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Business Intelligence Practices 1-3 16 Sep 2008

Business Intelligence Practices 1-3 16 Sep 2008. Morteza Sargolzae Javan Web : www.msjavan.tk Email : msjavan@aut.ac.ir. Introduction. 1- Syllabuses and References 2- BI Definitions 3- OLAP/OLTP. Universities in this study:. San Jose State University

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Business Intelligence Practices 1-3 16 Sep 2008

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  1. Business IntelligencePractices 1-316 Sep 2008 MortezaSargolzaeJavan Web: www.msjavan.tk Email: msjavan@aut.ac.ir

  2. Introduction 1- Syllabuses and References 2- BI Definitions 3- OLAP/OLTP

  3. Universities in this study: • San Jose State University • Indian School of Business • University of California • University of Wisconsin Oshkosh • Webster University 3

  4. Intro- San Jose State University • Title: Business Intelligence Technologies • Code: CMPE 274 • Spring 2008 • Instructor: Dr. Magdalini Eirinaki • Email: Magdalini.Eirinaki@sjsu.edu • Web page: http://sjsu6.blackboard.com/webct/logon/1507417001 4

  5. Description- San Jose State University • 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

  6. Syllabus - San Jose State University

  7. References- San Jose State University • 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)

  8. Tools - San Jose State University Required Software: • Business Intelligence Development Studio (in SQL Server 2005 Developer Edition,

  9. Intro - Indian School of Business • Title: Business Intelligence using Data Mining • Instructor: Ravi Bapna, Ph.D. • Associate Professor of IS, Executive Director, CITNE • Email: ravi.bapna@gmail.com; • Blog : http://magicbazaar.blogspot.com

  10. Description - Indian School of Business • 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

  11. Syllabus - Indian School of Business • What is data mining? • Exploratory data analysis • Classification and Prediction • Simple Classification Schemes • Classification and Prediction • Affinity Analysis

  12. Reference - Indian School of Business • “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.

  13. Tools- Indian School of Business • Required Software We will make extensive use of Microsoft Excel and a data mining software called XLMiner, which is an Excel add-in.

  14. Intro - University of California • Title: Business Intelligence Technologies – Data Mining • Code: MGT/P 296 • Spring 2008 • Instructor: Professor Yinghui (Catherine) Yang • Graduate School of Management • Email: yiyang@ucdavis.edu • Web: http://faculty.gsm.ucdavis.edu/~yiyang

  15. Description - University of California 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.

  16. Syllabus - University of California • 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

  17. Reference - University of California • Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Second Edition • Michael Berry and Gordon Linoff, 2004, Wiley

  18. Intro - University of Wisconsin Oshkosh • Title: Business Intelligence • Code: Bus 782 • Spring 2007 • Instructor: Dr. George C. Philip • Office: Clow Faculty 207; • Email: Philip@uwosh.edu

  19. Description - University of Wisconsin Oshkosh • 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.

  20. Syllabus - University of Wisconsin Oshkosh • 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

  21. Reference - University of Wisconsin Oshkosh • Week 1: Intro to BI & Decision Making • Mulcahy, “ABCs of Business Intelligence”, CIO Magazine, Jan 2007. • 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. • Week 2: Decision Making • Pfeffer and Sutton, “Evidence-based Management”, Harvard Business Review, Jan 2006. • Baserman and Chugh, “Decisions without Blinders”, Harvard Business Review, Jan 2006. • Davenport, “Competing on Analytics”, Harvard Business Review, Jan 2006. • Hayashi, “When to Trust Your Guts”, Harvard Business Review, Feb 2001. • Week 3: Data Warehousing • Inmon, Building the Data Warehouse, 3rd Ed., Chapter 1, John Wiley, 2002. • Cooper, Watson, Wixom, Goodhue, “Data Warehousing Supports Corporate Strategy at First American”, MIS Quarterly, Dec 2000. • …

  22. Intro - University of Webster • Title: Data Mining • Code: COMP 5990 • Summer, 2005 • Instructor: Monte F. Hancock • Email: hancock@essexcorp.com

  23. Description - University of Webster • 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.

  24. Syllabus - University of Webster • 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.

  25. Reference - University of Webster • REQUIRED TEXTS: • Data Mining Explained: A Managers’ Guide to Customer-Centric Business Intelligence; Rhonda Delmater, Monte Hancock; Digital Press, 2001. (ISBN 1-55558-231-1), paperback.

  26. BI Definition(1) • University of Wisconsin-Stout:http://www3.uwstout.edu/lit/eis/dw/index.cfm • Business Intelligence is a process for increasing the competitive advantage of a business by intelligent use of available data in decision making.

  27. BI Definition(2,3) • 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

  28. BI Definition(4) • 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.

  29. BI Definition(5) • University of San Jose State: http://www.engr.sjsu.edu/meirinaki/courses/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.

  30. OLAP/OLTP • Performance • Architecture • Tools • Users • Test

  31. OLAP - Demo • http://www.microsoft.com/Industry/government/solutions/virtual_earth/demo/ps_gbi.html

  32. OLAP – Architecture (1) • University of Georgia State : http://www2.gsu.edu/~wwwkem/

  33. OLAP – Architecture (2) • http://cgmlab.cs.dal.ca/Members/obaltzer/SOLAP/solap_arch.png

  34. BI: OLAP - Popular Tools • Business Objects • Cognos • Hyperion • Microsoft Analysis Services • MicroStrategy • Microsoft Office PerformancePoint Server

  35. End of Presentation. • Thanks for your attention.

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