HOW BEHAVIOURAL ANALYTICS WILL REDEFINE HIRING
Looking at the advancement in AI and ML, the time is not
far when the moment a person walks in for an interview, complete profiling of
the personality will be done
Artificial intelligence, machine learning, internet
of things (IoT), data analytics, big data etc are likely to redefine
practically every aspect of our lives. One of the crucial areas where AI,
machine learning and data analytics is making long strides is behavioural
science and hiring.
WHAT IS BEHAVIOURAL SCIENCE
Though behavioural science for understanding the
personality of a candidate made its presence in the corporate arena somewhere
in mid-1900, it was majorly restricted to large corporations as it required a lot
of manual effort and resources. The process often led to the candidate going
through lengthy paper-based assessments and then the HR had to do manual
calculations and analysis to come up with a broad understanding of the person’s
traits. But now, with the advent of cloud computing, advanced data processing
and machine learning, even small to medium size organisations can make use of
behavioural science, at an affordable price, to pick the right talent.
Technology has made it possible to figure out a personality in less than six
minutes, as it gives the details on the working style of a person. Looking at
the advancement in AI and ML, the time is not far when the moment a person
walks in for an interview, complete profiling of the personality will be done.
Even today, solutions exist which claim to analyse the body language, facial
expressions, voice modulation etc to develop deeper understanding of the
candidate’s personality.
BEHAVIOURAL ASSESSMENT
Many organisations make the new joinees go through
the predictive index behavioural assessment which gives an insight into traits
such as their leadership style, communications style, multitasking
capabilities, comfort with team work and much more. It can help in uncovering
the true motivating needs of a person and how a person is expected to behave in
different work scenarios. All this helps the organisation in predicting how a
candidate is expected to perform in the particular role and what can be the
possible challenges in doing day to day activities. It is not enough to
understand the traits of the people.
Rather, even more pertinent is to determine what are
the competencies required for a particular role. This has been a blind spot of
a lot of psychometric systems that existed till date. The contemporary advanced
systems allow the organisation to exactly determine the ideal traits and their
intensities for performing a given job. Recently, one of our clients employed a
person for new sales position that was created for foraying into retail
segment. This position required the incumbent to be good with strategy,
comfortable with ambiguity, think out-of-the-box and be decisive. They hired a
person who was amazing in the interviews. But about 10 months down the line,
top management realised this person was very different from what they thought
during the interview. When this organisation got his predictive index
assessment, it clearly indicated this person’s natural traits were very
different from what was required for the role.
ADVANCEMENT IN DATA ANALYTICS
AI, machine learning and advancement in data
analytics makes talent analytics more accurate, fast and reliable. The systems
are now increasingly able to self-learn from the data and its usage patterns.
Top it up with the information and insights derived from the social media about
an individual. Systems of tomorrow would be able to combine all such pieces
together. Even these days such systems have become user-friendly and does not
require any special knowledge of psychology. The HR and top management can get
relevant actionable information for making the right hiring decisions. This
brings talent analytic system into what the manufacturing sector would terms as
– ‘Kaizen Mode or Continuous Improvement mode.’
(Vinaya Bansal -a workplace
behaviour expert and is co-founder of The Predictive Strategy Group)
TOI17DEC18
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