Building Team Effectiveness—Big Data Show it is the Small Stuff that Matters
In a meeting this week several physician leaders were bemoaning the resistance of some of their colleagues to change in the face of what they felt were good data. I pointed out that part of the problem is the previous behavior had been successful, making it difficult to give it up. The response was we did not have the data. Of course, we have never had, and in my view never will, have all the data to make the practice of medicine purely rational and “data-driven.”
Today the New York Times magazine published an in-depth report on Google’s efforts to define what makes an effective team. As you would expect from a data driven organization, they started by looking at a lot of data regarding individual team members without being able to discern any pattern. Since their data analytics are likely the best available, it is reasonable to conclude there aren’t any patterns when you look at individual characteristics.
The author then reports on results from Google’s “Project Aristotle,” an effort to analyze how to build “perfect” teams by looking at the characteristics of teams that were successful compared to those that were not.
“We looked at 180 teams from all over the company. We had lots of data, but there was nothing showing that a mix of specific personality types or skills or backgrounds made any difference. The ‘who’ part of the equation didn’t seem to matter.”
The researchers then looked at the literature suggesting group norms determined how the group behaved, but decided they could not discern which norms mattered if the goal was to build successful teams. Previous studies had suggested two factors were associated with success.
“First, on the good teams, members spoke in roughly the same proportion, a phenomenon the researchers referred to as ‘equality in distribution of conversational turn-taking.’ On some teams, everyone spoke during each task; on others, leadership shifted among teammates from assignment to assignment. But in each case, by the end of the day, everyone had spoken roughly the same amount. ‘As long as everyone got a chance to talk, the team did well. But if only one person or a small group spoke all the time, the collective intelligence declined…Second, the good teams all had high ‘average social sensitivity’—a fancy way of saying they were skilled at intuiting how others felt based on their tone of voice, their expressions and other nonverbal cues…
Within psychology, researchers sometimes colloquially refer to traits like
‘conversational turn-taking’ and ‘average social sensitivity’ as aspects of what’s
known as psychological safety—a group culture that the Harvard Business School
professor Amy Edmondson defines as a ‘shared belief held by members of a team
that the team is safe for interpersonal risk-taking.’ Psychological safety is ‘a sense of
confidence that the team will not embarrass, reject or punish someone for speaking
up,’ Edmondson wrote in a study published in 1999. ‘It describes a team climate
characterized by interpersonal trust and mutual respect in which people are
comfortable being themselves.’”
The leaders of the Aristotle project, then, decided that psychological safety was the key objective to meet for building successful teams. Since they were dealing with engineers, who by nature dislike the “touchy-feely” aspects of this subject, they decided they needed to build an algorithm that could be easily implemented and scaled across the organization. They ended up simply sharing their data and developed self-assessment tools that would give team leaders insights into what was actually happening within their teams, which could then be addressed. The bottom line:
“The technology industry is not just one of the fastest growing parts of our economy; it is also increasingly the world’s dominant commercial culture. And at the core of Silicon Valley are certain self-mythologies and dictums: Everything is different now, data reigns supreme, today’s winners deserve to triumph because they are clear-eyed enough to discard yesterday’s conventional wisdoms and search out the disruptive and the new.
The paradox, of course, is that Google’s intense data collection and number
crunching have led it to the same conclusions that good managers have always known. In the best teams, members listen to one another and show sensitivity to
feelings and needs.”
I wonder if the modern “healthcare-system” has fallen victim to the data myth? And I wonder how much of our collective dysfunction results from our inability to develop psychological safety without our organizations. Big data, from a master of same, has shown we all view ourselves as important and want our efforts to matter. My daughter has a friend who says “I may not be much, but I am all I have.” We should recognize and incorporate that truth into our way of doing business.
28 February 2016
 Duhigg, Charles. What Google Learned From Its Quest to Build the Perfect Team. Online at http://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html.
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