To crack the code on real potential, data scientists from Google's world-famous People Analytics team have launched an initiative with the not-so-secret codename Project Aristotle. Here is their initial mission:to build the perfect team. At first glance, the task may seem simple. If you were to create a dream team, you'd just fill it with top performers, right?
So what specific qualities are you looking for? High IQ? Proficiency in multiple languages? Project Aristotle used the best algorithm technology to find out. By analyzing incredible amounts of data, including tens of thousands of responses across 180 teams, they sought to create the profile that would make the perfect performer in the workplace. The conclusion was astonishing and challenged everything you might think you knew about the potential.
They discovered there was no such thing as a perfect performer. When it comes to potential, individual traits and abilities are poor predictors of success on a team.
It's not about how smart you are, how many degrees you have, what grades you have received, how creative you are or what your personality looks like. Google has confirmed using the best available technology that these are the wrong variables to measure when trying to calculate success and potential.
Why? Because these are individual attributes. If these individual attributes don't predict success, what does? The answer is clear:it all depends on the ecosystem around you. The Aristotle Project found that if team members had 1) high "social awareness," meaning a strong awareness of the importance of social connections, and 2) if the team had cultivated an environment where everyone spoke as equals and everyone felt safe sharing their ideas, the team achieved their highest level of performance again and again.
In other words, success within a team is not a matter of survival of the fittest, but of survival of the best fit.
For decades, we have measured intelligence at the individual level, just as we have measured creativity, commitment and courage. But it turns out we weren't able to measure something with much greater impact.
