Outline

Overview

Business analytics and the ability to interpret and respond to related outputs, is a major source of competitive advantage in the information age and is therefore a leading business priority globally. In recent times, this field has evolved from a technology topic to a management priority, creating an unprecedented demand for new competencies in managing with data. Taking a business rather than technology perspective, this unit covers the enterprise ecosystem in the context of strategic and operational analytics and decision making. Topics include innovation through advanced analytics, data driven performance management, strategic business improvement and management of complex BI projects. The unit offers hands-on experience in using a commercial platform, combined with in-depth analytical skills, and enables students completing the unit to help any organization to derive more value from data and information and compete on analytics.

Unit details and rules

Academic unit	Business Information Systems
Credit points	6
Prerequisites ?	None
Corequisites ?	INFS5002 or COMP5206 or QBUS5001
Prohibitions ?	None
Assumed knowledge ?	Understanding the major functions of a business and how those business functions interact Semester 1 internally and externally so the company can be competitive in a changing market. How information systems can be used and managed in a business. How to critically analyse a business and determine its options for transformation. Desirable Experience as a member of a project team
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Lubna Alam, lubna.alam@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
Portfolio or journal	Reflective Essay Written essay on core learnings	25%	Multiple weeks Due date: 31 May 2026 at 23:59 Closing date: 10 Jun 2026	2500 words	AI allowed
Outcomes assessed:
Written test	Mid-semester test Mid-semester test will assess core learnings from weeks 1-5	35%	Week 07 Due date: 19 Apr 2026 at 15:10	2 hours	AI prohibited
Outcomes assessed:
Data analysis	Project report Written report	30%	Week 12 Due date: 18 May 2026 at 23:59 Closing date: 28 May 2026	4000 words	AI allowed
Outcomes assessed:
Presentation	Group project presentation Group presentation of written report and related analysis	10%	Week 13	Poster + 5 min Presentation + 15 min Q&A	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Mid-semester test to be held in Week 7 on core concepts delivered in weeks 1-5.

Group project and report: In groups, students will use Tableau to explore a real-world data set and consider the implications for distinct personas. Students will prepare a report outlining this analysis. Students will be orally examined in groups on the contents of this report.

Reflective essay: Students will utilise a reflective framework to critically evaluate managerial practices

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.

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:

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	Foundational and historical concepts	Seminar (3 hr)
Week 02	Decision making for managers	Seminar (3 hr)
Week 03	Data quality	Seminar (3 hr)
Week 04	Strategies & business models for valuecreation	Seminar (3 hr)
Week 05	Strategic alignment and IT governance	Seminar (3 hr)
Week 06	Exam preparation	Seminar (3 hr)
Week 07	Mid-semester Test	Seminar (3 hr)
Week 08	Analytics, people and culture	Seminar (3 hr)
Week 09	Ethics for business intelligence	Seminar (3 hr)
Week 10	Managing the change process and stakeholders	Seminar (3 hr)
Week 11	Guest Lecture	Seminar (3 hr)
Week 12	Managing emerging technologies: Blockchain and AI	Seminar (3 hr)
Week 13	Managing the cloud	Seminar (3 hr)

Attendance and class requirements

Lecture recordings: All lectures and seminars are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

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.

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. Apply the fundamental concepts and practice of managing through data challenges in differentiated organisational settings.
LO2. Identify and analyse issues, challenges and solutions in managing through data/business analytics systems from the business rather thantechnical perspective.
LO3. Critically examine the intra and inter organisational and societal issues involved in implementing various types of analytical systems.
LO4. Design and implement a small-scale data analysis project in an organisational setting.
LO5. Evaluate different analytical outputs and their ethical impacts.
LO6. Apply advanced principles and techniques related to strategy, organizational change and information governance to help organisations effectively maximise value from their data, information assets and technology investments.

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.

Following learnings from last year's delivery I have changed the teaching model to reflect ways inw which we can run this course more efficnelty and effectively with less risk exposure if staff are absent. Consequently, I have altered the assessment to reflect these changes.

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

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

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

INFS6018: Managing with Information and Data

Semester 1, 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

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