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Unit of study_

INFS6018: Managing Business Intelligence

Semester 1, 2020 [Normal evening] - Camperdown/Darlington, Sydney

Business Intelligence (BI), or Business Analytics, 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 management skills. Taking a business rather than technology perspective, this unit covers the enterprise BI ecosystem in the context of strategic and operational BI. Topics include assessment and management of organisational data quality, multidimensional data modelling and integration, management of structured and unstructured data (including those created by social media), business aspects of data warehousing, innovation through advanced analytics, BI driven performance management, business process intelligence, active enterprise intelligence, and management of complex BI projects. The course offers hands-on experience in using a commercial BI platform, combined with in-depth analytical skills, will enable students completing the unit to help any organization (regardless of its size and industry domain) to derive more intelligence from its data and compete on analytics. This unit does not require programming experience; it is suitable for both current and aspiring BI practitioners as well as general business practitioners from any functional area interested to learn how to start and lead BI-related initiatives.

Unit details and rules

Unit code INFS6018
Academic unit Business Information Systems
Credit points 6
Assumed knowledge

Understanding the major functions of a business and how those business functions interact 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. (ii) Desirable Experience as a member of a project team.

Available to study abroad and exchange students


Teaching staff

Coordinator Daniel Gozman,
Type Description Weight Due Length
In-semester test Mid-semester exam
Online exam
35% Mid-semester exam period 1.5 hours
Outcomes assessed: LO1 LO3 LO2
Small continuous assessment Quizzes
35% Multiple weeks n/a
Outcomes assessed: LO1 LO2
Assignment group assignment Project report
Written report
30% Week 13
Due date: 05 Jun 2020 at 17:00

Closing date: 12 Jun 2020
3000 words
Outcomes assessed: LO2 LO3 LO4 LO5
group assignment = group assignment ?

Assessment summary

  • Mid-semester exam: This is an online exam designed to test the fundamental knowledge acquired in prior weeks.
  • Project report: This project requires students to design small-scale data analysis and visualisation application for a real-life organisation, and submit a project report. The report (one per group) must be submitted through Canvas (TurnItIn).
  • Quizzes: The online quizzes are designed to help students explore and understand basic concepts prior to attending the class. Seven weekly or biweekly timed online quizzes will be made available to the students to complete anytime during a 24 hour period. The duration of the quizzes will depend on the topics for the week and coverage and will be announced on Canvas.

Detailed information for each assessment can be found 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


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. 


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.


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.


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. 


0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

For more information see guide to grades.

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 Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
Week 01 Introduction and core concepts Seminar (3 hr)  
Week 02 Decision making and analytics Seminar (3 hr)  
Week 03 Data quality for business intelligence (BI) Seminar (3 hr)  
Week 04 Architectures for business intelligence (data warehousing) Seminar (3 hr)  
Week 05 Mid-semester exam preparation Seminar (3 hr)  
Week 06 Mid-semester exam Seminar (3 hr)  
Week 07 Dimensional modelling and OLAP Seminar (3 hr)  
Week 08 Ethics for business intelligence Seminar (3 hr)  
Week 09 Personas Seminar (3 hr)  
Week 10 Developing the business case for business intelligence Seminar (3 hr)  
Week 11 Emergent technology for business intelligence Seminar (3 hr)  
Week 12 Guest lecture (TBC) Seminar (3 hr)  
Week 13 Unit recap 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 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.

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

  • LO1. apply the fundamental concepts and practice of business intelligence to analyse data-related challenges in any organisational setting (large or small)
  • LO2. identify and analyse multi-disciplinary issues, challenges and solutions in managing business intelligence/business analytics systems from the business rather than technical perspective
  • LO3. examine critically the organisational, cross-organisational and societal issues involved in implementing various types of BI systems and their emergent use including the contexts of open data, big data, unstructured and social media data
  • LO4. design and implement a small-scale data analysis and visualisation project in a real-life organisational setting, recommend and evaluate creative solutions and present the outcomes orally and in writing
  • LO5. demonstrate an awareness of the current and emerging BI-related trends in business, government and society, including BI-related ethical issues.

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

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

The assessment structure has been amended by replacing group presentation and reflective essay with the online quizzes. This was done to create an ongoing feedback loop to inform the student learning process.


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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