Course Description

This course focuses on the features, uses, and design strategies for IT-enabled managerial decision support. Data-oriented techniques for business intelligence and corporate decision making are emphasized. Technology context includes an overview of business intelligence framework, business process management and application-based business analytics and reporting. Specific techniques include business reporting using pivot tables, descriptive statistics, statistical process control, and other tools common to business process improvement. The course is project-based and the context is Corporate Performance Management with emphasis on the Six Sigma methodology of process improvement.

Course Objectives

· Develop a foundation in Business Intelligence (BI) for Business Analysis.

· Understand decision makers, the decision making process and the role of decision support tools in an organization.

· Understand the different aspects of the BI environment, and key success factors.

· Design, develop and implement a desktop BI system.

· Be able to create reports using pivot tables – business reporting.

· Be able to estimate means and proportions using statistical estimation techniques and apply them in the context of a business problem.

· Be able to identify best practices by comparing means and variances using statistical hypothesis testing.

· Develop a foundation of understanding for the Lean Six Sigma (LSS) business process improvement methodology and its relationship to BI.

· Apply BI and LSS tools and techniques in the context of a business process improvement case study.

Grading

Exam	25%
Course Project (Group Work)	30%
Course Case Study (Group Work)	35%
Group Presentation	10%

Grading Scale

A	93% and Above
A-	90% and Above
B+	87.5% and Above
B	83% and above
B-	80% and Above
C+	77.5% and Above
C	73% and Above
C-	70% and Above
D	60% and Above
F	Below 60%

Policies and Procedures:

· I expect you to publish (turn-in) your reports on time to receive proper credit/grade. You will demonstrate continuous improvement in the quality of both content and presentation as we progress through the semester.

· While discussion with classmates is encouraged, any work submitted must be your own (or your group’s, for group projects).

· I expect everyone to contribute equally to group assignments. I encourage collaborative learning. Your assignments should be in a report format (Word, html, etc.) and should be posted on the web when possible. I may assign exercises during class or as after-class assignments, and each question should address know-what, know-how, know-why, and care-why aspects. At the beginning of each class period, I may select a few groups to make a brief presentation of the previous week's work.

· I will try to maintain the class schedule; however, I may need to make adjustments. Although some lecture notes will be posted to my website, assignments and exams will be based on the comprehensive body of knowledge resulting from lecture, discussions, and assigned readings.

· Attendance in every class is expected and class participation and discussion is strongly encouraged. All of us share the responsibility to come to class prepared and on time. The student is responsible for any materials missed as a result of an absence.

· Late work will not be accepted unless prior arrangements have been made directly with me (minimum 2 days before due date on weekly assignments). Cases will be decided on an individual basis.