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Predictive analytics:
Looking into the future
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Sunil Paul, Co-Founder and
COO of Finesse, a Global
Digital Transformation
Company, which provides
Analytics and BI Solutions
to organisations across
industries, talks about the
potential for predictive
analytics to produce
deeper insights to drive
specific business outcomes.
C
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.
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 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
on their actual performance instead of time-
based schedule.
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.
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The telecom 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.
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.
In healthcare, there are opportunities to use
predictive analytics to improve patient care
to better hospital management.
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.
Sunil Paul, Co-Founder and COO of Finesse
Remember, predicting the future is useful
only when that data and information can
be transferred into action before your
competitors beat you to it. n
www.intelligentcio.com
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