QM901x – Predictive Analytics
Prof. U Dinesh Kumar
Module 1 – Clip 01
Week 3
Introduction to Analytics
Hello Welcome to the Course Predictive Analytics.
My Name is Dinesh Kumar and I am a Professor at the Indian Institute of Management
Bangalore.
I will be the instructor for the course.
There is a lot of Buzz around Analytics. I am going to discuss why Analytics is very Popular
and How companies are benefiting by use of Analytics.
Today Many beliefs that the next economic growth will be driven by Analytics and Artificial
Intelligence.
Let me First Introduce the Business Analytics First.
I Would like to start with the famous quote by Edwards Deming, he said "In God we trust, all
others must bring data".
He probably mentioned this quote in 1930's or 40's, long before terms such as data scientist
or analytics or Artificial Intelligence was in use.
Let us try to understand what may have triggered Deming to come up with this particular
quote. When we talk about decision making, many organizations actually use different
approaches. What happens is that, when organizations face problem they form a committee.
And one person in the committee will immediately form a Whatsapp group and send
irrelevant things.
In the committee meetings, members of the committee express their opinion about what
decision to take and how to solve the problem. There is one famous algorithm called "Hippo
Algorithm" which stands for "Highest Paid Person's Opinion". Typically in most cases they
may go with the opinion of the highest paid person, usually it may be the Opinion of CEO or
CFO. When organization take decision based on highest paid persons opinion, they can go
wrong. So that's what probably triggered the quote Deming, “In god we trust all others must
bring data.
The Quote emphasizes the importance of data and analytics in Decision Making.
Let us try to understand what is Business Analytics?
© All Rights Reserved, Indian Institute of Management Bangalore
QM901x – Predictive Analytics
Prof. U Dinesh Kumar
Module 1 – Clip 01
Business Analytics is a multidisciplinary field that uses expertise such as statistical learning,
Week 3
machine learning, artificial intelligence, computer science, information technology and
management strategies to generate value from data.
It has three main components Business Context, Science and Technology.
Business Context is important since the success of Analytics will Depend on the ability to ask
rights Questions. One of the Frequent Examples quoted by many about successful application
of analytics is Target’s Pregnancy Test. Target is one of the Largest Retail Chains in India and
early 2000, they developed a analytical model to predict whether a customer is pregnant or
not.
You may wonder why target was interested in Knowing whether someone is Pregnant? Well,
they are special customers since they are likely to price insensitive. They would like to buy
the best product as long as they can afford it during pregnancy and immediately after the
child birth. It was estimated that a typical American parent spend about 7000 dollars before
the child reaches its first birth day and the Market Size of Mom and Baby Products was close
to 37 Billion USD. So, We are Talking about hugh market of Price insensitive customers, and
capturing them early will add significant value.
The Next Component of Business Analytics is Technology. Taking the Target’s Example, they
have to collect transactional data of customer purchases, store it, retrieve it and analyses it
to gain insights. This will require Data Base Systems, Software Tools to analyse the Data etc.
In case of Big Data, which I will discuss later, we will need sophisticated technologies to handle
huge volume of data.
The Third Component of Analytics is the Science Part. Taking the Target’s Pregnancy Test
Example, basically we have to classify a customer as either Pregnant or not Pregnant. In
Analytics this is known as classification problem. Problems such as customer churn, employee
attrition and fraud are examples of classification Problems. There are many algorithms used
for solving Classification Problems such as logistic regression, decision trees, random forest,
neural network and so on. The role of Data Science component of analytics is to find the best
algorithm for a given problem based on a selection criteria.
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