Download Data Warehouse and Business Intelligence

AUSFÜLLHILFE: BEWEGEN SIE DEN MAUSZEIGER ÜBER DIE ÜBERSCHRIFTEN. AUSFÜHRLICHE HINWEISE: LEITFADEN MODULBESCHREIBUNG
Data Warehousing and Business Intelligence
Module code
1
2
3
Workload
Credits/CP
Semester
180 h
6
1
Module
Teaching
Language
Frequency of module
Winter Semester
Contact hours
Self-study
Data Warehousing and Business
English
4 SWS / 45 h
Intelligence
Learning outcomes
After passing this module successfully, students are able to …
135 h
Duration
1 Semester
Class size
15
Knowledge (1)
 Differentiate the concepts of data warehousing and business intelligence
 Give an overview on important types of data warehouse architecture and BI functionality
 Describe the role of information and communication technology to meet the challengeces of international acting
enterprizes
Understanding (2)
 Understand process characteristics of data warehouse and BI systems
 Classify the relevant types of technological and business aspects and drivers
Practice (3)
 Define business case for BI prototype
 Select and apply appropriate methodological and architectural needs to define business case and BI prototype
Analysis (4)
 Analyse selected data warehouse and BI needs, described in a case studies (As-is and to-be concept, Excel
prototype).
Synthesis (5)
 Implementing business case concept by using Business Intelligence Software
Evaluation (6)
 Evaluate opportunities and threads of BI usage and implementation
Individual component content


Introduction/Overview
 Data Warehousing
 Fundamentals (e.g.: ETL, OLAP, Data Mining)
 Application areas (e.g.: Controlling, marketing)
The architecture of a Data Warehouse
 ROLAP and MOLAP
 SQL and Data Warehouse
 Semantic data models
 Data Warehouse-configurations
 Examples of configurations and software-tools
Version
1.3
Erstellt von
jr
Freigabe (Datum/Kürzel)
QM-Board 11.4.2012, 16.01.2013
04.06.2013/jr
Gültig ab
04.06.2013


4
Applications for a Data Warehouse
Business Intelligence (BI)
 Steps to Business Intelligence
 Data Warehouse and Operational Data Store (ODS)
 Development of integrated BI-Application-Systems
 BI project (Business case definition; as-is and to-be analysis; prototyping)
Teaching methods
Lectures style, exercises and practices (case study), presentations
5
Prerequisites


6
Basic principles in business administration and business information systems
Basic principles in database systems
Methods of assessment
Final written exam, presentation, written term paper
7
Applicability of module
Mandatory in Business Consulting Masters course
8
Person responsible for module/ lecturer
Prof. Dr. Monika Frey-Luxemburger
9
Reading list

Imhoff, C.; Galemmo, N.; Geiger, J. G.: Mastering Data Warehouse Design – Relational and Dimensional
Techniques. New York 2003.

Inmon, W. H.: Building the Data Warehouse. 4. Auflage, Indianapolis 2005.

Inmon, W. H.: Building the Operational Data Store. 2. Auflage, New York u.a. 1999.

Kimball, R.; Caserta, J.: The data warehouse ETL toolkit – Practical techniques for extracting, cleaning,
conforming, and delivering data. Indianapolis 2004.

Kimball, R.; Reeves, L.; Ross, M.; Thornthwaite, W.: The Data Warehouse Lifecycle Toolkit – Expert Methods for
Designing, Developing, and Deploying Data Warehouses. New York u.a. 1998.

Kimball, R.; Ross, M.: The data warehouse toolkit – The complete guide to dimensional modelling. New York u.a.
2002.

Moss, L.; Atre, S.: Business Intelligence Roadmap – The Complete Project Lifecycle for Decision-Support
Version
1.3
Erstellt von
jr
Freigabe (Datum/Kürzel)
QM-Board 11.4.2012, 16.01.2013
04.06.2013/jr
Gültig ab
04.06.2013
Applications. Boston u.a. 2003

Thomsen, E.: OLAP Solutions – Building Multidimensional Information Systems. 2. Auflage, New York u.a. 2002.
Version
1.3
Erstellt von
jr
Freigabe (Datum/Kürzel)
QM-Board 11.4.2012, 16.01.2013
04.06.2013/jr
Gültig ab
04.06.2013