Business Intelligence

Unit Code: HIT8413

Credit Points: 12.5 Credit Points

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Related Course/s:

A unit of study in the Master of Information Technology, Master of Information Technology (Professional Computing) and Master of Technology (Information Technology).

Aims & Objectives:

Students who complete this unit of study should be able to:

• Explain the notion of information value, classifying types and sources of value, and explaining the types of processing that can add value to corporate data sources.
• Explain the nature and role of business intelligence in contributing to the delivery of business value and competitive in modern organisations.
• Relate the business intelligence environment, specifically data warehousing and data mining, to different organisational contexts.
• Identify the potential benefits, risks, and range of organisational and managerial issues associated with a successful implementation of a business intelligence system.
• Explain the need for a data integration process, data profiling, data cleansing and data enhancement, and their contribution to adding value to data.
• Using the link between corporate strategy, IS strategy and business intelligence strategy, assess alignment in a particular context, and associate particular business analytics with particular business intelligence strategies.
• Distinguish between the concepts of knowledge discovery and creation, and data mining, and select appropriate data mining tools and techniques to implement a business intelligence strategy.
• Appreciate data modelling, star schemas, andnormalisation issues for data warehouses.

Teaching Methods:

Lecture (24 hrs) and Seminar or Tutorial (Labs) (12 hrs)

Assessment:

Group Assignments, Examination

Content:

• Concept of business value from corporate data, the exploitation of information for advantage, types and sources of information value.
• Nature and value of business intelligence, the business intelligence environment, and how types of data processing can add value to corporate data sources.
• Knowledge discovery, data mining, data warehousing.
• Business analytics.
• OLAP analysis, metadata.
• Customer Relationship management systems.
• The relationship between corporate strategy, IS strategy and business intelligence strategy.
• BI links to enterprise systems, SCM systems, KM systems.
• Structured & unstructured data, content management systems.
• Enterprise information portals, data delivery.
• Privacy, ethical, legal issues.
• Legacy data, data integration, data profiling, data cleansing and data enhancement.
• BI, Decision Support Systems, Expert Systems and Executive Information Systems.
• Data modelling, star schemas.

References:

Thierauf, RJ, Effective Business Intelligence Systems. Quorem Books, 2001.

Moss, LT & Atre, S, Business Intelligence Roadmap: the Complete Project Life Cycle for Decision Support Applications. Boston, Addison Wesley, 2003.

Biere, M, Business Intelligence for the Enterprise, Upper Saddle River, N.J., Prentice Hall, 2003.

Loshin, D, Business Intelligence: the Savvy Manager's Guide, San Francisco, Morgan Kaufmann, 2003.

Rajiv Sabherwal and Irma Becerra-Fernandez, 2010, Business Intelligence Practices, Technologies and Management, John Wiley and Sons, New Jersey, USA.