Innovation is the epitome of an umbrella term. It’s associated with everything from new ideas to revised processes and modifications, and takes on countless shapes and meanings depending on different industries, organizations, and individuals.
This indefinite concept of innovation isn’t, by itself, a structure or process. When done correctly, it’s not a boilerplate, one-size-fits-all solution, nor is it a singular product or service. Instead, it’s the culmination of collaborative ideation, transparent communication, behavioral data, and crowd analytics, interconnected in such a way that disruptive and incremental innovations become possible.
The Art and Science of Crowd Analytics
Regardless of its innate ambiguity and malleability, innovation is a crucial component of change, of progress, and of overall success. Equally critical are the parts under the hood — specifically, the aforementioned data gleaned using crowd analytics, or crowd science.
Mindjet’s lead Data Scientist, Anna Gordon, writes: “Using data science techniques — such as data mining, algorithm development, and statistical modeling — on social network and crowdsourced data, crowd science invites psychological and behavioral elements that are not necessarily present in traditional data science. Basically, what we’re seeing is that new elements of behavior are affecting data, such as politics, opinions, and agents interacting with and influencing each other. So, in addition to looking at data in the traditional way, we must now consider political and social structures, and how people learn from and influence each other; we must consider how ideas flow through social networks, what motivates people to contribute to discussions, and the consequences of engagement.”
What makes crowd analytics so important? In a nutshell, it’s the piece of the innovation process that makes it possible to surface the most viable ideas, predict how much those ideas are worth, and let you do so over and over. “This much larger store of data on social structures and phenomena gives us the ability to study and understand these networks and how they evolve,” says Gordon. “Whereas traditionally, scientific data analysis involved careful measurement and often cumbersome collection of data, we now have a vast and expanding resource.”
Better Data for Better Decisions
Because decision-making in business can draw the fine line between lasting success and crumbling failure, coming to conclusions isn’t something most company leaders like to gamble with. They need the ability to quantify potential returns, determine how long projects will take and the resources they’ll exhaust, and the various costs of bringing different ideas to fruition. And to get there, leaders need accurate and in-depth understanding of both the operational and creative sides of the business.
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