HRO Today April 2019 - 42

HR Technology
Powerful HR applications abound. Email and calendar
metadata enables HR to go beyond organizational charts
by piecing together network maps to better understand
fl ows of information and collaboration. Wearables and
sensors are capable of measuring how well people sleep,
whether they are getting enough exercise, and their levels
of stress or engagement throughout the day. Social media
can be scraped for clues about employee engagement.
Continuous pulse survey and collective intelligence tools
enable HR professionals to crowdsource ideas to better
understand and improve employee engagement. Also,
algorithms can be built to predict high-performing
recruits, match people to jobs, estimate time away for
injured workers, and model attrition. These applications
only scratch the surface of what's possible.
The above possibilities refl ect conventional discussions of
data science in HR. They focus on polling the workforce
and leveraging data sources, analytical tools, and laundry
lists of point solutions. This is a good start but it is
important not to equate people analytic sophistication
with specifi c types of quantitative technical sophistication,
such as information processing or machine learning.
To illustrate, consider the challenge of using data to make
better hiring decisions. It is increasingly practical to train
complex machine learning algorithms on rich datasets
to identify high-performing recruits. While valuable if
done well, this alone does not constitute sophisticated
people analytics-it merely begins the sensing process.
This starts with recognizing the pitfalls of relying on
unaided judgment about information derived from
unstructured job interviews. Humans have a hard time
weighing together fi ve variables, much less 50 or 500; they
are liable to make different decisions before lunch than
after lunch; and they fall prey to " thinking fast " decision
traps, such as halo effects, over-generalizing from personal
experience, and unconscious bias. Unbiased algorithms can
help overcome the limits of human judgment, as long as
the data sets used to train them aren't inherently biased
themselves. Training machine learning algorithms on
biased datasets and deploying them can therefore amplify,
rather than mitigate, the cognitive biases.
An alternate way is to use data analytics to select job
interview questions and to apply behavioral design
principles to de-bias hiring environments. Such data-driven
and psychologically informed interventions help take
some of the human biases out of hiring. Tapping into the
wisdom of crowds by having multiple interviewers follow
the same structured procedure eliminates some of the
[42]
HRO TODAY MAGAZINE
|
APRIL 2019
Organizations that use
people insights well
are 10 times more likely
to see a signifi cant
positive outcome from
that data on hiring.
Source: Bersin, Deloitte Consulting LLP
noise from the process. For example, the noise created
by one interviewer's bad mood will tend to get averaged
away by the uncorrelated judgments of the other
interviewers. Not surprisingly, Bersin's High Impact People
Analytics study reports that organizations that use people
insights well are 10 times more likely to see a signifi cant
positive outcome from that data on hiring.
This specifi c use case of data-driven hiring illustrates the
more general point that people analytics goes beyond
the routine application of machine learning techniques. It
should be conceived broadly to incorporate methods from
data science, the psychological and behavioral sciences,
and the use of experiments to fi gure out what works.
People analytics is ultimately about adopting a culture of
evidence-based decision making and using the scientifi c

HRO Today April 2019

Table of Contents for the Digital Edition of HRO Today April 2019

HRO Today April 2019 - 1
HRO Today April 2019 - 2
HRO Today April 2019 - 3
HRO Today April 2019 - 4
HRO Today April 2019 - 5
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