Master of Business Administration
(2019-20)
Strategic Business Intelligence
(Course Code)
Spring Semester
Name of the Instructor: Samadrita Bhattacharyya
INTRODUCTION TO THE COURSE
Strategic business intelligence is invaluable for improving any organization's strategic decision making process.
Managers extract knowledge and intelligence from various internal and external information sources and apply
them toward making better decisions. This course underscores the convergence of knowledge management (KM),
business intelligence (BI), and competitive intelligence (CI) into what is defined as strategic business intelligence
and emphasizes its importance in organizational success. It further sensitizes the students about how strategic
business intelligence these days is primarily propelled by innovative approaches of extracting knowledge and
insights from conventional and unconventional data streams. Thus motivated, this course also teaches different
data mining methods used in organizations for extracting strategically valuable insights.
COURSE OBJECTIVES
At the end of this course students should be able to

Understand concepts such as business intelligence, competitive intelligence and knowledge management

Learn how these three converges to provide strategically valuable insights to managers

Learn about data visualization and extraction

Understand the concepts of data warehousing and data extraction

Learn about various data mining techniques and their application on real data set (using Excel Miner,
SPSS, or Stata).
LEARNING GOALS
In addition to the specific course related objectives, this course is designed to achieve the following learning goals
1. Critical and Integrative thinking: Each student will be able to identify key issues in Strategic business
intelligence, develop a perspective that is supported with relevant information and integrative thinking, to
draw and assess conclusions. This learning goal will be measured through exams, cases and projects.
2. Awareness of Global Issues affecting - Information Systems Related Decisions: Each student will be able
to identify key relevant global issues and be able to analyze the impact of the global environment on –
Information Systems, as compared with domestic market related management issues. This learning goal
will be assessed through class discussion on various cases of successful technology applications.
3. Interpersonal Awareness and Working in Teams: Each student shall demonstrate an ability to work
effectively in a team, exhibiting behavior that reflects an understanding of the importance of individual
roles and tasks and the ability to manage conflict and compromise so that team goals are achieved. The
group-based case presentations and the group project will be a major component for measurement of
this learning goal.
4. Effective Presentation Skill: Each student shall be able to communicate verbally in an organized, clear,
persuasive manner and be a responsive listener. Participation during class interaction and class
presentations will be used to assess effective oral communication.
TEACHING METHOD
The course will have a judicious mix of lectures, cases, and hands on exercises. Use of case studies and examples
during the lecture will help the students understand the concepts and how they are applicable in real business
setting. Additional reading materials will help the students learn the concepts more clearly and in depth.
ATTENDANCE POLICY
Attendance to all the sessions is as per the university rules (minimum 75% excluding all leaves). The following
instances will also be treated as absence unless prior permission is taken



Attending only part of the session, either entering or leaving during the break
Arriving in class after the session is scheduled to begin
Failing to display the name card
GRADING
The course grade will be determined on the basis of
Evaluation Item
Weightage
Nature
End term
40%
Individual
Case presentations
10%
Group
Class Quiz
20%
Individual
Project Work
30%
Group
PROJECT (TEAM BASED)
Each team needs to identify a dataset related to a real business/ social problem (Plenty of open source data sets
are available online for various data analytics/ data science competitions, e.g., Kaggle). Students are required to
frame the business problem clearly. Then they need to apply any one or more data mining techniques to the data
set to generate usable insights addressing the business problem.
DELIVERABLES
Each team needs to submit a maximum 8 page report (1.5 spaced, 12 point font, Arial Narrow; exhibits extra; 1inch margin) to the instructor over email on or before the mutually decided date. This project reports should be
submitted in MSWORD/ PDF format. They will also be asked to submit the Excel Miner output file showing the
results of the data mining techniques used.
Apart from that each team will have to present their project in front of the class (15 minutes presentation) and take
up questions regarding the same. The date of presentations will be notified to the teams later.
TEXT BOOK, COURSE PACKAGE AND OTHER READINGS
Text book: Data Mining for Business Intelligence: Concepts, Techniques and Applications in Microsoft Office Excel
with XLMiner. Shmueli, Patel, and Bruce. Wiley.
Chapters from this book are assigned as required readings in the class schedule below. The lectures will use the
book chapters as reference and may elaborate on some topics going beyond the scope of the text book. In that
case additional reading materials will be provided in class. Cases will be announced in the class.
Additional book for reference: Strategic Intelligence: Business Intelligence, Competitive Intelligence, and
Knowledge Management. Jay Liebowitz. Auerbach Publications; 1 edition.
CLASS SCHEDULE
(Each session is of 90 minutes’ duration)
Session No-1
Introduction to strategic business intelligence (SBI)
Objective of the session
At the end of this session you will learn

Concept of strategic business intelligence

Importance of strategic business intelligence in organizations

BI architecture
Readings
Reading materials to be given later.
Case Title and Number
To be announced in the class
Pedagogy
Lecture and class discussion
Session No-2
Competitive intelligence
Objective of the session
At the end of this session you will learn

What is CI?

CI life cycle

Link between CI and Strategic business intelligence
Readings
Reading materials to be given later.
Case Title/ Number
To be announced in the class
Pedagogy
Lecture and class discussion
Session No-3
Knowledge management (KM) - I
Objective of the session
At the end of this session you will learn

Basic concepts of knowledge management

Different types of knowledge in organization

KM life cycle
Readings
Reading materials to be given later.
Case Title/ Number
To be announced in the class
Pedagogy
Lecture and class discussion
Session No-4
Knowledge management (KM) - II
Objective of the session
At the end of this session you will learn

KM systems in organizations

Knowledge creation and knowledge architecture

Knowledge capture and codification

KM portals and tools
Readings
Reading materials to be given later.
Case Title/ Number
To be announced in the class
Pedagogy
Lecture and class discussion
Session No-5
Data warehousing -I
Objective of the session
At the end of this session you will learn about

Basic concepts of data warehousing

OLAP

Schemas
Readings
Reading materials to be given later.
Case Title and Number
To be announced in the class
Pedagogy
Lecture and class discussion
Session No-6
Data warehousing - II
Objective of the session
At the end of this session you will learn

Data extraction

ETL
Readings
Reading materials to be given later
Case Title/ Number
To be announced in the class
Pedagogy
Lecture and class discussion
Session No-7
DW/BI tool

Objective of the session
Hands on experience with DW/BI tool
Readings
NA
Case Title/ Number
To be announced in the class
Pedagogy
Lecture, class discussion, hands on, demo
Session No-8
Data mining concepts
Objective of the session
At the end of this session you will learn

Steps in data mining

Model building in data mining

Regression
Readings
Chapter 2 & 3 of the text book.
Case Title/ Number
To be announced in the class
Pedagogy
Lecture and class discussion
Session No-9
Classification techniques
Objective of the session
At the end of this session you will learn

What is classification?

Different classification models: KNN, Naïve Bayes, CRT
Readings
Chapter 6 and 7
Case Title/ Number
To be announced in the class.
Pedagogy
Lecture, class discussion and in-class exercises
Session No-10
Basic concepts of cluster analysis
Objective of the session
At the end of this session you will learn

What is cluster analysis?

Why is it important?

Hierarchical and non-hierarchical clustering
Readings
Chapter 12 of the text book
Case Title and Number
To be announced in the class.
Pedagogy
Lecture, class discussion and in-class exercises
Session No-11
Association rule mining
Objective of the session
At the end of this session you will learn about

Basic concept of association rule mining

Discovering associations in transactional database

The Apriori algorithm
Readings
Chapter 11 of the text book
Case Title/ Number
To be announced in the class
Pedagogy
Lecture, class discussion and in-class exercises
Session No-12 & 13
Data mining tool demo

Objective of the session
Hands-on with Excel Miner
Readings
NA
Case Title/ Number
NA
Pedagogy
Hands-on with Excel Miner
Session No-14
Business performance management
Objective of the session
At the end of this session you will learn

Dashboards, KPI, balance scorecards…
Readings
Will be given later
Case Title and Number
To be announced in the class.
Pedagogy (choose one Lecture and class discussion
of the following)
Session No-15
Project presentation
Note: The content of the sessions could be slightly modified during the course depending on the receptivity and
pace of learning of the students in class.