IAB410 Enterprise Data & AI Governance
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: | IAB410 |
|---|---|
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
Unit Outline: Semester 1 2027, Gardens Point, Internal
| Unit code: | IAB410 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | Completion of 96 cps |
Overview
In today's rapidly evolving digital landscape with new data and artificial intelligence (AI) opportunities, effective and responsible governance is essential for organisational success, ethical integrity, and regulatory compliance. The Enterprise Data and AI Governance unit equips you with the strategic insight and practical expertise to navigate the complexities of governing digital assets confidently. You will do so by leveraging governance frameworks to create value for an organisation and its stakeholders, supported by a practical blend of theory and real-world insights. You will master good practices in corporate, data, and AI governance, ensuring alignment between organisational goals and technological capabilities. This unit uniquely equips you with the comprehensive skills needed to advance enterprises in a data-driven, AI-enabled world.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain and apply key concepts, theories, frameworks and practices for governing data and AI in organisations.
- Critically analyse real-world data and AI opportunities and their requirements for enterprise governance.
- Develop data and AI governance strategies and recommendations in an organisational context that are well-founded and justified.
- Effectively communicate governance strategies and recommendations to diverse stakeholders in enterprise-level decision-making contexts.
- Work effectively by leveraging AI tools and cooperating with team members in a productive and responsible way.
Content
The unit is structured into three coherent modules designed to progressively build your expertise for the enterprise governance of data and AI.
Module 1: Enterprise governance of information & technology introduces you to foundational principles of governance, providing an integrated perspective across corporate, IT, AI and data governance. The module emphasises governance as a strategic function essential to maximising organisational value, effectively managing risks, and maintaining ethical integrity and regulatory compliance in a data-driven, AI-enabled business environment. Particular attention is given to ethical, regulatory, and policy considerations within the Australian context.
Module 2: Enterprise governance of Artificial Intelligence (AI) introduces you to the AI lifecycle and ecosystem as a foundation for understanding the opportunities and challenges facing organisations leveraging AI in fast-evolving technological landscape. You will become familiar with leading AI governance frameworks and practices, systematically guiding strategic and operational decisions regarding AI development and use in organisations. Additionally, you will delve more deeply into specialised topics, including the consumerisation of Generative AI and its implications for governance within an organisational context.
Module 3: Enterprise governance of data introduces you to the role of (big) data and analytics in organisations, emphasising data as a strategic asset. You will become familiar with leading data governance frameworks and practices for evaluating, directing and monitoring the handling and use of data in organisations. This will include more high-level, principles-based governance approaches and their implementation through more practical data management and techniques throughout the data lifecycle. Additionally, you will explore specialised topics in greater depth, including understanding the value of data and using data to improve decision-making.
Learning Approaches
In this unit, you will learn by engaging in the following learning approaches and activities: lectures and tutorials.
This unit will use lectures to provide you with the big picture and a detailed understanding of concepts, theories, frameworks and practices related to the enterprise governance of data and AI. You are expected to study the lecture content in preparation for the tutorials.
The tutorials will review discuss with you the lecture content, illustrate its applicability through real-world case studies and examples, and apply it through exercises. The tutorials will also support you with your assignments.
Peer-to-peer learning will play a major role in the tutorials and you are encouraged to work together, building on each other’s strengths and assisting each other to learn, valuing the knowledge and experience you bring. As these tutorials will be ‘hands-on,’ you are required to participate actively in these workshops.
Assessment tasks 1 and 2 (Governance Requirements, Strategy and Recommendations) will use real-world examples and authentic assessment to learn how organisations can govern data & AI in a contemporary enterprise setting. You are expected to study the lecture content and participate in the workshops in preparation for these assessment tasks.
You should expect to spend, on average, 10 - 15 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as in independent study to consolidate your learning. You must be able to manage your time and prioritise activities to complete the required unit activities.
Feedback on Learning and Assessment
You will have a range of opportunities to receive feedback on your learning and progress in this unit, including formative in-class individual or whole-of-class feedback on your work by tutors and peers as well as individual feedback on assessment tasks via a rubric and written feedback. Individual consultations with your tutor can be arranged at a mutually convenient time.
Assessment
Overview
You will have three opportunities in this unit to demonstrate your ability to achieve the unit learning outcomes. Early in the semester you will complete a low-stakes problem solving task and present your results. Mid-way through the semester you will collaborate on a more complex data and AI problem, where you will produce a report on your analysis and justified recommendations. At the end of the semester your knowledge of data and AI governance will be assessed through an invigilated examination. Feedback on your progress in the form of rubrics and written feedback will be provided progressively throughout the semester.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Data & AI Opportunity and Governance Requirements
Critically analyse a data and AI opportunity for an organisation and articulate the governance requirements applying concepts, theories and frameworks introduced in this unit.
Assessment: Data & AI Governance Strategy and Recommendation
Provide a governance strategy and recommendation for a data and AI opportunity including a full justification for recommendations. This will include being explicit about the effective and responsible use of Generative AI. This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
This invigilated exam will assess your knowledge of data and AI governance developed across the semester.
This examination will require attendance at a local testing centre. If you are enrolled as internal or on-campus student, the local testing centre will be on QUT campus. For students enrolled as online, QUT Examinations will provide local testing centre information.
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
There are no out of the ordinary costs associated with the study of this unit.
Resources
All learning materials will be provided via the Canvas site.
You are expected to have access to basic hardware and software as used in professional practice to produce reports and presentations (Microsoft Word and Microsoft PowerPoint, or compatible software). You also need access to the library and Internet for performing research.
Risk Assessment Statement
There are no extraordinary risks associated with the classroom activities in 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: Data & AI Opportunity and Governance Requirements, Data & AI Governance Strategy and Recommendation, Examination (invigilated) - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Data & AI Opportunity and Governance Requirements, Data & AI Governance Strategy and Recommendation, Examination (invigilated) - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Data & AI Opportunity and Governance Requirements, Data & AI Governance Strategy and Recommendation - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Data & AI Governance Strategy and Recommendation - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Data & AI Opportunity and Governance Requirements, Data & AI Governance Strategy and Recommendation - Develop your learning, professional capabilities and skills, and capture it through a curated portfolio of work.
Relates to: Data & AI Governance Strategy and Recommendation
Unit Outline: Semester 1 2027, Online
| Unit code: | IAB410 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | Completion of 96 cps |
Overview
In today's rapidly evolving digital landscape with new data and artificial intelligence (AI) opportunities, effective and responsible governance is essential for organisational success, ethical integrity, and regulatory compliance. The Enterprise Data and AI Governance unit equips you with the strategic insight and practical expertise to navigate the complexities of governing digital assets confidently. You will do so by leveraging governance frameworks to create value for an organisation and its stakeholders, supported by a practical blend of theory and real-world insights. You will master good practices in corporate, data, and AI governance, ensuring alignment between organisational goals and technological capabilities. This unit uniquely equips you with the comprehensive skills needed to advance enterprises in a data-driven, AI-enabled world.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain and apply key concepts, theories, frameworks and practices for governing data and AI in organisations.
- Critically analyse real-world data and AI opportunities and their requirements for enterprise governance.
- Develop data and AI governance strategies and recommendations in an organisational context that are well-founded and justified.
- Effectively communicate governance strategies and recommendations to diverse stakeholders in enterprise-level decision-making contexts.
- Work effectively by leveraging AI tools and cooperating with team members in a productive and responsible way.
Content
The unit is structured into three coherent modules designed to progressively build your expertise for the enterprise governance of data and AI.
Module 1: Enterprise governance of information & technology introduces you to foundational principles of governance, providing an integrated perspective across corporate, IT, AI and data governance. The module emphasises governance as a strategic function essential to maximising organisational value, effectively managing risks, and maintaining ethical integrity and regulatory compliance in a data-driven, AI-enabled business environment. Particular attention is given to ethical, regulatory, and policy considerations within the Australian context.
Module 2: Enterprise governance of Artificial Intelligence (AI) introduces you to the AI lifecycle and ecosystem as a foundation for understanding the opportunities and challenges facing organisations leveraging AI in fast-evolving technological landscape. You will become familiar with leading AI governance frameworks and practices, systematically guiding strategic and operational decisions regarding AI development and use in organisations. Additionally, you will delve more deeply into specialised topics, including the consumerisation of Generative AI and its implications for governance within an organisational context.
Module 3: Enterprise governance of data introduces you to the role of (big) data and analytics in organisations, emphasising data as a strategic asset. You will become familiar with leading data governance frameworks and practices for evaluating, directing and monitoring the handling and use of data in organisations. This will include more high-level, principles-based governance approaches and their implementation through more practical data management and techniques throughout the data lifecycle. Additionally, you will explore specialised topics in greater depth, including understanding the value of data and using data to improve decision-making.
Learning Approaches
In this unit, you will learn by engaging in the following learning approaches and activities: lectures and tutorials.
This unit will use lectures to provide you with the big picture and a detailed understanding of concepts, theories, frameworks and practices related to the enterprise governance of data and AI. You are expected to study the lecture content in preparation for the tutorials.
The tutorials will review discuss with you the lecture content, illustrate its applicability through real-world case studies and examples, and apply it through exercises. The tutorials will also support you with your assignments.
Peer-to-peer learning will play a major role in the tutorials and you are encouraged to work together, building on each other’s strengths and assisting each other to learn, valuing the knowledge and experience you bring. As these tutorials will be ‘hands-on,’ you are required to participate actively in these workshops.
Assessment tasks 1 and 2 (Governance Requirements, Strategy and Recommendations) will use real-world examples and authentic assessment to learn how organisations can govern data & AI in a contemporary enterprise setting. You are expected to study the lecture content and participate in the workshops in preparation for these assessment tasks.
You should expect to spend, on average, 10 - 15 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as in independent study to consolidate your learning. You must be able to manage your time and prioritise activities to complete the required unit activities.
Feedback on Learning and Assessment
You will have a range of opportunities to receive feedback on your learning and progress in this unit, including formative in-class individual or whole-of-class feedback on your work by tutors and peers as well as individual feedback on assessment tasks via a rubric and written feedback. Individual consultations with your tutor can be arranged at a mutually convenient time.
Assessment
Overview
You will have three opportunities in this unit to demonstrate your ability to achieve the unit learning outcomes. Early in the semester you will complete a low-stakes problem solving task and present your results. Mid-way through the semester you will collaborate on a more complex data and AI problem, where you will produce a report on your analysis and justified recommendations. At the end of the semester your knowledge of data and AI governance will be assessed through an invigilated examination. Feedback on your progress in the form of rubrics and written feedback will be provided progressively throughout the semester.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Data & AI Opportunity and Governance Requirements
Critically analyse a data and AI opportunity for an organisation and articulate the governance requirements applying concepts, theories and frameworks introduced in this unit.
Assessment: Data & AI Governance Strategy and Recommendation
Provide a governance strategy and recommendation for a data and AI opportunity including a full justification for recommendations. This will include being explicit about the effective and responsible use of Generative AI. This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
This invigilated exam will assess your knowledge of data and AI governance developed across the semester.
This examination will require attendance at a local testing centre. If you are enrolled as internal or on-campus student, the local testing centre will be on QUT campus. For students enrolled as online, QUT Examinations will provide local testing centre information.
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
There are no out of the ordinary costs associated with the study of this unit.
Resources
All learning materials will be provided via the Canvas site.
You are expected to have access to basic hardware and software as used in professional practice to produce reports and presentations (Microsoft Word and Microsoft PowerPoint, or compatible software). You also need access to the library and Internet for performing research.
Risk Assessment Statement
There are no extraordinary risks associated with the classroom activities in 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: Data & AI Opportunity and Governance Requirements, Data & AI Governance Strategy and Recommendation, Examination (invigilated) - Use appropriate statistical, computational, modelling, data management, programming and generative artificial intelligence techniques to develop solutions for deriving insights from data.
Relates to: Data & AI Opportunity and Governance Requirements, Data & AI Governance Strategy and Recommendation, Examination (invigilated) - Demonstrate critical thinking and problem-solving skills, as well as adaptivity in applying learned techniques in new and unfamiliar contexts.
Relates to: Data & AI Opportunity and Governance Requirements, Data & AI Governance Strategy and Recommendation - Work effectively both independently and collaboratively in diverse and interdisciplinary teams.
Relates to: Data & AI Governance Strategy and Recommendation - Communicate effectively in a variety of modes, to expert and non-expert audiences, including in a professional context.
Relates to: Data & AI Opportunity and Governance Requirements, Data & AI Governance Strategy and Recommendation - Develop your learning, professional capabilities and skills, and capture it through a curated portfolio of work.
Relates to: Data & AI Governance Strategy and Recommendation