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Data warehouses are designed to provide organizations with an integrated set of high quality data to support decision-makers. They should support flexible and multi-dimensional retrieval and analysis of data. Topics covered include data warehousing and decision-making, data warehouse design, data warehouse implementation, data sourcing and data quality, on-line analytical processing (OLAP) and data mining, customer relationship management systems, and case studies of data warehousing practice. This subject is part of the Business Analytics stream within the Master of Information Systems.
This subject introduces the compelling need for data warehousing, data warehouse architectures, decision making, data warehouse design, data warehouse modelling, data quality, data warehouse implementation - including the Extract Transform Load (ETL) process, and data warehouse use in supporting decision making – including decision making tools and OLAP. Readings are provided for all topics that introduce real world cases on data warehousing and related areas and include the use of data warehousing for competitive advantage, success and failure stories in Data Warehousing.
Intended learning outcomes
INTENDED LEARNING OUTCOMES (ILOs)
Having completed this subject the student is expected to:
- Be familiar with data warehousing and its relationship to decision-making
- Understand the main concepts underlying data warehouse design and implementation, data quality and retrieval and analysis of data
- Be familiar with the role of data warehousing in customer relationship management systems
On completion of this subject students should have the following skills:
- Students should develop skills in literature search and analysis, critical thinking and independent learning
Last updated: 6 December 2019