32567 Business Intelligence for Decision Support
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Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksThere are course requisites for this subject. See access conditions.
Recommended studies: it is assumed that students are familiar with basic information system concepts and have basic software, database and mathematical skills
Description
Business intelligence is an umbrella term that combines architectures, tools, databases, analytical tools, applications and methodologies. The major objectives of business intelligence is to enable interactive access to data and to give business managers the ability to conduct analysis and make better decisions. Decision support systems are computer-based information systems that combine models/methods and data in an attempt to solve semi/non-structured decision problems with extensive user involvement through a friendly user interface. Business high-level decisions are often semi/non-structured and require an increased level of intelligent and technical support, in particular, when they become rich in data. Decision support systems can be integrated with variable business intelligence techniques to support related decision problem solving. This subject introduces business intelligence, decision support systems, group decision support, intelligent decision support, web-based support systems, decision optimisation technologies, personalised recommender systems. The subject also covers how to design, implement and integrate business intelligence techniques with methods to support business decision-making.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Identify key elements of the body of knowledge for the field of decision support technology and business intelligence. (D.1) |
|---|---|
| 2. | Identify main decision models, decision methods, and decision support systems. (D.1) |
| 3. | Apply decision support models, methods and systems in related business intelligence systems such as e-government, e-business, e-banking, e-logistics, e-learning and warning systems. (C.1) |
| 4. | Apply current issues in intelligent decision support systems, multi-criteria, multi-objective and multi-level decision support models, optimization theory, group decision-making models, computational intelligence (such as fuzzy logic), and their applications to business intelligence systems. (C.1) |
| 5. | Develop an in-depth understanding of selected parts of the material. (D.1) |
| 6. | Communicate in the form of technical reports and presentations. (E.1) |
| 7. | Work in a group to achieve a common objective and task. (E.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
- Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
- Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating autonomously within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
Teaching and learning strategies
Three-hour sessions per week of integrated presentations.
The subject will consist of approximately 18 hours of lectures and 21 hours of seminar/workshops supplemented by guided online/offline discussion. These sessions may be offered on a weekly basis throughout the session or in block mode.
Content (topics)
- Decision making and business intelligence
- Business decision models and methods
- Decision support system and its development
- Group decision support and business performance evaluation
- Multi-criteria decision-making and its business applications
- Intelligent and cognition-driven process for business intelligence
- Personalised recommender systems for business intelligence
- Information integration for business intelligence
- Web-based decision support technology in business intelligence
- Soft computation (such as fuzzy logic) application in business intelligence
- Advanced intelligent decision and business intelligence techniques
Assessment
Assessment task 1: Discussion post
| Intent: | To promote online engagement and discussion with peers around key idea in decision support. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 6 and 7 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): E.1 |
| Groupwork: | Individual |
| Weight: | 5% |
Assessment task 2: Essay
| Intent: | To explore and understand current techniques in decision support. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3, 4, 5 and 6 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1, D.1 and E.1 |
| Type: | Essay |
| Groupwork: | Individual |
| Weight: | 30% |
| Length: | Approximately 2500 words |
Assessment task 3: Class individual presentation
| Intent: | To present an oral summary of findings and respond to relevant questions from peers and experts. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 4, 5 and 6 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1, D.1 and E.1 |
| Type: | Presentation |
| Groupwork: | Individual |
| Weight: | 15% |
| Length: | About 15 minutes |
Assessment task 4: Research project
| Intent: | To explore new and emerging problems in decision support , find effective models, and provide solution to support decision making. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3, 4, 5, 6 and 7 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1, D.1 and E.1 |
| Type: | Project |
| Groupwork: | Group, group assessed |
| Weight: | 30% |
Assessment task 5: Presentation
| Intent: | To present problem findings and explain process to audience of peers and experts. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3, 4, 5, 6 and 7 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1, D.1 and E.1 |
| Type: | Presentation |
| Groupwork: | Group, individually assessed |
| Weight: | 20% |
| Length: | About 20 minutes (including questions) |
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Recommended texts
Efraim Turban, Jay Aronson (2007), Decision support systems and intelligent systems, sixth edition, Prentice Hall.
Gupta, J.N., Forgionne, G.A. and Mora, M. eds. (2007), Intelligent decision-making support systems: foundations, applications and challenges. Springer Science & Business Media.
Lu, J., Zhang, G., Ruan, D. and Wu, F. (2007), Multi-objective group decision-making: methods, software and applications, Imperial College Press, London.
References
George Marakas, Decision support systems in 21st century, second edition, Prentice Hall
Bernard Liautaud, E-Business Intelligence, McGraw Hill,2000
Some papers from the journal of Decision support systems, Computer and industry engineering, Information and management, and some articles given in classes.
Other resources
Canvas may be used to distribute course material, announcements and facilitate discussions, located at https://canvas.uts.edu.au/