EXPERT SPEAK
and functions. In manufacturing, for
example, firms are using predictive
analytics to achieve better inventory control
by tracking stock levels using IoT and
automating the replenishment process.
In fact, in asset-heavy industries like oil
and gas or power generation, the power of
predictive analytics is being harnessed to
service components and equipment based
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an present and past experiences
provide a window into the future? It is
an open debate on the individual front
but not so much for businesses as predictive
analytics holds the alluring promise of
visibility and predictability into what will
happen in the future.
If we consider the exponential growth in
devices connected to the Internet of Things
(IoT), with estimates ranging from 20 million
by 2020, according to Gartner and 25 million
by 2025, according to GSMA Intelligence,
predicting the future could only get better.
In fact, greater availability of data, storage
and computing power has helped bring
predictive analytics into the mainstream from
its high perch a decade or so ago.
Machine Learning, data mining, Artificial
Intelligence and predictive modelling
constitute the core elements of predictive
analytics solutions. An example of predictive
analytics at work in our daily lives is perhaps
weather forecasting, where current and past
data are used to predict the weather for the
days ahead.
But for businesses, its advantage lies in
identifying trends, understanding customers,
improving business performance and driving
strategic decision making. It wouldn’t be
incorrect to state that predictive analytics
could be used to produce deeper insights to
drive specific business outcomes.
Predictive analytics is being employed
across varied industry verticals, businesses
Predicting the future
is useful only when
that data and
information can be
transferred into
action before your
competitors beat
you to it.
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Issue 25
on their actual performance instead of a
time-based schedule.
In healthcare, there are opportunities to
use predictive analytics to improve patient
care to improve hospital management.
The telecoms industry was one of the
first verticals to use predictive analytics
for applications ranging from predicting
consumer churn to managing network assets.
In the insurance industry, predictive
analytics is being used not only to control
risks in underwriting, but also detect
insurance fraud claims.
To get started on predictive analytics, the
fundamental requirement is data availability.
In fact, the maxim is the more data you have,
the better. And the more accurate the data,
the more accurate are the predictive models
and their predictions. This data can be from
both sources that are internal and external
to the company. There is also the question
of whether to hire data scientists to build
predictive models in-house or use external
providers, which is a decision best left to the
company’s leadership.
However, do remember that it is easy
to become enamoured with predictive
analytics, so much so that making
predictions purely for the sake of predicting
the future could become a habit with zero
benefits. As predictive analytics solutions get
more and more accurate, and competitors
scramble to get on board, the challenge
would be to act on those predictions and act
quickly enough.
Remember, predicting the future is
useful only when that data and information
can be transferred into action before your
competitors beat you to it.
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