INFOGRAPHIC: How Crowd Science Drives Innovation
As savvy business leaders continue to strive for the best ways to catalyze innovation and stay ahead of the competition, identifying the best ideas from a diverse and expansive organization is an absolute must. And, with such a robust selection of approaches to take and methodologies to use, this can be relatively difficult, costly, and time-consuming.
However, it’s clear that there’s a significant need for companies to implement innovation strategies that are based on data, algorithms, and behavioral patterns. The solution is to leverage the collective thinking of your company’s greatest asset — your people. Harvesting ideas and sparks of genius can and will lead to new solutions to core business problems and challenges, as well as provide market insight. This not only allows for the mechanization of the innovation process, but helps build a foundation for predictive analysis and learning. This is why so many top-tier companies are turning to crowdsourcing, collective ideation, and more importantly, crowd science.
Why Crowd Science is the Answer
Today’s business successes are dependent on that one unique differentiator: a great idea. And while infrastructure, technology, and capital are widely available, creating a corporate culture where all levels and backgrounds can collaborate on business challenges equally is what really leads to better outcomes and greater employee engagement — a proven way to increase a company’s overall value. At Mindjet, our efforts are centered around helping enterprise companies leverage the full power of their workforce in order to find answers to everyday questions and challenges. Our SpigitEngage platform allows our clients to create engagement at all levels of their organization and surface new ideas through collaborative brainstorming.
Recently, Data Scientist Anna Gordon wrote a piece for VentureBeat that details the nuances of crowd science and its importance in innovation initiatives. “You might be wondering how crowd science is any different from traditional data science,” she said. “Data science also deals with finding signals and patterns in large amounts of potentially noisy data, but crowd science explores data that has a subjective element to it: the psychology, variable behaviors, and opinions of the crowd. This begets a different kind of noise from what the typical data scientist must filter out. Members of a crowd can have vastly differing opinions about a topic, might accidentally or intentionally enter incorrect data, or might try to outsmart the system. As a result, crowd scientists must eliminate outlying data points and introduce techniques that ensure honesty. Wikipedia even has a checks-and-balances system (or algorithm); if someone updates a post with faulty information, the post can be flagged and revised by other members of the collective crowd.”
To us, focusing on the confluence of math, science, and ideation is absolutely paramount to achieving sustained competitive differentiation in an impacted marketplace. So, to help you better understand the value of using a crowd science and innovation platform backed by data, we’ve developed the below infographic, which highlights important statistics and provides an overview of how crowd science is the most effective way to surface ideas and drive innovation in your company.