IAB353 Data Analytics for Enterprise Systems
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | IAB353 |
|---|---|
| Prerequisite(s): | ((IFB104 or ITD104 or EGB103) and (IFB105 or IFB130 or ITD105)) or IAB251 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,192 |
| Domestic tuition unit fee | $4,704 |
| International unit fee | $5,640 |
Unit Outline: Semester 2 2026, Gardens Point, Internal
| Unit code: | IAB353 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | ((IFB104 or ITD104 or EGB103) and (IFB105 or IFB130 or ITD105)) or IAB251 |
| Coordinator: | Darshika Koggalahewa | darshika.koggalahewa@qut.edu.au |
Overview
This unit provides knowledge and skills for supporting Business Intelligence (BI) using enterprise systems. BI is a technology-driven process for analysing data and delivering actionable outcomes as part of planning and decision-making tasks undertaken by executives, managers, and workers. It involves data science and machine learning techniques and tools applied to key aspects of businesses including products, services, customers and resources. You will be exposed to the planning, modelling, reporting, and prediction structures underpinning business intelligence. To support this, you will learn, Data preparation, analysis and modelling, predictions, and visualization. In addition, you will be exposed to advanced data analytics capabilities including, real-time analytics and Internet of Things (IoT) analytics. This will be applied through a comprehensive framework that supports data Ops, data security, and governance. The unit provides a rich exposure to real-world BI platforms.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain the concepts and principles of business intelligence (BI), Data Analytics and their strategic importance in modern enterprises.
- Apply advanced techniques and skills in data analytics, visualization, and prediction relevant for enterprise planning and operations.
- Apply BI techniques using tools and technologies available through enterprise BI platforms to solve real-world business problems.
- Justify the data governance, ethical and legal considerations for enterprises using Data Analytics.
- Communicate information through written, oral, and visual formats, tailoring to both specialist and general audiences
- Collaborate with others in a team environment to achieve project deliverables.
Content
The first module introduces you to the fundamentals of Business Intelligence. You will learn about data warehousing and data modelling, data cleaning and integration, data visualization and predictive analytics. A real-world enterprise business intelligence platform and algorithms will be explored to apply BI concepts to enterprise business scenarios.
The second module introduces you to the advance concepts of Business Intelligence using cutting-edge industry technologies and algorithms. You’ll learn the fundamentals of, advance analytics, self-service BI, streaming, and IoT analytics. The focus will be to apply BI concepts to address complex, real-time business problems.
The third module introduces you to the operations, security, and data governance of Enterprise Systems. You’ll learn the fundamentals of, Data Ops, ethical and legal considerations, and ethical decision-making in BI for Enterprise Systems.
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You can expect to spend 10-15 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. You will 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
You can obtain feedback on your 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. You 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 IoT and real-time data analytics to make predictive insights for a given business problem.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: BI Solution Design and Implementation
For this assignment, you will design and implement a comprehensive BI solution for a given business scenario.
This assignment is eligible for the 48-hour late submission period and assignment extension
Assessment: Enterprise Data Analytics and Predictive Insights
This assignment allows you to work with real world enterprise data, implementing data analytics to process the enterprise data. You will also utilize predictive analytics techniques to make informed predictions based on historical data in organizations. By incorporating these practical aspects, you will gain hands-on experience in analysing enterprise data, extracting valuable insights, and using predictive models to drive decision-making in real-world scenarios.
This assignment is eligible for the 48-hour late submission period and assignment extension
Assessment: Online Examination
The final examination presents students with a written, real-world enterprise data scenario. Students are required to demonstrate their understanding of data analytics concepts by interpreting data-related problems, recommending suitable analytical approaches, and justifying data-driven decisions. The exam emphasises conceptual reasoning, critical thinking, and the application of analytical frameworks, reflecting the type of strategic data interpretation and communication expected in professional enterprise environments.
An open book Examination which covers 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.
Resources
There is no required textbook for this unit. However, this unit may where appropriate, make use of the selected chapters from textbooks, journals, and magazines. You are encouraged to obtain a copy of these materials from the library. Where possible, materials will be made available online through QUT Readings. All other learning materials will be provided by the unit Canvas site.
Risk Assessment Statement
There are no out-of-the-ordinary risks associated with studying this unit
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: BI Solution Design and Implementation , Online Examination - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights, Online Examination - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights - Apply awareness of the relevant social and ethical frameworks, including Australian indigenous perspectives, concerning the collection, storage and use of data in informing decision-making.
Relates to: Enterprise Data Analytics and Predictive Insights, Online Examination
IN01 Bachelor of Information Technology
- Demonstrate a broad theoretical and technical knowledge of well-established and emerging IT disciplines, with in-depth knowledge in at least one specialist area aligned to multiple ICT professional roles.
Relates to: ULO1, BI Solution Design and Implementation , Online Examination - Integrate and apply technical knowledge and skills to analyse, design, build, operate and maintain sustainable, secure IT systems using industry-standard tools, technologies, platforms, and processes.
Relates to: ULO2, ULO3, BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights - Demonstrate an understanding of the role of IT in enabling business outcomes and how business realities shape IT decisions.
Relates to: ULO2, ULO3, BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights - Demonstrate initiative, autonomy and personal responsibility for continuous learning, working both independently and collaboratively within multi-disciplinary teams, employing state-of-the-art IT project management methodologies to plan and manage time, resources, and risk.
Relates to: BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights - Communicate professionally and effectively in written, verbal and visual formats to a diverse range of stakeholders, considering the audience and explaining complex ideas in a simple and understandable manner in a range of IT-related contexts.
Relates to: ULO5, BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights - Assess the risks and potential of artificial intelligence (and other disruptive emerging technologies) within an organisation and leverage AI knowledge and skills to solve IT challenges, improve productivity and add value.
Relates to: ULO4, Enterprise Data Analytics and Predictive Insights, Online Examination
Unit Outline: Semester 2 2026, Online
| Unit code: | IAB353 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | ((IFB104 or ITD104 or EGB103) and (IFB105 or IFB130 or ITD105)) or IAB251 |
Overview
This unit provides knowledge and skills for supporting Business Intelligence (BI) using enterprise systems. BI is a technology-driven process for analysing data and delivering actionable outcomes as part of planning and decision-making tasks undertaken by executives, managers, and workers. It involves data science and machine learning techniques and tools applied to key aspects of businesses including products, services, customers and resources. You will be exposed to the planning, modelling, reporting, and prediction structures underpinning business intelligence. To support this, you will learn, Data preparation, analysis and modelling, predictions, and visualization. In addition, you will be exposed to advanced data analytics capabilities including, real-time analytics and Internet of Things (IoT) analytics. This will be applied through a comprehensive framework that supports data Ops, data security, and governance. The unit provides a rich exposure to real-world BI platforms.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain the concepts and principles of business intelligence (BI), Data Analytics and their strategic importance in modern enterprises.
- Apply advanced techniques and skills in data analytics, visualization, and prediction relevant for enterprise planning and operations.
- Apply BI techniques using tools and technologies available through enterprise BI platforms to solve real-world business problems.
- Justify the data governance, ethical and legal considerations for enterprises using Data Analytics.
- Communicate information through written, oral, and visual formats, tailoring to both specialist and general audiences
- Collaborate with others in a team environment to achieve project deliverables.
Content
The first module introduces you to the fundamentals of Business Intelligence. You will learn about data warehousing and data modelling, data cleaning and integration, data visualization and predictive analytics. A real-world enterprise business intelligence platform and algorithms will be explored to apply BI concepts to enterprise business scenarios.
The second module introduces you to the advance concepts of Business Intelligence using cutting-edge industry technologies and algorithms. You’ll learn the fundamentals of, advance analytics, self-service BI, streaming, and IoT analytics. The focus will be to apply BI concepts to address complex, real-time business problems.
The third module introduces you to the operations, security, and data governance of Enterprise Systems. You’ll learn the fundamentals of, Data Ops, ethical and legal considerations, and ethical decision-making in BI for Enterprise Systems.
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You can expect to spend 10-15 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. You will 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
You can obtain feedback on your 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. You 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 IoT and real-time data analytics to make predictive insights for a given business problem.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: BI Solution Design and Implementation
For this assignment, you will design and implement a comprehensive BI solution for a given business scenario.
This assignment is eligible for the 48-hour late submission period and assignment extension
Assessment: Enterprise Data Analytics and Predictive Insights
This assignment allows you to work with real world enterprise data, implementing data analytics to process the enterprise data. You will also utilize predictive analytics techniques to make informed predictions based on historical data in organizations. By incorporating these practical aspects, you will gain hands-on experience in analysing enterprise data, extracting valuable insights, and using predictive models to drive decision-making in real-world scenarios.
This assignment is eligible for the 48-hour late submission period and assignment extension
Assessment: Online Examination
The final examination presents students with a written, real-world enterprise data scenario. Students are required to demonstrate their understanding of data analytics concepts by interpreting data-related problems, recommending suitable analytical approaches, and justifying data-driven decisions. The exam emphasises conceptual reasoning, critical thinking, and the application of analytical frameworks, reflecting the type of strategic data interpretation and communication expected in professional enterprise environments.
An open book Examination which covers 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.
Resources
There is no required textbook for this unit. However, this unit may where appropriate, make use of the selected chapters from textbooks, journals, and magazines. You are encouraged to obtain a copy of these materials from the library. Where possible, materials will be made available online through QUT Readings. All other learning materials will be provided by the unit Canvas site.
Risk Assessment Statement
There are no out-of-the-ordinary risks associated with studying this unit
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.DS01 Bachelor of Data Science
- Demonstrate a broad and coherent knowledge of the principles, concepts and techniques of the data science discipline, with depth of knowledge in at least one area developed through a major.
Relates to: BI Solution Design and Implementation , Online Examination - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights, Online Examination - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights - Apply awareness of the relevant social and ethical frameworks, including Australian indigenous perspectives, concerning the collection, storage and use of data in informing decision-making.
Relates to: Enterprise Data Analytics and Predictive Insights, Online Examination
IN01 Bachelor of Information Technology
- Demonstrate a broad theoretical and technical knowledge of well-established and emerging IT disciplines, with in-depth knowledge in at least one specialist area aligned to multiple ICT professional roles.
Relates to: ULO1, BI Solution Design and Implementation , Online Examination - Integrate and apply technical knowledge and skills to analyse, design, build, operate and maintain sustainable, secure IT systems using industry-standard tools, technologies, platforms, and processes.
Relates to: ULO2, ULO3, BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights - Demonstrate an understanding of the role of IT in enabling business outcomes and how business realities shape IT decisions.
Relates to: ULO2, ULO3, BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights - Demonstrate initiative, autonomy and personal responsibility for continuous learning, working both independently and collaboratively within multi-disciplinary teams, employing state-of-the-art IT project management methodologies to plan and manage time, resources, and risk.
Relates to: BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights - Communicate professionally and effectively in written, verbal and visual formats to a diverse range of stakeholders, considering the audience and explaining complex ideas in a simple and understandable manner in a range of IT-related contexts.
Relates to: ULO5, BI Solution Design and Implementation , Enterprise Data Analytics and Predictive Insights - Assess the risks and potential of artificial intelligence (and other disruptive emerging technologies) within an organisation and leverage AI knowledge and skills to solve IT challenges, improve productivity and add value.
Relates to: ULO4, Enterprise Data Analytics and Predictive Insights, Online Examination