit record worry that it may be the one and only time in their career when they get a substantial payoff from their efforts.
Kaggle’s crowdsourced gamification model seems designed to create a community of one-hit-wonder data scientists. How many of its participants have won more than one competition? Even if any have won just one contest, is the payoff enough to give up a day job? The cited SmartData Collective article states that rewards are “currently up to $30,000, although occasionally much larger for the top projects.”3 If you’re a professional data scientist, that average reward—the odds of which are small that you’ll ever collect—isn’t much better than what you might pull in with near-100 percent certainty from your next standard consulting project. The risk-reward ratio favors standard data-scientific consulting work over crowdsourced contests.
Am I missing something here? Is participation in these Kaggle contests motivated more by the intellectual challenge than any hope of cashing in? If so, Kaggle may be regarded as a mind-testing game show, such as Jeopardy!, where just a few take home the money and the rest get a brief chance to show the world how smart they are. And that approach is fine. It’s not exploitation; it’s an opportunity to build personal visibility in the data science field, which can be monetized through channels other than Kaggle.
Whether or not its participants are raking in the big bucks, Kaggle seems to be monetizing its business model well. The cited article states that Kaggle is profitable. It also notes that Kaggle is “charging companies they work with—including Amazon, Facebook, Microsoft, and Wikipedia—up to $300 per hour for consultancy work.”4
Crowdsourcing for the greater good
To be fair, Kaggle’s mission includes initiatives with a scientific or higher-education focus that don’t appear to be motivated by profit potential. Indeed, money isn’t everything for data scientists who are engaged in significant problems facing humanity. But money pays the bills, and working data scientists can’t crowdsource the funding of their ongoing living expenses from communities such as these. They know better than anyone that, statistically, the odds of hitting a Kaggle jackpot are stacked against them.
Please share any thoughts or questions in the comments.
1 Das Kapital, Karl Marx, first English edition published 1887, Progress Publishers, Moscow, USSR, 1887. Collector’s Edition, Synergy International of the Americas, Ltd., September 2007.
2,3,4 “The Amazing Big Data World of Kaggle and the Crowd-Sourced Data Scientist,” by Bernard Marr, SmartData Collective, July 2014.
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