Crowdsourcing is distributed problem solving. You take one task (or many smaller ones) and spread it across a crowd of hundreds to thousands to solve. In our case, it’s translation, but you can also use a crowd for reviews, design, research… nearly anything. You’ll find crowds these days almost everywhere you look.

Back in October, Gengo attended CrowdConf, the crowdsourcing industry’s annual conference. It was interesting to see how established the industry is, with an entire session dedicated to legal considerations in crowdsourcing and representatives from major enterprises such as Bloomberg, eBay and LinkedIn.

My favorite session was a panel discussion entitled “Things I Wish I’d Known When I Started Crowdsourcing” with crowdsourcing old hats Patrick Booher (Autodesk), Jason E. Rollins (Thomson Reuters) and Matt Swanson (Silicon Valley Software Group). Booher’s response in particular struck a chord: he wished he’d known he was trying to solve a labor problem, not a data one.

In his role as Director of Enterprise Data Management at Autodesk, Booher uses CrowdFlower’s crowd to ensure the accuracy and completeness of millions of customer records. It’s the fastest and most scalable way to get the job done, but it doesn’t come without challenges, including the unexpected challenge of managing and training a global workforce of thousands, aka Booher’s “labor problem”.

In an interview with Diginomica’s Phil Wainewright, Booher reiterates: “Your results are only as good as the jobs and training that you write. How do you break down your job into very simple tasks and how do you train the crowd to do those tasks?”

This got me thinking about Gengo: would our customers get a better result if they approached their translation problem as a labor one? Yes and no, I think. Yes, in that it’s important to design translation jobs with the translator in mind. Much in the same way that CrowdFlower’s results are only as good as the jobs and training customers write, translation quality is heavily influenced by the quality of the source text and context provided.

However, Gengo (not its customers) is primarily responsible for training and managing its translator team. We create the tests, uphold standards through testing and reviews and offer training in the form of regular expert feedback. As Community Manager, I work closely with our translators to ensure they have the tools and resources they need to perform their jobs well. These factors distinguish Gengo’s crowd from many others.

In fact, I think my biggest takeaway from CrowdConf is that “crowdsourcing” is a very broad term, representing a wide range of crowds and business models including skilled vs. unskilled, generic vs. specialist, paid vs. volunteer, high vs. low barriers to entry and everything in between.

People may be wary of using crowdsourced solutions because they are popularly associated with low quality, but it’s important to keep this diversity in mind. Crowds are constructed and maintained in different ways for different purposes. Customers will get their best results with crowdsourcing only after knowing which kind of crowd they’re using and why.

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