Business Analytics: The Science of Data-driven Decision Making

738 Pages·30.31 MB·English

by  U. Dinesh Kumar

37 mm
About the Book Key Features B
B u s i n e s s
l Equal importance to theory and practice with u
Written with the aim of becoming the primary
examples across industries.
resource for students of business analytics, this s
l Case studies providing deeper understanding
i
book provides a holistic perspective of analytics
of analytics techniques and deployment of n
with theoretical foundations and applications of
analytics driven solutions. e
the theory using examples across several l Coverage of topics such as basic probability s
A n a ly t i c s
industries. The content of the book starts with concepts, probability distributions, hypothesis
s
the foundations of data science and includes testing, multiple linear regression, logistic

descriptive, predictive and prescriptive analytics regression, decision trees, forecasting, A
topics which are discussed using examples from lustering, prescriptive analytics, stochastic
several industries as well as nine analytics case models and Six Sigma. n
studies distributed by Harvard Business l Data sets have been hosted in Wiley's a
Web page: https://www.wileyindia.com/
Publishing and used by several institutes across
l
business-analytics-the-science-of-data- y
the world. The book is enriched with 10 years of
driven-decision-making.html
teaching experience of the author at various t
l Discussion of analytics applications in i
programs in the Indian Institute of Management c
industries such as banking and finance,
Bangalore and several training programs and
e-commerce, healthcare, manufacturing, s The Science of Data-Driven Decision Making
consulting projects carried out by him. retail and services.

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The strongest point of this book is that it blends both theory and applications very well. a h
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—Professor M K Tiwari, IIT Kharagpur -D Sc U Dinesh Kumar
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This book is needed since many books in the market are either purely theoretical or practitioner e c
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—Dr M Mathirajan, Indian Institute of Science Bangalore c
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There are not many books with the level of comprehensiveness that Prof Dinesh's book provides. n

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—Kiran R, Director Data Sciences and Advanced Analytics, VMWare
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Wiley India Pvt. Ltd. ISBN 978-81-265-6877-2 u
m
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Business
Analytics
The Science of Data-Driven Decision Making

Business
Analytics
The Science of Data-Driven Decision Making
U Dinesh Kumar
Professor, Decision Sciences and Information Systems
Indian Institute of Management Bangalore
Business Analytics
The Science of Data-Driven Decision Making
Copyright © 2017 by Wiley India Pvt. Ltd., 4435-36/7, Ansari Road, Daryaganj, New Delhi-110002.
Cover Image: Ali Kerem Yücel/Getty Images
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First Edition: 2017
ISBN: 978-81-265-6877-2
ISBN: 978-81-265-8384-3 (ebk)
www.wileyindia.com
97881265 64071 Financial Services, 2nd ed
To Haritha and Prathusha
To the one who showed a lotus flower when asked to teach
Preface_Custom.indd vi 10/7/2016 4:01:28 PM
Preface
When the student is ready, the teacher will appear.
— The Buddha
Business Analytics has become one of the most important skills that every business school student should
acquire to become successful in a management career. The use of analytics across industries for decision
making, automation of business processes, products, and solutions driven by analytics makes it an essen-
tial skill for every student graduating from management and engineering disciplines. Analytics is used
as a competitive strategy by many successful companies. Many organizations generate solutions to their
problems using analytics and innovation in many companies is driven by analytics. Organizations such
as Amazon, Apple, Bigbasket, Capital One, Disneyland, Facebook, Flipkart, Google, IBM, and Walmart
have created solutions using analytics. Amazon has created solutions such as recommender systems
and Amazon Go that are driven by analytics. Apple’s predictive keyboard is another example of solu-
tions driven by analytics. Analytics is relevant not only to profit-making companies but also for govern-
ment and non-government organizations (NGOs). The Akshaya Patra Foundation, an NGO based out
of Bangalore, has used several analytics models for effective management of its free meal program which
provides free meals to about 1.5 million school children in India. Governments across the world use
analytics to handle black money and other economic offences.
One of the challenges that organizations face in analytics is finding the right talent. There is a severe
shortage of analytics talent across the globe. International Data Corporation (IDC) predicted a shortage
of 1,81,000 data scientists by 2018 and a severe shortage was also reported by Deloitte and McKinsey.
It is clear from the reported literature that there is significant shortage of talent in data science. Many
universities and institutions have started several undergraduate and post-graduate programs in Business
Analytics. Having said that, there is shortage of quality teaching material in Business Analytics. This
book makes an attempt to fill the gap. There are several books on analytics written for practitioners
that focus on strategic and tactical applications of analytics. There are also many books that explore the
theoretical aspects of analytics. I have chosen the middle path, discussing the theoretical foundation of
analytics as well as its applications and deployment using case studies. The objective of the book is to
introduce the students to foundations of data science and expose them to several advanced analytics
techniques using several applications and case studies from Indian organizations.
I have been teaching Business Analytics at the Indian Institute of Management Bangalore since 2008
as an elective course for the post graduate program in management. I started a certificate program in
analytics at IIMB in 2010 and have conducted several training programs and provided consulting ser-
vices in Analytics. The book is a collection of material created over the last 10 years and was tested among
students of post graduate program in management and working executives across several corporates.
Preface.indd 7 8/10/2017 6:04:01 PM
viii Preface
ORGANIZATION OF THE BOOK
The book has 17 chapters and addresses all components of analytics such as descriptive, predictive,
and prescriptive analytics. The first few chapters are dedicated to foundations of business analytics.
Introduction to business analytics and its components such as descriptive, predictive, and prescrip-
tive analytics along with several applications are discussed in Chapter 1. In Chapters 2 to 8, we discuss
basic statistical concepts such as descriptive statistics, concept of random variables, discrete and
continuous random variables, confidence interval, hypothesis testing, analysis of variance and cor-
relation. Chapters 9 to 13 are dedicated to predictive analytics techniques such as multiple linear
regression, logistic regression, decision tree learning, and forecasting techniques. Clustering is dis-
cussed in Chapter 14. Chapter 15 is dedicated to prescriptive analytics in which concepts such as
linear programming, integer programming, and goal programming are discussed. Stochastic models
and Six Sigma are discussed in Chapters 16 and 17, respectively. Each chapter is enriched with sev-
eral solved problems, multiple choice questions, and exercise problems. Many chapters include case
studies that will improve understanding of the concepts and enable students to understand how
analytics is used by the industry.
The book is written in such a way that a beginner can learn the concepts with little or no
assistance from the instructor. Each chapter starts with learning objectives and ends with exercise
problems. Students without any statistical background should go through the initial chapters before
reading predictive analytics chapters. The multiple choice questions at the end of the chapter will
ensure that the reader has understood the learning outcomes. The best way to understand the con-
cepts is to solve the exercise problems and case studies. The microsite of the book lists the data sets used
in the book. The case studies in this book are distributed through the Harvard Business Publishing
(HBP) case portal. Several institutions across the world have been using case studies for teaching
analytics.
FEATURES OF THE BOOK
Several features are incorporated in the book to make the learning effective and fun.
Learning Objectives
Learning objectives LEARNING OBJECTIVES
provide a summary of LO 8-1 Understand the concept of correlation and its role in analytics.
LO 8-2 Learn to calculate correlation between two continuous variables.
key concepts that would
LO 8-3
be discussed in the chap-
LO 8-4 Understand correlation between a continuous variable and a discrete variable.
ter and key takeaways LO 8-5 Learn to calculate correlation between two discrete variables.
from the chapter.
Preface.indd 8 8/10/2017 6:04:04 PM
Preface ix
3
Fun Facts 4.1.1 | When Jesus Christ Became 2nd Best in the World
The book is filled
with several fun
facts about insights and third place, respectively (Szczepanik, 2016). Apparently the poll was rigged by the supporters of the

derived from both On 25th April 1998, Manchester City was playing against QPR in Division 1 of English football and both
correct and incorrect teams were in danger of being relegated to division 2 (third tier of English football). When the goal was 1−1,
Jamie Pollock scored an own goal giving upper hand to QPR. Although the match ended in a 2−2 draw,
use of data.
2012, Szczepanik 2016). Collecting survey and ensuring that the sample is unbiased is one of the major
challenges in analytics. Summers (1969) grouped bias in survey research in to several categories such as
(a) sampling bias, (b) non-responsive bias, (c) respondent bias, (d) instrumentation bias, etc.
Case Study
Best way to learn
analytics is to
619
understand the
Customer Analytics at Flipkart.com
business context,
It was typical cloudy monsoon weather at Bangalore on July 28, 2015. In the Darwin room of Flipkart’s
deriving analytics
yManager Analytics were brainstorming various business problems that Flipkart as an e-commerce problem from busi-
d
u ness context and
tanalytics in every aspect of decision making. Forecasting demand for thousands of stock-keeping
Sunits (SKUs), predicting returns and cancellations of orders, predicting the reasons when custom- using data science

eers contact the customer service centers, optimizing markdown pricing, identifying various types of
s to derive insights
afrauds, optimizing vehicle routing, and enabling adherence to service-level agreements, were some of
Cthe typical problems that the analytics division of Flipkart was solving using state-of-the-art analytics and actionable
techniques. In 2015, the team included about 100 data scientists mostly recruited from institutes such
items. The book has
9 case studies on
use of analytics by
Indian companies.
Supplementary Online Material
The readers can enhance their learning experi-
ence by watching the predictive analytics
course offered by the author on edX platform. https://www.edx.org/course/predictive-
LEARNING OBJECTIVES
The course is part of the Indian Institute analytics-iimbx-qm901x
LO 8-1 Understand the concept of correlation and its role in analytics.
of Management Bangalore’s massive open
LO 8-2 Learn to calculate correlation between two continuous variables.
LO 8-3 online course (MOOC). The course videos
LO 8-4 Understand correlation between a continuous variable and a discrete variable. are available at the following link:
LO 8-5 Learn to calculate correlation between two discrete variables.
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