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Sunday, April 1, 2012

New Science of Building Great Teams II

"> Energy levels within a team are not static. For instance, in my research group at MIT, we sometimes have meetings at which I update people on upcoming events, rule changes, and other administrative details. These meetings are invariably low energy. But when someone announces a new discovery in the same group, excitement and energy skyrocket as all the members start talking to one another at once.
The second important dimension of communication isengagement, which reflects the distribution of energy among team members. In a simple three-person team, engagement is a function of the average amount of energy between A and B, A and C, and B and C. If all members of a team have relatively equal and reasonably high energy with all other members, engagement is extremely strong. Teams that have clusters of members who engage in high-energy communication while other members do not participate don’t perform as well. When we observed teams making investment decisions, for instance, the partially engaged teams made worse (less profitable) decisions than the fully engaged teams. This effect was particularly common in far-flung teams that talked mostly by telephone.
The third critical dimension, exploration, involves communication that members engage in outside their team. Exploration essentially is the energy between a team and the other teams it interacts with. Higher-performing teams seek more outside connections, we’ve found. We’ve also seen that scoring well on exploration is most important for creative teams, such as those responsible for innovation, which need fresh perspectives.
To measure exploration, we have to deploy badges more widely in an organization. We’ve done so in many settings, including the MIT Media Lab and a multinational company’s marketing department, which comprised several teams dedicated to different functions.
Our data also show that exploration and engagement, while both good, don’t easily coexist, because they require that the energy of team members be put to two different uses. Energy is a finite resource. The more that people devote to their own team (engagement), the less they have to use outside their team (exploration), and vice versa.
But they must do both. Successful teams, especially successful creative teams, oscillate between exploration for discovery and engagement for integration of the ideas gathered from outside sources. At the MIT Media Lab, this pattern accounted for almost half of the differences in creative output of research groups. And in one industrial research lab we studied, it distinguished teams with high creativity from those with low creativity with almost 90% accuracy.
Beyond Conventional Wisdom
A skeptic would argue that the points about energy, engagement, and exploration are blindingly obvious. But the data from our research improve on conventional wisdom. They add an unprecedented level of precision to our observations, quantify the key dynamics, and make them measurable to an extraordinary degree.
For example, we now know that 35% of the variation in a team’s performance can be accounted for simply by the number of face-to-face exchanges among team members. We know as well that the “right” number of exchanges in a team is as many as dozens per working hour, but that going beyond that ideal number decreases performance. We can also state with certainty that in a typical high-performance team, members are listening or speaking to the whole group only about half the time, and when addressing the whole group, each team member speaks for only his or her fair share of time, using brief, to-the-point statements. The other half of the time members are engaging in one-on-one conversations, which are usually quite short. It may seem illogical that all those side exchanges contribute to better performance, rather than distract a team, but the data prove otherwise.
The data we’ve collected on the importance of socializing not only build on conventional wisdom but sometimes upend it. Social time turns out to be deeply critical to team performance, often accounting for more than 50% of positive changes in communication patterns, even in a setting as efficiency-focused as a call center.
Without the data there’s simply no way to understand which dynamics drive successful teams. The managers of one young software company, for instance, thought they could promote better communication among employees by hosting “beer meets” and other events. But the badge data showed that these events had little or no effect. In contrast, the data revealed that making the tables in the company’s lunchroom longer, so that strangers sat together, had a huge impact.
A similarly refined view of exploration has emerged in the data. Using fresh perspectives to improve performance is hardly a surprising idea; it’s practically management canon. But our research shows that most companies don’t do it the right way. Many organizations we’ve studied seek outside counsel repeatedly from the same sources and only at certain times (when building a business case, say, or doing a postmortem on a project). The best-performing and most creative teams in our study, however, sought fresh perspectives constantly, from all other groups in (and some outside) the organization.
How to Apply the Data
For management tasks that have long defied objective analysis, like team building, data can now provide a foundation on which to build better individual and team performance. This happens in three steps.
Step 1: Visualization. In raw form the data don’t mean much to the teams being measured. An energy score of 0.5 may be good for an individual, for example, but descriptions of team dynamics that rely on statistical output are not particularly user-friendly. However, using the formulas we developed to calculate energy, engagement, and exploration, we can create maps of how a team is doing on those dimensions, visualizations that clearly convey the data and are instantly accessible to anyone. The maps starkly highlight weaknesses that teams may not have recognized. They identify low-energy, unengaged team members who, even in the visualization, look as if they’re being ignored. (For examples, see the exhibit “Mapping Teamwork.”)
When we spot such people, we dig down into their individual badge data. Are they trying to contribute and being ignored or cut off? Do they cut others off and not listen, thereby discouraging colleagues from seeking their opinions? Do they communicate only with one other team member? Do they face other people in meetings or tend to hide from the group physically? Do they speak loudly enough? Perhaps the leader of a team is too dominant; it may be that she is doing most of the talking at meetings and needs to work on encouraging others to participate. Energy and engagement maps will make such problems clear. And once we know what they are, we can begin to fix them.
Exploration maps reveal patterns of communication across organizations. They can expose, for instance, whether a department’s management is failing to engage with all its teams. Time-lapse views of engagement and exploration will show whether teams are effectively oscillating between those two activities. It’s also possible to layer more detail into the visualizations. We can create maps that break out different types of communication among team members, to discover, for example, if teams are falling into counterproductive patterns such as shooting off e-mail when they need more face time. (For an example, see the exhibit “Mapping Communication over Time.”)
Step 2: Training. With maps of the data in hand, we can help teams improve performance through iterative visual feedback.
Work we did with a multicultural design team composed of both Japanese and American members offers a good example. (Visual data are especially effective at helping far-flung and multilingual groups, which face special communication challenges.) The team’s maps (see the exhibit “Mapping Communication Improvement”) showed that its communication was far too uneven. They highlighted that the Japanese members were initially reluctant to speak up, leaving the team both low energy and unengaged.
Every day for a week, we provided team members a visualization of that day’s work, with some light interpretation of what we saw. (Keep in mind that we didn’t know the substance of their work, just how they were interacting.) We also told them that the ideal visualization would show members contributing equally and more overall contributions. By day seven, the maps showed, the team’s energy and engagement had improved vastly, especially for the two Japanese members, one of whom had become a driving force.
The notion that visual feedback helps people improve quickly shouldn’t be surprising to anyone who has ever had a golf swing analyzed on video or watched himself deliver a speech. Now we have the visual tools to likewise improve teamwork through objective analysis.
Step 3: Fine-tuning performance. We have seen that by using visualizations as a training tool, teams can quickly improve their patterns of communication. But does that translate to improved performance? Yes. The third and final step in using the badge data is to map energy and engagement against performance metrics. In the case of the Japanese-American team, for example, we mapped the improved communication patterns against the team’s self-reported daily productivity. The closer the patterns came to those of our high-performance ideal, the higher productivity rose.
We’ve duplicated this result several times over, running similar feedback loops with teams aiming to be more creative and with executive teams looking for more cohesiveness. In every case the self-reporting on effectiveness mapped to the improved patterns of communication.
Through such maps, we often make important discoveries. One of the best examples comes from the bank’s call center. For each team there, we mapped energy and engagement against average handling time (AHT), which we indicated with color. (See the exhibit “Mapping Communication Against Performance.”) That map clearly showed that the most efficient work was done by high-energy, high-engagement teams. But surprisingly, it also showed that low-energy, low-engagement teams could outperform teams that were unbalanced—teams that had high energy and low engagement, or low energy and high engagement. The maps revealed that the manager needed to keep energy and engagement in balance as he worked to strengthen them.
If a hard metric like AHT isn’t available, we can map patterns against subjective measures. We have asked teams to rate their days on a scale of “creativity” or “frustration,” for example, and then seen which patterns are associated with highly creative or frustrating days. Teams often describe this feedback as “a revelation.”
Successful tactics. The obvious question at this point is, Once I recognize I need to improve energy and engagement, how do I go about doing it? What are the best techniques for moving those measurements?
Simple approaches such as reorganizing office space and seating are effective. So is setting a personal example—when a manager himself actively encourages even participation and conducts more face-to-face communication. Policy changes can improve teams, too. Eschewing Robert’s Rules of Order, for example, is a great way to promote change. In some cases, switching out team members and bringing in new blood may be the best way to improve the energy and engagement of the team, though we’ve found that this is often unnecessary. Most people, given feedback, can learn to interrupt less, say, or to face other people, or to listen more actively. Leaders should use the data to force change within their teams.
The ideal team player. We can also measure individuals against an ideal. In both productivity-focused and creativity-focused teams, we have discovered the data signature of what we consider the best type of team member. Some might call these individuals “natural leaders.” We call them “charismatic connectors.” Badge data show that these people circulate actively, engaging people in short, high-energy conversations. They are democratic with their time—communicating with everyone equally and making sure all team members get a chance to contribute. They’re not necessarily extroverts, although they feel comfortable approaching other people. They listen as much as or more than they talk and are usually very engaged with whomever they’re listening to. We call it “energized but focused listening.”
The best team players also connect their teammates with one another and spread ideas around. And they are appropriately exploratory, seeking ideas from outside the group but not at the expense of group engagement. In a study of executives attending an intensive one-week executive education class at MIT, we found that the more of these charismatic connectors a team had, the more successful it was.
Team building is indeed a science, but it’s young and evolving. Now that we’ve established patterns of communication as the single most important thing to measure when gauging the effectiveness of a group, we can begin to refine the data and processes to create more-sophisticated measurements, dig deeper into the analysis, and develop new tools that sharpen our view of team member types and team types.
The sensors that enable this science are evolving as well. As they enter their seventh generation, they’re becoming as small and unobtrusive as traditional ID badges, while the amount and types of data they can collect are increasing. We’ve begun to experiment with apps that present teams and their leaders with real-time feedback on group communications. And the applications for the sensors are expanding beyond the team to include an ever-broader set of situations.
We imagine a company’s entire staff wearing badges over an extended period of time, creating “big data” in which we’d find the patterns for everything from team building to leadership to negotiations to performance reviews. We imagine changing the nature of the space we work in, and maybe even the tools we use to communicate, on the basis of the data. We believe we can vastly improve long-distance work and cross-cultural teams, which are so crucial in a global economy, by learning their patterns and adjusting them. We are beginning to create what I call the “God’s-eye view” of the organization. But spiritual as that may sound, this view is rooted in evidence and data. It is an amazing view, and it will change how organizations work.
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