IFN655 Advanced Business Intelligence Concepts for Enterprise Systems

Unit Outline: Semester 2 2026, Gardens Point, Internal

Overview

This unit elevates the Business Intelligence (BI) expertise for enterprise systems by focusing on sophisticated use of data science and machine learning in enterprise settings. BI represents a process fuelled by technology, focusing on the analysis of data and the delivery of actionable insights, crucial for the planning and decision-making activities carried out by executives, managers, and staff. This unit involves AI-driven analytics, data science, machine learning techniques and tools, and advanced IoT analytics, thereby preparing students for high-level managerial decision-making. To support this, you will learn advanced predictive modelling, real-time analytics, and complex data visualization. The unit also covers data Ops, security, and governance. The unit provides a rich exposure to real-world BI platforms, integrating advanced BI skills from data analysis to strategic decision support, ensuring graduates are adept in both technology and its business applications.

Learning Outcomes

On successful completion of this unit you will be able to:

1. Analyze the advanced concepts and strategic importance of Business Intelligence in modern enterprises, identifying key trends and future developments
2. Develop advanced techniques and skills in data analytics, visualization, and prediction relevant for enterprise planning and operations.
3. Apply Business Intelligence techniques using tools and technologies available through enterprise BI platforms to solve real-world business problems.
4. Evaluate comprehensive Business Intelligence solutions using advanced tools and technologies to solve complex, real-world business problems.
5. Justify the data governance, ethical and legal considerations for enterprises using Business Intelligence.

Content

Learning Approaches

You can expect to spend 12-15 hours per week involved in preparing for and attending scheduled classes, preparing, and completing assessment tasks as well as independent study and consolidation of your learning. The unit uses pre-recorded lectures, case studies, and practical exercises to develop your understanding of the theory and practice of Business Intelligence using Enterprise Systems.

The pre-recorded lectures and online activities will provide you with the knowledge and skills for using Business Intelligence in Enterprise Systems, modelling, analysing, visualizing, and predicting data and insights for organizations.

Tutorials will be conducted in face-to-face computer labs on-campus or online. They will be activity-based involving modelling, analysing, visualizing, and predicting data and insights for large-scale organizations. The tutorials build directly on the material presented in the pre-recorded lectures and will involve detailed instruction sheets for undertaking the required tasks. They are designed to support class instruction, group work, and class reflection.

QUT Canvas site will be used for lecture notes, tutorial materials, reading resources, and online class discussions.

This unit emphasises practical skills and artifact-driven learning. Students actively engage in hands-on exercises, supplemented by readings and discussions from the development community, to gain real-world experience and prepare for future challenges

Feedback on Learning and Assessment

Students can obtain feedback on their progress throughout the unit through the following mechanisms:

• Class and group-based feedback on workshop exercises
• Written feedback on the formative phase of assessment tasks
• Written feedback on summative phases of assessment tasks including a rubric
• General verbal feedback will be provided to the entire class on assessment tasks
• You will receive feedback and results on each assessment task prior to the submission of the next assessment task

Assessment

Overview

The assessments in this unit have been designed so that you may develop knowledge and skills for applying business intelligence using enterprise systems. Students will develop the skills in planning, modelling, reporting, and predicting structures underpinning business intelligence.

The assessments will be structured through the different perspectives of applying BI in enterprise systems. It includes planning, modelling, designing, and developing a BI solution for a given case organization and working with advance visualization for strategic decision making, IoT and real-time data analytics to make predictive insights for a given business problem. It also evaluate the correct use of machine learning algorisms using modern BI tools to solve complex real world problems.  

Unit Grading Scheme

7- point scale

Assessment Tasks

Assessment: BI Solution Design and Implementation

For this assignment, students will design and implement a comprehensive BI solution for a given business scenario including analysis, design, implementation and correct predictions for decision making.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Assessment: IoT Data Analytics and Predictive Insights

This assignment allows students to work with real-time IoT data, implementing stream analytics to process the data as it arrives. They will also utilize predictive analytics techniques to make informed predictions based on historical IoT data. By incorporating these practical aspects, students will gain hands-on experience in analysing IoT data, extracting valuable insights, and using predictive models to drive decision-making in real-world scenarios. They also provide and justify the correct use of security and governance of the developed solution. 

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Assessment: examination (written)

Based on several real-world problems, and employing technical and business intelligence skills taught in this unit, you will take a quiz/text covering the lectures from weeks 1-13

Academic Integrity

Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.

The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.

You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.

Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.

Requirements to Study

Costs

No extraordinary charges or costs are associated with the requirements for this unit.

Resources

Risk Assessment Statement

There are no out-of-the-ordinary risks associated with studying this unit