Training Industry Magazine - July/August 2019 - 24

Text mining reads each sentence in
verbatim from the feedback forms and
then breaks, chunks and organizes the
information. Then, it throws up key themes
in visual formats (such as highlighting pain
areas in red and successes in green).
Now this is something L&D teams can
work with! They now know what specific
aspects are working and which are not,
which
geography/group/department
is performing well and what can others
imbibe from them, and a host of other
meaningful insights. This helps L&D
teams create continuous improvements
over time.

LEARNING
LMSs today can generate a host of
useful insights from learner-generated
data. They can also generate individual
and group proficiency dashboards
by processing learners' pre- and postassessment scores or assigned target
score levels and achieved score levels.
Reports can also be generated to compare
the scores of managers, departments,
specific groups and/or locations, in
addition to showcasing trending scores
and the progression of scores over time.
Overall, artificial intelligence (AI) powered
LMSs have the ability to scan the host of
data and visually categorize scores, depict
trends and flag anomalies.
This empowers L&D teams much more
than traditional LMSs that provide
insights solely related to the amount
of time learners spent on the course,
individual learner scores and the most
popular courses. Thus, L&D teams can
accurately quantify the learning that
has taken place in the organization as a
result of L&D interventions.

BEHAVIOR AND RESULT
This is where AI and predictive analytics
come in. Data on behavior and results over
time are typically derived from outside the
LMS, from systems such as HR management
systems (HRMS), performance evaluations,
customer surveys and/or 360 surveys.
Today, even external data sources can
be plugged into the learning system to
provide a more accurate employee profile.
Systems such as Adobe Captivate Prime
have features that enable the automatic
importing of user details from HRMS or
other applications, such as Salesforce Dot

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Com (SFDC), into the system. Thus, the
system can recommend learning paths
based on a combination of assessment
scores and performance data. This is the
essence of predictive learning.
Predictive analytics is embedded in several
learning platforms today. Platforms like
EdCast and several others apply AI and
machine learning algorithms in order
to provide corporate learners with the
online training resources they require.
The resources are pulled from the LMS as
well as external sources, such as YouTube
videos, TED talks and learning libraries
on the web. As a result, every member of
your team can pursue personalized online
training paths to bridge gaps and improve
workplace performance. Learners are
given recommendations based on their
performance according to key metrics,
assessment scores, what courses they are
viewing already, what courses others in
similar roles are viewing, similar learning
needs and/or similar learning interests.
The result is wholesome learning
integrated with performance and
behavioral aspects. Training paths can
be re-adjusted based on behavioral
and performance changes, and the
data is available to managers to inform
evaluation feedback.

HOW TO KICKSTART AND
SUSTAIN LEVERAGING ANALYTICS
FOR LEARNING
L&D professionals should not shy away
from asking for support. They will need to
work closely with big data and analytics
experts to see where they are in terms of
the quantity and quality of learning data,
what improvements need to be made and
the time and effort that will entail. It is the
data that is key to showing results. L&D
professionals will also need to work with
technology experts to understand what
exists in the organization, to evaluate what
features their LMS supports (assuming
there is an LMS in place) and whether
they are optimally utilizing the LMS; they
should also evaluate whether a change
in LMS implementation is required.
Adopting an LMS that integrates with
the organization's HRMS and curates and
aggregates content from external sources
- while also employing AI and machine
learning for personalized learning - is the
way forward.

Ideally, this process should be followed
up with an implementation roadmap
and associated budgets, with relevant
stakeholders enlisted for approvals. This
would aid in ensuring the L&D team is
committed to driving business results by
understanding the value of training through
effective training evaluation practices.

Having the right data
will help drive continuous
improvement and
performance enhancement
across the business.
BENEFITS OF LEVERAGING DATA
MINING AND ANALYTICS IN L&D
Having the right data and accurate
insight into learning effectiveness will
help drive continuous improvement
and performance enhancement across
the business. Through these tangible
contributions to the business, L&D can
establish itself as not just a cost center -
but as a business partner.
Insights on skill levels, the alignment
of employee skills with business needs,
and the impact of learning on key
organizational metrics can enable more
informed decision-making. As a result, L&D
can better determine what courses need
to be designed and delivered, and can
make better decisions related to hiring,
staffing and competency development as
a result.
By leveraging data mining and analytics
in L&D, business stakeholders can
see how the learning organization is
addressing and impacting key business
imperatives,
including
operational
efficiency, employee performance and,
ultimately, business results.
Preeta Chandran is a digital transformation
and L&D professional with over 18 years of
experience. She is the CEO of eWandzDigital
Services, a digital media company, and was
formerly an operations leader with global
conglomerate Genpact. Email Preeta.
Training Industry Magazine - July/August 2019

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