{"id":4411,"date":"2017-03-19T17:09:50","date_gmt":"2017-03-19T21:09:50","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/3-kaggles-krowd-woes\/"},"modified":"2017-03-19T17:09:50","modified_gmt":"2017-03-19T21:09:50","slug":"3-kaggles-krowd-woes","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/3-kaggles-krowd-woes\/","title":{"rendered":"3: Kaggle’s Krowd Woes"},"content":{"rendered":"

Earlier this month, Google acquired Kaggle, a platform that hosts competitions to crowdsource machine learning solutions to company-submitted problems.\u00a0 Companies post a problem and an accompanying dataset to the Kaggle community of some 140,000+ members.[1]<\/a>\u00a0 The winner receives a prize, historically ranging from $3,000 to $3,000,000.[2]<\/a>\u00a0 For instance, Deloitte recently posted a $70,000 prize for the algorithm that best predicted which customers would leave an insurance company in the next 12 months.[3]<\/a><\/p>\n

The Kaggle community consists of both data science experts and less experienced users.\u00a0 In fact, it seems that anyone can create an account: I was able to sign up with my personal email and even had the option of entering a competition.\u00a0 That said, users are subjected to an incredibly raw level of transparency that self-police submission quality.\u00a0 Competition entrants are ranked on a public leaderboard with statistics on how their rank has changed over the last week, total entries to date, and score.\u00a0 Furthermore, companies can limit competitors to the top tier of Kagglers through a service called Kaggle Connect<\/em>.[4]<\/a><\/p>\n

The community comes to Kaggle for more than just prize money, however.\u00a0 The platform includes a substantial learning component, Kernels, <\/em>where users can download the latest libraries, learn cutting-edge techniques, engage in discussion, and give and receive feedback on others\u2019 work.<\/p>\n

Finally, Kaggle offers companies recruiting exposure via its jobs board.<\/p>\n

 <\/p>\n

Value Creation and Capture<\/u><\/p>\n

Like many crowd-sourcing efforts, Kaggle creates value by allowing companies to affordably tap a long-tail of data science talent.\u00a0 But the key value creation element here is the transparency<\/strong> it creates in data science outcomes, which drives both company postings and community engagement from actual experts<\/em>.\u00a0 The public manner in which leaderboards publish the efficacy of solutions and surface talent encourages user competition and engagement.\u00a0 This has created a certain brand equity around the Kaggle ranking.\u00a0 Not only do users take pride in their rank, but employers have begun asking for it in job posting responses.[5]<\/a>\u00a0 This transparency also promotes innovation<\/em>, where users can easily identify winners and learn and continually hone the latest techniques.<\/p>\n

Kaggle captures value<\/strong> through fees from its competition and job postings.<\/p>\n

 <\/p>\n

What went wrong<\/u><\/p>\n

In 2015, Kaggle faced earnings pressure, laying off 1\/3 of its workforce and shutting down a pivot attempt that provided consulting services to the energy industry.[6]<\/a>\u00a0 While the team blamed market cyclicality, additional elements were likely at play here \u2013 for both the consulting service and Kaggle overall:<\/p>\n