Outline

Overview

Data governance is a major imperative for organisations in effectively managing, using, protecting and leveraging their critical data assets. This unit introduces students to key concepts, processes, technologies and stakeholders related to the design and implementation of a data governance program. The unit takes an interdisciplinary and multi-level approach that examines standards, frameworks and methodologies for managing data quality, protecting critical and sensitive information, supporting business analytics and meeting compliance obligations. In examining different stages of the data lifecycle, students also learn about legal, professional and ethical responsibilities, policy implications, required skill sets and accountabilities.

Unit details and rules

Academic unit	Business Information Systems
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	INFS3010 or INFS3030
Assumed knowledge ?	INFS1000 or INFO1000 or INFO1003 or INFO1903
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Catherine Hardy, catherine.hardy@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Contribution	Personal Learning Objectives Presentation and responses to presentations by others.	5%	Week 03 Due date: 13 Mar 2026 at 23:59 Closing date: 23 Mar 2026	2 minutes presentation; 2 posts	AI allowed
Outcomes assessed:
Case studies	Individual Assignment One Written task	25%	Week 06 Due date: 02 Apr 2026 at 23:59 Closing date: 16 Apr 2026	1500 words	AI allowed
Outcomes assessed:
Case studies	Group Assignment Group Report Submission	30%	Week 12 Due date: 18 May 2026 at 23:59 Closing date: 18 May 2026	2000 words	AI allowed
Outcomes assessed:
Presentation	Group Assignment: Presentation Group Presentation	6%	Week 12 Due date: 18 May 2026 at 23:59 Closing date: 18 May 2026	-	AI allowed
Outcomes assessed:
Evaluation	Group Assignment: Individual Evaluation Individual Evaluation	4%	Week 12 Due date: 22 May 2026 at 23:59 Closing date: 22 May 2026	-	AI allowed
Outcomes assessed:
Case studies	Individual Assignment Two Written task	30%	Week 13 Due date: 29 May 2026 at 23:59 Closing date: 12 Jun 2026	1800 words	AI allowed
Outcomes assessed:
= group assignment ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

The assessments support the learning objectives of the unit.

Individual Assignment One: This assignment will require you to integrate information from lectures and tutorials to create a concise written report.

Individual Assignment Two: This assignment will require you to integrate information from lectures and tutorials to create a concise written report.

Group Assignment: This assignment consists of a presentation as well as other written and non-written elements.

Personal Learning Objectives (PLO): In week 3 you will present your PLO for this unit and respond to the PLO of your peers.

Submission links and more information about each assessment are available on Canvas.

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 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.

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: The Data Governance Imperative	Lecture (1.5 hr)
Week 01	Introduction: The Data Governance Imperative	Tutorial (1.5 hr)
Week 02	Data governance: definitions, principles and frameworks (Part 1)	Lecture (1.5 hr)
Week 02	Data governance: definitions, principles and frameworks (Part 1)	Tutorial (1.5 hr)
Week 03	Data governance: definitions, principles and frameworks (Part 2)	Lecture (1.5 hr)
Week 03	Data governance: definitions, principles and frameworks (Part 2)	Tutorial (1.5 hr)
Week 04	Directed data, surveillance and governance	Lecture (1.5 hr)
Week 04	Directed data, surveillance and governance	Tutorial (1.5 hr)
Week 05	Volunteered data, analytics and governance	Lecture (1.5 hr)
Week 05	Volunteered data, analytics and governance	Tutorial (1.5 hr)
Week 06	Self Directed Learning and Consultation	Lecture (1.5 hr)
Week 06	Self directed learning and consultation	Tutorial (1.5 hr)
Week 07	Automated data, platforms and governance	Lecture (1.5 hr)
Week 07	Automated data, platforms and governance	Tutorial (1.5 hr)
Week 08	Data ethics and data governance	Lecture (1.5 hr)
Week 08	Data ethics and data governance	Tutorial (1.5 hr)
Week 09	Data integrity and data governance	Lecture (1.5 hr)
Week 09	Data integrity and data governance	Tutorial (1.5 hr)
Week 10	Data security and data governance	Lecture (1.5 hr)
Week 10	Data security and data governance	Tutorial (1.5 hr)
Week 11	Self directed learning and consultation	Lecture (1.5 hr)
Week 11	Self directed learning and consultation	Tutorial (1.5 hr)
Week 12	Group Presentation and Evaluation	Lecture (1.5 hr)
Week 12	Group Presentation and Evaluation	Tutorial (1.5 hr)
Week 13	Data Governance Evaluation and Application	Lecture (1.5 hr)
Week 13	Data Governance Evaluation and Application	Tutorial (1.5 hr)

Attendance and class requirements

All lectures are recorded and will be available on Canvas together with weekly class requirements.

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. describe key concepts and principles of data governance and explain their importance in business information systems
LO2. demonstrate knowledge of the relationship of data governance with security, privacy, ethics and regulatory functions
LO3. identify key stakeholders involved in governing data in different and changing institutional contexts and technology platforms
LO4. demonstrate knowledge of key data governance standards and frameworks and practise their application through class activities and assigned tasks
LO5. conduct research to develop and support arguments about key issues, challenges and trends associated with data governance in business
LO6. communicate in oral and written form your knowledge, thoughts and findings through class discussions, group work and individual assignments
LO7. provide constructive feedback to your peers on their written work, and address issues identified by your instructor and peers when reflecting and revising your own written work.

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 data governance case studies, revised assessments, and updated learning materials.

Additional information

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

Early feedback task

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

INFS2050: Data Governance and Technology Assurance

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

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