This a duplicated article from the Harvard
Business Journal from April 2012. It is written by Alex “Sandy” Pentland and it
is still relevant today.
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
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
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.
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
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.
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
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.
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
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. 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.”
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.