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BUSINESS INTELLIGENCE
PRACTICES 1-3
16 SEP 2008
Morteza Sargolzae Javan
Web: www.msjavan.tk
Email: msjavan@aut.ac.ir
Introduction
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1- Syllabuses and References
2- BI Definitions
3- OLAP/OLTP
Universities in this study:
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San Jose State University
Indian School of Business
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University of California
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University of Wisconsin Oshkosh
Webster University
Intro- San Jose State University
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Title: Business Intelligence Technologies
 Code:
CMPE 274
 Spring 2008
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Instructor: Dr. Magdalini Eirinaki
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Email: Magdalini.Eirinaki@sjsu.edu
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Web page: http://sjsu6.blackboard.com/webct/logon/1507417001
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Description- San Jose State University
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This course covers technologies that are key to
delivering business intelligence to an enterprise.
Prerequisites:
 CMPE
272: Enterprise Software Overview
 CMPE 273: Enterprise Distributed Objects
Syllabus - San Jose State University
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References- San Jose State University
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Required textbooks:
 OLAP
Solutions: Building Multidimensional Information
Systems. by Erik Thomsen Wiley, 2nd edition (2002)
 Data Mining Techniques for Marketing, Sales, and
Customer Relationship Management by Michael J. A.
Berry and Gordon S. Linoff Wiley (2004)
Tools - San Jose State University
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Required Software:
• Business Intelligence Development Studio (in SQL
Server 2005 Developer Edition,
Intro - Indian School of Business
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Title: Business Intelligence using Data Mining
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Instructor: Ravi Bapna, Ph.D.
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Associate Professor of IS, Executive Director, CITNE
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Email: ravi.bapna@gmail.com;
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Blog : http://magicbazaar.blogspot.com
Description - Indian School of Business
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An important feature of this course is the use of Excel,
an environment familiar to MBA students. All required
data mining algorithms (plus illustrative data sets) are
provided in an Excel add-in, XLMiner
Syllabus - Indian School of Business
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What is data mining?
Exploratory data analysis
Classification and Prediction
Simple Classification Schemes
Classification and Prediction
Affinity Analysis
Reference - Indian School of Business
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“Data Mining for Business Intelligence: Concepts,
Techniques, and Applications in Microsoft Office
Excel with XLMiner”
by Galit Shmueli, Nitin R. Patel and Peter C. Bruce,
Wiley, 2007.
Tools- Indian School of Business
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Required Software
We will make extensive use of Microsoft Excel and
a data mining software called XLMiner, which is an
Excel add-in.
Intro - University of California
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Title: Business Intelligence Technologies
– Data Mining
Code: MGT/P 296
Spring 2008
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Instructor: Professor Yinghui (Catherine) Yang
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Graduate School of Management
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Email: yiyang@ucdavis.edu
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Web: http://faculty.gsm.ucdavis.edu/~yiyang
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Description - University of California
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The course focuses on two subjects simultaneously:
1- The essential data mining and knowledge
representation techniques used to extract
intelligence from data and experts. Such techniques
include decision trees, association rule discovery,
clustering, classification, neural networks, nearest
neighbor, link analysis, etc.
2- Common problems from Marketing, Finance, and
Operations that demonstrate the use of various
techniques.
Syllabus - University of California
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Course Overview, Intro to Data Mining
Market Basket Analysis & Association Rules, CRM
Market Segmentation & Clustering, Prepare data
Prediction & Classification – Decision Tree
Personalization & Nearest Neighbor
Financial Forecasting & Neural Networks
Link Analysis & Web mining
Reference - University of California
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Data Mining Techniques: For Marketing, Sales, and
Customer Relationship Management, Second Edition
 Michael
Berry and Gordon Linoff, 2004, Wiley
Intro - University of Wisconsin Oshkosh
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Title: Business Intelligence
Code: Bus 782
Spring 2007
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Instructor: Dr. George C. Philip
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Office: Clow Faculty 207;
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Email: Philip@uwosh.edu
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Description - University of Wisconsin Oshkosh
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The objective of the course is to provide students with
an understanding of various aspects of business
intelligence systems and knowledge management,
with a managerial focus.
Syllabus - University of Wisconsin Oshkosh
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Intro to BI & Decision Making
Decision Making
Data Warehousing
Data Warehouse Architectures
ETL
Data Capture and Data Quality
Data Mining
Document Warehousing & Text Mining
Knowledge Management & Expert Systems
Reference - University of Wisconsin Oshkosh
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Week 1: Intro to BI & Decision Making
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Mulcahy, “ABCs of Business Intelligence”, CIO Magazine, Jan 2007.
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Jacobs, “Data Mining:What General Managers Need to Know”, Harvard Management
Update,October 1999.
Hammond, Keeney, and Raifa, “The Hidden Traps of Decision Making”, Harvard Business
Review, Jan 2006.
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Week 2: Decision Making
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Pfeffer and Sutton, “Evidence-based Management”, Harvard Business Review, Jan 2006.
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Baserman and Chugh, “Decisions without Blinders”, Harvard Business Review, Jan 2006.
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Davenport, “Competing on Analytics”, Harvard Business Review, Jan 2006.
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Hayashi, “When to Trust Your Guts”, Harvard Business Review, Feb 2001.
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Week 3: Data Warehousing
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Inmon, Building the Data Warehouse, 3rd Ed., Chapter 1, John Wiley, 2002.
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Cooper, Watson, Wixom, Goodhue, “Data Warehousing Supports Corporate Strategy at First
American”, MIS Quarterly, Dec 2000.
…
Intro - University of Webster
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
Title: Data Mining
Code: COMP 5990
Summer, 2005
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Instructor: Monte F. Hancock
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Email: hancock@essexcorp.com
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Description - University of Webster
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The course will focus on practical applications of data mining
for business decision making.
Generally available tools (e.g., EXCEL) will be used to illustrate
the development of decision support applications for the
modern data-centric enterprise.
Syllabus - University of Webster
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The data mining process
Information technology and “data”
Mathematics of data mining
Knowledge discovery
Predictive modeling
Data mining in the “real world”: Overcoming
obstacles, data mining project management.
Reference - University of Webster
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REQUIRED TEXTS:
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Data Mining Explained: A Managers’ Guide to Customer-Centric Business
Intelligence; Rhonda Delmater, Monte Hancock; Digital Press, 2001.
(ISBN 1-55558-231-1), paperback.
BI Definition(1)
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University of Wisconsin-Stout:
http://www3.uwstout.edu/lit/eis/dw/index.cfm
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Business Intelligence is a process for increasing the
competitive advantage of a business by intelligent
use of available data in decision making.
BI Definition(2,3)
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http://en.wikipedia.org/wiki/Business_intelligence
Business intelligence (BI) refers to technologies,
applications and practices for the collection,
integration, analysis, and presentation of business
information and sometimes to the information itself.
The purpose of business intelligence is to support
better business decision making.[1] Thus, BI is also
described as a decision support system (DSS)[2]
1) H. P. Luhn (October 1958). "A Business Intelligence System“ . IBM Journal. Retrieved, 2008.
2)D. J. Power "A Brief History of Decision Support Systems, version 4.0". DSSResources.COM. Retrieved on 2008
BI Definition(4)
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University of Michigan:
www.businessintelligence.umich.edu
BI is an IT term that refers to the
collecting, structuring, analyzing
and leveraging of data to turn it
into easy-to-understand
information. This enables the
leaders to use their expertise to
make data-driven decisions.
BI Definition(5)
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University of San Jose State:
http://www.engr.sjsu.edu/meirinaki/cours
es/cmpe274s08/cmpe274.html
The goal of business intelligence is
to analyze and mine business
data to understand and improve
business performance by
transforming business data into
information into knowledge.
OLAP/OLTP
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Performance
Architecture
Tools
Users
Test
OLAP - Demo
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http://www.microsoft.com/Industry/government/solutions/virtu
al_earth/demo/ps_gbi.html
OLAP – Architecture (1)
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University of Georgia State : http://www2.gsu.edu/~wwwkem/
OLAP – Architecture (2)
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http://cgmlab.cs.dal.ca/Members/obaltzer/SOLAP/solap_arch.png
BI: OLAP - Popular Tools
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Business Objects
 Cognos
 Hyperion
 Microsoft Analysis Services
 MicroStrategy
 Microsoft Office PerformancePoint Server
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End of Presentation.
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Thanks for your attention.