Outline

Overview

Despite the widely acknowledged importance of data as a strategic resource, organisations continue to struggle with appropriately managing their data assets. This unit highlights the crucial importance of data governance in the administration, protection, and optimisation of data. It provides an exploration into the core principles of data governance, discusses the influence of emerging technologies, such as artificial intelligence, and examines the broader societal and ethical implications surrounding data. Through interactive case studies and hands-on assessments, students learn about the complex issues surrounding data governance and formulate and persuasively communicate practical data governance approaches to empower organisations to navigate their data landscape.

Unit details and rules

Academic unit	Business Information Systems
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Mylene Struijk, mylene.struijk@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written work	Individual Report A Please see detailed Individual Assignment requirements in Canvas.	25%	Week 06 Due date: 11 Sep 2026 at 23:59 Closing date: 18 Sep 2026	1500 word report.	AI allowed
Outcomes assessed:
Case studies	Individual report B Part I Report B part I (750 words); marked for completeness. Failure to submit will affect the mark for Report B part II.	0%	Week 08 Due date: 25 Sep 2026 at 23:59 Closing date: 02 Oct 2026	750-word report	AI allowed
Outcomes assessed:
Case studies	Individual Report B Part II Full report after a mandatory in-class assignment in week 9. Failure to attend in class in week 9 will prohibit the student from being able to submit this report. Further assignment requirements in Canvas.	30%	Week 10 Due date: 16 Oct 2026 at 23:59 Closing date: 23 Oct 2026	1500 words report + attendance w9.	AI allowed
Outcomes assessed:
Presentation	Group project pitch Pitch of data strategy	5%	Week 13 Due date: 03 Nov 2026 at 17:00 Closing date: 03 Nov 2026	10 minute pitch	AI allowed
Outcomes assessed:
Written work	Group project report Full report with formative submissions throughout the semester. See Canvas for full details.	40%	Week 13 Due date: 06 Nov 2026 at 23:59 Closing date: 13 Nov 2026	4000 words	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Group project report and pitch: The group project is an applied assignment that requires the team's development of a data strategy.

Individual Assignment A: The report will assess the implications of generative AI for data governance.

Individual Assignment B: The report will explore decision-making with data in complex organizational settings.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Applies according to the University policies

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction and overview	Seminar (3 hr)
Week 02	Introduction to data governance	Seminar (3 hr)
Week 03	Strategies, programs, and frameworks	Seminar (3 hr)
Week 04	Data quality	Seminar (3 hr)
Week 05	The impact of emerging technologies	Seminar (3 hr)
Week 06	Leveraging digital data for decision-making	Seminar (3 hr)
Week 07	Societal and ethical considerations	Seminar (3 hr)
Week 08	Securing and protecting data	Seminar (3 hr)
Week 09	Collective data governance	Seminar (3 hr)
Week 10	Power and politics	Seminar (3 hr)
Week 11	Managing and sustaining change	Seminar (3 hr)
Week 12	Guest lecture, review and synthesis	Seminar (3 hr)
Week 13	Group pitches	Seminar (3 hr)

Attendance and class requirements

Attendance in class is mandatory in week 9 and week 13. Failure to participate in this weeks affects students' marks for individual report B and group pitch.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Details about prescribed readings for this unit are available on Canvas.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Discuss and apply the foundational principles and concepts of data governance, including stewardship, data quality, security, metadata, and lifecycle management
LO2. Reflect critically on the implications of emerging technologies, such as artificial intelligence, blockchain, and internet of things, on data governance practices
LO3. Analyze case studies of data governance practices in various (inter)organizational contexts to identify stakeholders, key success factors, challenges, and opportunities
LO4. Evaluate the broader societal and ethical implications surrounding data governance, including privacy and transparency, and propose and discuss the effectiveness of ethical guidelines
LO5. Assess how organizations can leverage digital data and become more data-driven to enhance decision-making processes
LO6. Develop and present a comprehensive data governance strategy tailored to a specific case organization, integrating lecture materials, and ensuring alignment with the organizational context

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This unit is continually updated in response to student feedback, including a greater emphasis on cutting-edge technologies, revised assessments, and updated learning materials.

Additional information

Business School Policies

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

INFS6077: Digital, Data and Decisions

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links