Crowd Science and Innovation Roll-Up: Driving Outcomes, Why it Matters, and Key Factors to Crowd Science Done Right

Filed Under Innovation

Lights of Numbers
Arwen Heredia

February 28, 2014

Over the course of this last month, we delved into the logistics of utilizing crowd science to drive innovation outcomes. From identifying why crowd science is so crucial for a successful innovation program, to offering up tips on how to implement it, we made the case for why algorithm-based platforms and repeatable processes are the way to go in today’s ever-evolving marketplace. Read on to catch up on February’s themed posts.

Crowd Science, Innovation, and Driving Outcomes

“Crowdsourcing can often seem like a shot in the dark; or more accurately, trying to find a really amazing needle in a very loud, boisterous haystack. But when we look at the exceptional value that crowdsourcing offers in ideation and shaping concepts, it makes sense that it would be even more beneficial if it were mechanized and scalable.

And so it is. Innovation management systems — like our own SpigitEngage — provide crowd-driven decision making backed by algorithms, allowing the best ideas to get surfaced every time. Guesswork is replaced by systematic idea graduation, workflows become more efficient, and even very large organizations are able to harness the power of the crowd and leverage it for executable innovation. Recently, Accenture released the results of an annual study they conduct on emerging IT trends, and found an uptick in engagement as well as ideas that actually made their way to the executive agenda — crowd science is what makes this possible.”

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Why Crowd Science Makes Sense: Part I

“It’s all well and good to encourage brainstorming through positivity — after all, one of the biggest reasons employees never voice their ideas is a fear of rejection or failure, and telling people they’ve come up with one or more bad suggestions is a surefire way to silence them in the future. But having the ability to mathematically discern between a ‘good’ idea and a ‘bad’ idea is critical to keeping the innovation pipeline free of bottlenecks and bias. Additionally, using desired outcomes to develop measurements, benchmarks, and qualities of innovation possibilities allows you to shape potential projects before they begin.”

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Why Crowd Science Makes Sense: Part II

“Experiments done by Mindjet’s data scientists show a direct correlation between the behavior of the crowd, the predictability of successful innovations, and defining what it means for an idea to be “good.” When using an innovation management system, organizations rely on graduated ideation, which is driven by things like votes and interactions. In order for ideas to rise to the top, they have to pass what is, in essence, a series of tests that prove the sustainability and resiliency of the idea. In a nutshell, as the criteria for what makes good ideas good becomes exponentially stricter, the overall number of good ideas drops, as does the number of innovators who come up with those good ideas.”

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4 Key Factors to Crowd Science Done Right

“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.”

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Dig this topic? Check out The Science of Innovation: 4 Ways Big Data Informs Innovative Processes.