Target developed an analytical model in the early 2000s to predict whether customers were pregnant based on transactional data. They were interested because expecting mothers represent a huge, price-insensitive market for mom and baby products. The model classified customers as pregnant or not pregnant, which is known as a classification problem in analytics. It required collecting, storing, and analyzing customer purchase data using database systems and software tools. Data science techniques like logistic regression and decision trees can be used to solve classification problems and find the best algorithm for a given problem.

1. Predictive analytics
"In God we trust; all others must bring Data"
-
Edwards Deming

2. • The epigraph captures the importance of analytics and data-driven decision
making in one sentence.
• During the early period of the 20th century, many companies were taking
business decisions based on 'opinions' rather than decision based on proper
data analysis (which probably acted as a trigger for Deming's quote). Opinion-
based decision making can be very risky and often leads to incorrect
decisions. One of the primary objectives of business analytics is to improve
the quality of decision making using data analysis

3. At the end of this module, you will be able to
understand:
• The foundations of analytics and how it is becoming a competitive strategy for many organizations.
• The importance of analytics in decision making and problem solving.
• How different organizations are using analytics to gain insights and add value.
• How organizations are using analytics to generate solutions and products.
• Different types of analytical models.
• Framework for analytics model development and deployment.

5. 2.1 Analytics Overview
• Analytics is a body of knowledge consisting of statistical, mathematical, and operations research
techniques; artificial intelligence techniques such as machine learning and deep learning
algorithms; data collection and storage; data management processes such as data extraction,
transformation and loading (ETL); and computing and big data technologies such as Hadoop,
Spark, and Hive that create value by developing actionable items from data. The primary macro-
level objectives of analytics are problem solving and decision making.
• "Analytics help organizations to create value by solving problems effectively and assisting in
decision making"
At the end of this sub-section, you will be able to understand:
• The importance of data and analytics in decision making.
• The different components of business analytics.
• That the success of analytics depends on the ability to ask rights Questions.

6. • 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.

7. • Business Analytics is a multidisciplinary field that uses expertise such as statistical learning, 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.

8. 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.