4 Key Factors to Crowd Science Done Right

Filed Under Innovation

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Arwen Heredia

by
February 13, 2014

Crowd science is still a relatively obscure topic for a lot of companies, especially those who haven’t yet nailed down a successful innovation program. But for innovation management to be truly effective and ensure that businesses never miss out on great ideas, it’s not enough to use unbridled crowdsourcing — the methodology is equally as important as the philosophy.

Here are 4 key factors to implementing crowd science the right way.

1. Use Social Dynamics

The reason social media has influenced platforms and services is because engagement is key to utilizing crowdsourcing. Social dynamics add a layer of engagement that’s both disarming enough to attract participation, and mechanized in a way that’s conducive to data tracking. Sharing ideas, having discussions with colleagues, and browsing through everyone’s comments happens in an environment that employees typically associate with leisure, allowing the social aspect of your approach to drive ideation and brainstorming. And with engagement levels at a disheartening low, indolence is not something you can afford in your innovation program.

2. Make Collaboration Purposeful

We’ve talked many times about what collaboration is (a necessary part of project effectiveness and overall business success, and a working method that helps amplify and polish ideas), and what it isn’t (throwing a bunch of people together on a project and telling them to work together). The biggest difference between the two? Intent. When stakeholders come together to attack an issue, identifying goals and determining structure is the best way to avoid confusion, frustration, and failure. Never collaborate just for collaboration’s sake — make it purposeful, and be aware of whether or not it’s adding or detracting from business benefits. For example, crowdsourcing for innovation is a collaborative process, but once an idea has graduated beyond the noise, narrowing participants down to those who can drive the idea through to completion — and experts to vet them — is critical to avoiding bottlenecks.

3. Do the Math

Perhaps most integral to using crowd science effectively is understanding that it’s a highly mathematical process — or rather, that it should be. Voting and game mechanics, coupled with algorithm-driven outcomes, automated idea graduation, and expert reviews are all decisive pieces of the puzzle. Without these data-driven components, innovation is subject to serendipity, and it’s more than likely that crowdsourcing will become far more overwhelming than it is valuable.

4. Track Your Innovation Pipeline

Speaking of data, it’s not worth a whole lot if you’re not monitoring and analyzing it. Accurately leveraging crowd science requires the creation of both automated and customized reports, as well as the use of predictive analytics. This helps businesses identify future trajectories based on historical patterns. It’s especially useful when trying to figure out things like why certain types of ideas become popular, where expertise exists among employees, how long it takes for a great idea to make it through all of the necessary channels, and most importantly, how to take all of that info and use it to develop a repeatable process for innovation.

Want to learn more? Check out Mindjet’s SpigitEngage to see how the right innovation management process can help your business rise above the noise.

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