School of Business at the State University of New York at New Paltz

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20579 Business Intelligence 

Fall 2002

Course goals

Business Intelligence is an field focusing on creating and developing information and knowledge from both internal and external sources to better support business decisions.  This course examines recent development in Business Intelligent Systems from both management and technical perspectives.  On the management side, we examine the benefit of using such systems to improve business processes and customer satisfaction and what resources are needed in order to build and manage a business intelligence system.  On the technical side, the emphasis is placed on data warehouse designs and analytical techniques employed to identify useful patterns from data retrieves from a database.  Major topics include On-Line Analytical Processing, Data Mining, and Customer Relationship Management.  It discusses the need of a data warehouse as the foundation for an intelligent system followed by developing unique goals and designs of various types of intelligent systems with an emphasis on the role and impact of business intelligence in an organization's  decision makingprocess.  Practical projects and computer software are used to enhance students' experience in business intelligent systems. 

A major challenge in this course is to balance the need between technical skill and managerial insight so that students can gain an overview of the whole subject area while at the same time being able to obtain some hand-on experience.  We highlight the following two technical skills that are most relevant to the course.

Some Basic Knowledge of a Database Management Systems (DBMS) :

The course starts with a review of DBMS concepts that are covered by other School of Business courses both at undergraduate and MBA levels, for example, Business Decision Systems and Management Information System, in particular,  relational database and Entity-Relationship (ER) model. We first introduce those concepts using a small example created by Microsoft Access which has a friendly user interface.  We then move on a much larger database using a commercial database software, IBM Universal Database and introducing Structured Query Language (SQL). 

Data Analysis Skills :

Knowledge in analytical techniques such as cluster analysis, discriminant analysis, regression analysis, etc. is essential.  These topics are also covered in other School of Business courses, such as Statistical Analysis and Quantitative Methods for Business Research.  However, the emphasis is on applying those models instead of covering the mathematical details of the models.  As a result, good computer skills in Excel and SPSS are helpful. 


The two textbooks belong to the trade book category instead of the traditional textbook category.  As a result, they do not provide clear beginning of chapter learning objectives and end-of-chapter exercises.  However, they cover a broad range of subjects related to Business Intelligence and are easy to understand by omitting technical details. 

Chris Todman, Designing A Data Warehouse: Supporting Customer Relationship Management, 1/e, Prentice Hall, 2001, ISBN 0-13-089712-4 
Berry, M.J.A. and Linoff, G., Data Mining Techniques: for Marketing, Sales, and Customer Support, John Wiley & Sons, 1997, ISBN 0-471-17980-9 

There are many books published recently in this area. 

1.    Intelligent Enterprise (, Netlibrary, Business Week,,, etc. 
2.    Turban and Aronson, Decision Support Systems and Intelligent Systems, 6/e, Prentice Hall, 2001 
3.    Amrit Tiwana, Essential Guide to Knowledge Management , The: E-Business and CRM Applications, 1/e,  Prentice Hall, 2001 

Course Outline: 

Business Intelligence Systems have become increasingly important in today's competitive environment.  Its role has been elevated from purely assisting operational decisions to enhancing strategic planning.  This course starts from comparing the different needs and goals of a transaction-oriented database and an analysis-oriented data warehouse.  It covers the concept of object-oriented data warehousing design and star/snow-flake schemas.  After that, we examine the design and development issues in OLAP, Data Mining, and Customer Relationship Management.  A project of developing a decision support system using the models discussed in the course is used to enhance students' understanding of the subject matter. 

Weekly topics: 

(subject to modifications depending on students' background and interests)
(T stands for the textbook by Todman and BL stands for the textbook by Berry and Linoff) 

Week 1: Review of DBMS and Overview of Business Intelligence (T Chapter 1 & BL Chapter 1) 
    Key Contents: Relational Database, Entity-Relationship model, Normalization, MS Access, DB2, CRM 
Week 2: Data Mining Overview (BL Chapters 2-7) and Data Warehousing Overview (T Chapters 2-4) 
    Key Contents:  Values of data mining, Transactional Database and Data Warehouse, Star vs. snow-flake schemas,  Object-oriented design 
Week 3: Analytical Models  Part I (BL Chapter 8-10) and Conceptual Logical model and Implementation (T Chapters 5-7) 
    Key Contents: Market Basket model, Memory-Based Reasoning, and Cluster Analysis, Introduction to TPC database and project 
Week 4: Four analytical Models Part II (BL Chapter 11-14)
    Key Contents: Link Analysis, Decision Tree model , Artificial Neural Networks, and Genetic Algorithms 
Week 5: Data mining and Corporate Data warehousing  (BL Chapter 15, 17, 18  T: Chapters 9-10   ) 
    Key contents: Planning, Implementing, and Controlling of Data Warehousing/ Data Mining Projects 
Week 6: OLAP (BL Chapter 16) and TPC Project Review 
Week 7: Implementation and Discussion of the Project. 
Week 8: Final Examination 

Grading Policy

Term grade is determined from the following components: 

1.    Assignments (20%): several prototype model constructions are assigned as homework 
2.    Participation (20%): Students are expected to participate in class discussions.  In addition, each student will be assigned a topic and be responsible for finding supplementary information for the subject. 
3.    Exams (30%): There will be a final exam. 
4.    Projects (30%): A project to design and implement an intelligent system will be assigned. 


Option I: a more technical option

The project requires student to plan, implement, and control the project of building an intelligent model based on a given transactional database (TPC-H) obtained from the Transaction Processing Performance Council ( ).  The database is generated by C programs and at its original form does not contain any patterns.  Students need to fully understand the structure of this database and the activities it support as a first step.  After that, students are required to identify the types of information needed to support decision purposes using the data stored in the database.  Based on the information required, students then design the structure of a data warehouse so as to better support information retrieval for further analytical tasks.  Most of the tasks required in this project belongs to the planning stage.  Students do not need to fully implement their plans.  However, they should provide evidence on how the proposed data warehouse could be used to achieve the specified goals with a few database tables and some SQL statements and clearly state how the data warehouse design achieves the specified goals which are not attainable without the proposed data warehouse.

Option II: a more managerial option

Amid recent crisis in corporate accounting scandals, you are assigned the job to suggest a better system not only to monitor irregular activities but also to establish better evaluation / performance measures so that chief  executivesare motivated to seek financially and ethically sound objectives.  You may start with the background of the recent incidents involving Enron, Worldcom, and others, analyzing the broad environment that leads to those ffraudulentpractices including but not restricted to accounting system, government regulation, financial market, etc.  You then discuss possible rremediesthat have been implemented or proposed, or your own opinions followed by the design of an intelligent system listing the necessary data needed to be collected, the relationship between different variables, and how early warnings can be signified to alert the management and investors.

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