Through analytics, companies can reduce overload, attrition, and
other costs of collaboration — and increase its rewards.
Image
courtesy of Jing Jing Tsong/theispot.com
No question, in a competitive global landscape, collaboration
allows companies to serve exacting clients more seamlessly, respond more
quickly to changing environments, and innovate more rapidly. But when an
organization tries to boost collaboration by adopting a new formal structure,
technology, or way of working, it often adds a steady stream of time- and
energy-consuming interactions to an already relentless workload, diminishing
instead of improving performance.
Think about the consequences at an individual level: It’s not
unusual to feel as if we are just starting our work at 5 p.m.,
after the daily battery of demands has finally quieted down. Thanks to the
plethora of technologies that keep us connected, increasingly integrated global
operations, and the need for a multidisciplinary approach to deploying complex
products and services, the problem has snowballed over the past decade, with
collaborative time demands rising more than 50%. Most knowledge workers and
leaders spend 85% or more of their time on email, in meetings, and on the
phone.1 Employees struggle with
increases in email volume, the proliferation of new collaborative tools, and
expectations of fast replies to messages — with deleterious effects on their
quality of work and efficiency. Research tells us that simple distractions like
checking a text message fragments our attention more than we realize, and more
consuming distractions — such as answering an email — can cost us more than 20
minutes to fully regain our focus.2
Even though employees are acutely aware that they’re suffering,
most organizations don’t recognize what’s happening in the aggregate. “We can
track an airline receipt down to two decimal places and create a whole infrastructure
around compliance, but we have no idea how effective networks are or where
collaborative time is being spent,” lamented the CIO of one company we studied.
With increasing pressure on organizations to become more agile, there is also a
greater tendency to swamp employees with collaboration demands in pursuit of
a networked organization. We have found that people have, on
average, at least nine different technologies to manage their interactions with
work groups. The result can be overwhelmed and unproductive employees, sapped
creativity, and employee attrition.
Fortunately, it is possible to improve collaboration efforts
with the help of analytics. Perhaps the first industry to do so was
professional basketball, where quantitative analysts realized that some players
scored relatively little but somehow made their teammates more successful.3 Similar analysis has been
deployed by professional soccer teams to identify what patterns of passing were
most effective for scoring goals under particular circumstances.4 But the benefits of
understanding patterns of collaboration can be reaped in all kinds of
organizations. Using analytics to make collaborative activities more
transparent helps companies identify and exploit previously invisible drivers
of revenue production, innovation, and employee effectiveness. Analytics enables
better management of what has become an enormous yet hidden cost for
organizations, one that employees aren’t equipped to manage on their own.
Five Ways Businesses Can Benefit
In our research on collaboration over the past decade, we have
seen some effective uses of analytics emerge in two industry consortia, where
we’ve identified whether collaborations are driving value or unintentionally
consuming resources.5 These organizations have
gone beyond documenting simple collaborative activities — who talks to whom at
what frequency — to systematically relating collaborative activities to key
outcomes.
In particular, we found five main ways in which companies derive
value from collaboration analytics. First, they scale collaboration effectively
by deploying targeted analytics to connect critical roles (for instance,
project leads and first-line leaders) and to link employees engaged in similar
work who are distributed across functions, units, or geographies. Second,
organizations improve collaborative design and execution by understanding how
networks cross hierarchies and team structures, and by replicating drivers of
success. Third, they use collaborative analytics to drive planned and emergent
innovation through networks that cross capabilities, markets, or functions.
Fourth, the insights they glean from analytics allow them to streamline collaborative
work by diagnosing and reducing collaborative overload and removing unnecessary
routine decision-making interactions. Fifth, companies engage talent by using
collaboration analytics to identify social capital enablers of performance,
engagement, and retention.
We’ll explore each source of value in turn.
1. Scaling collaboration effectively. Most
organizations have developed deep talent in knowledge-intensive core
capabilities, but it’s much less common that those individuals with expertise
are systematically connected to one another. They can be far-flung throughout
the organization, often distributed across functions, geographies, and
P&Ls, which means that no single leader or unit is responsible for deriving
benefits from their collaborations. As a result, scale benefits are often very
limited.
Collaboration analytics, however, can maximize the benefits of
scale in three key areas:
·
Around specific leadership roles —
typically first level and manager of manager — for which failure rates have a
significant impact on the organization.
·
Across strategically important functional
roles — or pivot roles6 — that have a
disproportionate impact on execution or innovation processes.
·
Within communities of core technical experts —
whether scientific-, engineering-, or software-related — that a company relies
on for strategic capability.
Take, for example, General Electric, which has an enormous
knowledge base in its more than 300,000 employees around the globe, across nine
businesses. Prior to 2015, GE’s efforts to link distributed pockets of
expertise were uneven. “We had bright spots where cross-business expertise
sharing was working, but we were not consistent within and across segments. It
was limiting our scale opportunities,” noted knowledge-sharing leader Dan
Ranta. Leaders saw the opportunity to improve collaboration across the company
through new analytics-powered expertise communities. The goal was to enable expertise
integration in a natural way that would require little effort from the experts
involved.
Ranta and his team first developed a quantitative model to
predict whether a given community was ready to share its expertise globally, on
the basis of data collected about successful knowledge-sharing communities
elsewhere in the company. They calculated scores that reflected the maturity of
collaboration among community members, their degree of mutual commitment to
success, the extent to which their local technological environment would
support a global community, and the level of support for a global community
within their organization. When the model predicted that a community was ready,
Ranta’s team included that community in a new knowledge-sharing architecture
featuring discussion spaces where experts could interact globally. Those that
were not ready were steered instead toward smaller and more focused structures,
such as mission-based teams.
GE used analytics to predict which community member would have the
right expertise to answer each kind of question and, through industrial-scale
software, to automatically distribute questions to the appropriate community
experts. For community management purposes, GE generated real-time analytics of
collaboration patterns to identify the employees who were most engaged and
making a difference across locations.
As a result of this work, GE’s expertise is becoming easier to
tap, wherever it resides. For example, GE’s Renewable Energy business, with
approximately 43,000 employees, has deployed 27 communities to connect
individuals across hundreds of technical discussions that span geographical and
business boundaries, collectively producing a vast array of solutions and
learnings. In one year, 1,172 internal collaborators collectively solved a
total of 513 customer problems, resulting in more than $1.1 million of cost
avoidance in productivity. “Analytics powers our processes, minimizes the human
cost of helping each other out, and lets us tap into the thickest vein in the ‘gold
mine of sharing,’ which is human generosity and professional pride,” Ranta
noted.
2. Improving collaborative design and execution. Team-based
structures are common in organizations, but employees assigned to too many
teams end up slowing efforts and creating significant disruption if they burn
out and leave.7 Collaboration analytics can
help leaders determine where team structures are most effective, informing
in-house training and generating best practices that help replicate those
networks and tune teams for agility and speed.
Lateral collaboration is particularly challenging in investment
banking firms. Despite often advocating a “one firm” culture, the hierarchies
that grow under a partner often lead employees to concentrate all of their
efforts within their teams, while time constraints further limit their ability
to learn about solutions available from other partner silos. This can lead
client-facing teams to focus on selling their own solutions rather than
integrated, holistic solutions that command higher margins and improve client
retention.
Executives at one global investment bank realized that this
partner-silo structural dilemma was preventing their firm from catching up to
industry leaders. Through a network analysis, an analytics team quantified the
number of revenue-producing ties among midtier team leaders to understand where
integrated offerings based on bundles of skills were — and were not — happening. The
team discovered an asset that had been overlooked: mid-tier employees who
enabled others to cross-sell services. Compared with other employees, these
“hidden integrators” had three times as many ties across partner groups, and
their connections were almost five times more likely to link poorly connected
teams. Financially, these hidden integrators accounted for more than six times
the average cross-selling revenue.
But it turned out that in spite of their tremendous value to the
firm, these hidden integrators were actually at risk. Several had recently
departed the firm. Analytics revealed that they were underappreciated: Their
impact on cross-selling was largely invisible to the company and not counted
toward revenue generation. Leaders quickly adjusted the compensation system to
acknowledge their critical contributions.
The Research
·
The authors spoke with more than 100 managers and executives
actively engaged in collaboration analytics projects.
·
Their sample was drawn from two industry-based consortia.
·
They focused on identifying where collaboration analytics had
been used to make evidence-based decisions that affected business performance.
Perhaps most important, analytics revealed that these valuable
integrators were successful in different ways. Some integrators specialized in
enabling many smaller transactions, so the firm freed up their time for this.
Other integrators excelled at enabling much larger transactions (more than $15
million), but because these occurred much less frequently, these employees had
to be managed and rewarded differently for their longer-term efforts.
3. Driving planned and emergent innovation. Innovation
is inherently a social process, grounded in the creative friction that comes
when people with different types of expertise and experiences pull one another
in unexpected directions and arrive at something entirely new. Understanding
where an organization should stimulate innovation by building networks that
bring together people with different kinds of expertise is not something best
left to chance. Collaboration analytics can uncover silos across capabilities
that — if better integrated — could spur innovation and translate creative
ideas into production-ready offerings.
General Motors used collaboration analytics to do just that.
Radically new business models are emerging in the automobile industry for
shared mobility, autonomous driving, electrification, and connectivity. In the
face of such opportunities and an unprecedented set of nontraditional
competitors, GM recognized that it had to take bold actions to adapt to this
new world.
GM rapidly acquired startups and hired new talent to boost its
technological capabilities in core strategic areas. But despite these investments
in GM’s human capital, executives also recognized the
importance of social capital, or the networks of ties that
connect employees and amplify their individual capabilities. To produce a
dramatic increase in the company’s agility and innovativeness, GM focused on
creating what then-chief talent officer Michael Arena termed adaptive
space — a network of connections that link the entrepreneurial pockets
of innovation within the company to its traditional execution-focused
operational elements.8 This began to chip away at
historic silos. Creating adaptive space required interventions around four
different kinds of networks: idea discovery, concept development, innovation
diffusion, and organizational disruption. Although all were important, let’s
focus on the second stage — concept development — in which promising ideas were
rapidly developed into emergent innovations.
Arena asked the internal analytics team to study the networks of
two development groups that transformed ideas into novel prototypes. One was
better at this than the other. Collaboration analytics derived from network
data revealed that the more successful group had a clustering coefficient (the
degree to which a group consists of small, tightly knit subgroups) that was
more than two times higher than that of its less successful counterpart. The
more successful group was better at forming small subgroups that collaborated
on a single task or function of the overall development challenge. That way,
they were able to concentrate on perfecting one thing at a time and make rapid,
focused progress.
As you might expect, the successful group also had a density
metric (a measure based on how many ties link a group together) almost double
that of the less successful group. Through these ties, team members tasked with
one aspect of development shared their advancements with members from other
clusters in ways that helped combine local innovations into a functioning,
broader automotive concept. Interestingly, while the successful network had
more internal ties, its members had fewer external ties to
potential idea sources in industry or academia, so they were free from outside
distractions that could hinder their focus on the task at hand throughout
development. The less successful development group had more external
connections, which were valuable in enabling discovery of new insights but
often led the team to hedge their development bets by simultaneously pursuing
multiple different possibilities. Ironically, this had a negative impact on the
speed of concept development and prolonged the decision to shut down less
successful prototypes.
The combination of acquiring skilled employees and ensuring that
these individuals are properly positioned in the network has enabled GM to
adapt faster to the disruptive forces that surround it.
4. Streamlining collaborative work. As
employees spend more of their time in meetings, on phone calls, and on email,
collaboration analytics can play a powerful role in identifying where excessive
connectivity is draining time, slowing speed to market, or hurting employee
morale. Collaboration overload can beset specific individuals or roles, and
collaboration analytics can identify the situations where some people are
collaboratively far less efficient than others in the organization.9 Sometimes overload is
created through excessively inclusive decision processes. In general, overload
occurs when more than a quarter of the people who interact with any individual
employee report (through an internal survey) that they cannot improve their own
performance without more access to that individual.
Perhaps nowhere is streamlining collaborative work more
important than in the commercialization of new pharmaceuticals.
Commercialization occurs after most of the enormous investments required to
develop a new drug have been made but before the product hits the shelves. It
is extraordinarily time-sensitive, with a single day’s delay costing the
company millions in lost profits. But drug commercialization is also incredibly
collaboration-intensive, requiring orchestration among regulatory affairs, medical
affairs, R&D, sales, marketing, legal, advocacy, manufacturing, and many
other functions.
Streamlining collaboration can have a direct and immediate
effect on the bottom line. The leader of a drug commercialization unit in one
pharmaceutical company we studied discovered that truth after using
collaboration analytics to identify opportunities to increase efficiency of
routine decision-making, which often seemed to be taking too long. The
analytics team asked each member of the commercialization group to answer a
series of questions about his or her network of collaborators, including how
much time each spent in routine versus nonroutine decisions. Armed with data
about the estimated delay these types of decisions caused, the team used text
analytics to calculate which categories of decisions delayed the process the
longest.
Focusing on each area of opportunity for improvement, leaders
and their staffs drafted guidelines for optimal decision-making, in some cases
developing decision-flow schematics to ensure that all parties involved knew
the best sequence and timelines. They revised governance principles and trained
employees to push responsibility and accountability down in the organization.
The analytics team also discovered significant variation in how
much time individuals spent collaborating with certain roles within the unit
and preparing for those interactions (what we term collaborative
efficiency). Statistical analyses identified four specific roles in which
individuals were acting in ways that they may have believed to be efficient but
that did not adhere to any standardized best practice. Those who were most
efficient in those roles consumed only a small amount of time from each person
in his or her network, while those who were least efficient consumed many times
as much. Subsequent calculations revealed that improving the latter group’s
efficiency could have a catalytic effect on the entire organization. Simply
bringing it up to average could free up more than 17,000 hours of collaboration
time annually in the rest of the organization — the equivalent of
almost nine full-time employees.
With these insights, the unit was able to recoup thousands of
hours and shave time off the overall commercialization process. Analyzing
collaboration in this way showed that changes were possible and desirable, and
provided the diagnostic insights to help other groups in the company discover
new and better ways of doing their jobs.
Using survey-based data about collaboration is not the only way
to glean useful insights about a company’s collaboration inefficiencies. It’s
also possible to extract collaboration data from existing digital sources, such
as meeting and email data, as a by-product of other behaviors. A leader in the
secondary mortgage market, employed a “passive data” collaboration analytics
engine that enabled its analytics team to easily identify opportunities for
streamlining. One unit seemed particularly effective, and analysis of passive
collaboration data revealed how those employees’ behaviors were different from
others in the company. This group had created a culture of empowerment and
strong working relationships among employees. For instance, they spent 56% less
time in approval-related meetings and 29% more time on approval-related emails.
They also worked with greater autonomy, spending 20% less time in meetings
where their supervisor was present. And they were more focused when in
face-to-face collaborations, having 40% fewer meeting conflicts and sending 18%
fewer emails while in meetings.
5. Engaging talent. A rapidly
developing set of collaboration analytics applications has emerged as a natural
extension of the people analytics functions in organizations. Organizations are
making quick progress on a variety of thorny talent-related issues — and
generating impact in areas where progress has often traditionally been limited
— by incorporating social capital drivers of success alongside traditional
human capital drivers. For instance, companies are doing the following:
·
Reducing attrition through analytics models that identify the
collaboration patterns that predict retention.10
·
Promoting individual performance and transition success by
studying networks of high performers and helping others to replicate those
networks.11
·
Refining performance management processes to locate and retain
top collaborators whom traditional systems often miss.
·
Using evidence-based approaches to generate more impact from
diversity and inclusion programs.
Booz Allen Hamilton provides a rare example of the use of predictive
collaboration analytics to not just anticipate but also improve employee
retention. The company had already developed a predictive attrition model based
on data such as demographic attributes, work characteristics, level in the
organization, length of service, and compensation and benefits. The model
pinpointed key attrition drivers and identified employees at greatest risk of
leaving the company who might benefit from targeted interventions. However,
after the model was developed, additional social factors that might affect
attrition came to light.
Data suggested that the risk for turnover was highest following
an employee’s transition to a new role. Further analysis revealed that how an
employee managed networks shaped the odds of leaving after a transition.
Mapping data about the size, reach, and quality of each employee’s
collaboration network against attrition data uncovered different insights at
specific tenure bands. The analysis contradicted much of the traditional advice
about networks (for instance, that a big network is always a good network).
Five categories of network-based factors distinguished employees
who departed within two years of joining the company from those who stayed.
(See “Network Drivers of Retention at Booz Allen Hamilton.”) The people who
stayed were those who created more energy in their interactions with others,
helped others find a sense that their work had purpose and mattered, generated “pull”
(or demand) for their talents, created diversity of thought through broader
networks, and connected with a strong peer cohort. On the basis of these
findings, Booz Allen implemented a new onboarding program that focused on the
specific network dimensions that were most likely to increase retention.
Follow-up analyses confirmed a significant improvement in retention as a result
of the new collaboration training.
Network Drivers of
Retention at Booz Allen Hamilton
Collaboration data analysis shows that new hires who stay with
the company are those who engage in these behaviors.
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