Enterprise Data Management

MGT 548

▪ Course Description:

Formulating data is the first and crucial step in developing an automated information system for enterprises. A chief source of failure of large-scale IS projects is unfortunately due to bad data design. Data is the most significantasset of enterprise. What sort of data do we have and how do they flow throughout the entire enterprise ought to be grasped prior to the development of application programming. Programming, in this sense, should not be allowed to precede the data modeling phase. In terms of time and expenditure in IS development, data modeling outweighs application programming. In this course, data modeling methodologies are dealt with through term projects, each chosen by an individual student. Conduct of the project is made from the beginning of the course until its end. No examinations will be conducted in this course.Prior knowledge on information technology is not needed for taking this course. A number of real-world cases are dealt with in this course.

▪ Class Schedule: TBD

▪ Grading Policy: No exams.

Term projects are the only means of evaluation. Phase by phase, a term project is carried out by consecutively submitting certain portions of the project, five-to-seven times throughout the course, based on the comments and feedbacks made by instructor for their previous submissions.There is no mid-term or final examination.

▪ Instructor: Songchun Moon

▪ Text and Reading Materials:

  1. Songchun Moon, Data Architecture, HyungSeul Publishing Co., 2004.

2. C. Batini, S. Ceri and S. Navathe, Conceptual Database Design,

Benjamin Cummings, 1992.

3. Hawryszkiewycz, Database Analysis and Design, Macmillan, 1991.

▪ Lecture Schedule:

  1. What is data in enterprises ?
  2. Enterprise-wide perspectives of data
  3. How does consistency matter?
  4. Phases in Information Systems Development
  5. Data-Oriented Approach- Why is data redundancy harmful ?
  6. Idea of Object Orientation
  7. What does Enterprise Resource Planning tell us ?
  8. Data Modeling Methodologies
  9. Data Formulation Step
  10. Entity-Relationship Modeling Step
  11. What is Normalization all about ?