People & Strategy Summer 2017 Vol. 40 Issue 3 - 63
Figure 4 illustrates leading practices for
designing a quantified workplace.
Embracing the Change
As we move toward the quantified
workplace, mistakes are inevitable: In
the world of data analytics, we often
must experiment to figure out the
true meaning of particular data. As
behavioral science research has shown,
instinct may lead to good decisions but
is prone to error. Work to understand
the needs of both your employees and
the business, and use those as variables
to create natural experiments. Pilot
programs, multiple tests, and replicated
groups around the organization are all
ways you can learn about the impact of
your data-without spending a year or
more on a massive, organization-wide
As data collection becomes more
pervasive in a connected world, so does
the potential for invasive overreach.
You should balance the analytical
insights possible from individuated
data with the need to protect privacy
by aggregating data. IoT technology
makes meaningful anonymization difficult. Designers should build in security
measures that put in place meaningful
technological and governance steps to
anonymize data shown to managers.
one minute per day, that translates
into a $14.5 million annual savings.11
If a company were to pass on even a
portion of such savings to employees
as a bonus or salary increase, it would
likely help align drivers' motivations
with organizational priorities, making
monitoring a little less ominous.
Value to the employee hardly needs
to be limited to financial rewards.
There is obvious value in making a job
easier, faster, or safer. Take firefighters,
for example. By instrumenting each
firefighter, the on-scene commanders
can more effectively deploy people at a
fire. An IoT-driven system can automatically and instantly alert individuals to
conditions they might not sense or to
dangers such as an impending building
focus, and selective behavior change.
Quality, not quantity."12
Remember also that accumulating
high-quality data often requires capturing information over an extended
period of time. During certain times of
the year, month, or season, employees'
responsibilities may radically shift-not
to mention their behavior near the end
of a stressful quarter.
Many HR organizations have not yet
developed policies and procedures for
data governance, which will likely be
increasingly important as the current
trickle of data becomes a flood. HR
leaders will want to develop a clear
process for securing data, managing
access, and holding people accountable for security and quality standards.
Designing a Quantified
FIGURE 4 DESIGNING A QUANTIFIED WORKPLACE
Before equipping an entire workforce
with smartwatches and connected ID
badges, you should carefully consider your goals: What problem are you
trying to solve? Begin with the business
problems you want to solve first, and
then decide what data you need.
Again, many employees are reticent-justifiably so-about becoming
quantified, giving their bosses unchecked IoT-aided access to their movements, meetings, and conversations.
Promises of confidentiality notwithstanding, once potentially compromising data is in the system an employer
may not be able to guarantee that the
information will never be invoked
during an annual review. U.C. Berkeley
management professor Morten Hansen
recommends that employers aim for
"less data, less but better feedback,
VOLUME 40 | ISSUE 3 | SUMMER 2017