Course coordinator
Associate Professor Ida Asadi Someh
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Postgraduate Coursework
- Location
- External
- Attendance mode
- Online
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
Data-driven innovation is the key to transforming economies and society. Building on a foundation of business analytics concepts, this course introduces the process of innovation and the elements of innovation strategy. Students will develop knowledge of business model innovation, skills in developing and executing data-driven innovation strategy, and understanding of related organisational factors.
Monetising Business Data is a non-technical course offered as part of the Master of Business Analytics program. It uses organisational and management concepts and techniques to delineate how firms create strategic value from their digital data assets. The course promotes a framework (called I-W-S - short-form for improve, wrap and sell) for value creation from data with three key components: 1) improving processes with data, 2) wrapping products and services with analytics features, and 3) selling information solutions. Students are them introduced to data monetisation capabilities, data-driven transformation, governance of data, and forming a strategic plan for data-driven value creation.
The course is divided into three modules:
- Introduction to I-W-S framework
- Data monetisation capabilities and culture
- Data governance and strategy
This course offers a fully online student experience that engages students through interactive content built into a learning platform. The content engagement is further enhanced with live sessions, and discussion and debates on a social platform. Every module will have live case study analysis sessions, for which students need to prepare their case analysis individually and engage in critical discussions with their peers. All the live sessions will be recorded and provided to students to enable flexible learning.
The focus of this course is on presenting students with the opportunities and authentic assessments that will help them translate their knowledge of business analytics into real-work applications that enhance student employability and adds value to their current or future employers.
Course requirements
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
BSAN7205 or DATA7001
Restrictions
MBusAn, MDataSc
Course contact
Course staff
Lecturer
Associate Professor Ida Asadi Someh
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please note: Teaching staff do not have access to the timetabling system to help with class allocation. Therefore, should you need help with your timetable and/or allocation of classes, please ensure you email business.mytimetable@uq.edu.au from your UQ student email account with the following details:
- Full name
- Student ID
- Course Code
Aims and outcomes
This course aims to introduce students to data monetisation concepts, capabilities and techniques. Students will develop deep organisational and managerial knowledge on how data transforms organisations, how it should be governed and what forms an effective data strategy.ﾠThis course will enhance students' employability by using real world scenarios to develop their skills in critical thinking, concise communication, change management, and stakeholder management in helping organisations create strategic value from data.ﾠﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain approaches, opportunities and challenges for monetising business data in a global context.
LO2.
Assess enterprise data monetisation capabilities and governance frameworks needed to build data-driven organisations.
LO3.
Formulate a comprehensive organisational strategy to capture value from digital data assets.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Portfolio | Data Monetisation Portfolio | 40% |
4/04/2025 5:00 pm |
| Project | Data Monetisation Capability Assessment | 20% |
2/05/2025 5:00 pm |
| Paper/ Report/ Annotation | Data Monetisation Strategy | 40% |
6/06/2025 5:00 pm |
Assessment details
Data Monetisation Portfolio
- Mode
- Product/ Artefact/ Multimedia
- Category
- Portfolio
- Weight
- 40%
- Due date
4/04/2025 5:00 pm
- Learning outcomes
- L01
Task description
In the emerging service economy, “the world’s most valuable resource is no longer oil, but data” (The Economist). High quality data enables firms to understand and anticipate the unique expectations of their customers and be able to offer consistently outstanding employee and customer experiences.
Business leaders are encouraged to develop data monetization portfolios to go beyond a single data initiative and reimagine their organizations’ existing services through leveraging organisational data assets.
In this assessment, using the approaches to data monetisation covered in this course, students will propose a data monetization portfolio for your own organization and evaluate it against your company’s strategic priorities.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Further details provided on Blackboard
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Data Monetisation Capability Assessment
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Project
- Weight
- 20%
- Due date
2/05/2025 5:00 pm
- Learning outcomes
- L02
Task description
To deliver on data monetisation goals, organisations need to invest in developing and nurturing a set of specific data-related capabilities:
- Data Asset
- Data Platform
- Data Science
- Customer Understanding
- Acceptable Data Use
These sets of capabilities developed at enterprise-level can help your company systematically and pervasively create value from data.
In this assessment, you will support your organisation to increase its data monetisation maturity through capability development along the five key capabilities. The capability development should facilitate delivering outcomes on data monetization portfolio you have already built in Assessment 1.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Data Monetisation Strategy
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40%
- Due date
6/06/2025 5:00 pm
- Learning outcomes
- L03
Task description
This assessment draws from your earlier work for your organisation in setting out a Data Monetisation Portfolio and assessing the organisation’s Data Monetisation Capabilities to outline a Data Monetisation Strategy for the organisation to execute in delivering on its strategic goals. The Data Monetisation Strategy identifies a Mission, Vision, Objectives, and Initiatives in support of work plans executed according to an agile and active strategic execution process.
The Strategy consists of a 'Strategy to a Page' summary, the Data Monetisation Strategy itself (4-6 pages) and up to 6 pages of supporting material as appendices.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 29 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Grades will be allocated according to University-wide standards of criterion-based assessment.
Supplementary assessment
Supplementary assessment is available for this course.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 |
Not Timetabled |
Topic 1.1: The Data Monetisation Framework This topic builds on a practical framework called Improve, Wrap, Sell (I-W-S) to explain how businesses can create economic value from their digital data assets. This topic provides a foundation for the analysis of the case studies addressed in this week's live session. Learning outcomes: L01 |
Seminar |
Live session In this session we will brief you on assessment requirements and provide an introduction to the course. We will also introduce the approach to case study analysis used in this course. Learning outcomes: L01, L02, L03 |
|
Week 2 |
Not Timetabled |
Topic 1.2: Data Monetisation Case Study This topic leverages the I-W-S framework to showcase how large global companies monetise their business data. This topic provides a foundation for the analysis of the case studies addressed in this week's live session. Learning outcomes: L01 |
Seminar |
Live session In this live session we will analyse several case studies and reflect on the other topics addressed in this module. Learning outcomes: L01 |
|
Week 3 |
Not Timetabled |
Topic 1.3: Creating value with process improvement This topic takes a deep dive into understanding how organisations utilise data to change work processes and make them better, cheaper, and faster. This week we consider the SEC, Guess, and Trinity Health cases. Learning outcomes: L01 |
Seminar |
Live session In this live session we will analyse several case studies and reflect on the other topics addressed in this module. |
|
Week 4 |
Not Timetabled |
Topic 1.4: Creating value with data wrapping This topic focuses on understanding how organisations wrap their products and services with data and analytics features. This week, we consider the Cochlear case. Learning outcomes: L01 |
Seminar |
Live Session In this live session we will analyse several Case studies and reflect on the other topics addressed in this module. |
|
Week 5 |
Not Timetabled |
Topic 1.5: Creating value through selling data This topic explores how organisations build and create value from novel information solutions. This topic provides a foundation for the analysis of the case studies addressed in this week's live session. Learning outcomes: L01 |
Seminar |
Live session In this live session we will analyse several Case studies and reflect on the other topics addressed in this module. Learning outcomes: L01 |
|
Week 6 |
Practical |
Assessment work Learning outcomes: L01 |
Week 7 |
Not Timetabled |
Topic 2.1: Data monetisation capabilities This topic introduced five specific capabilities required for data monetisation. Learning outcomes: L02 |
Practical |
Live sessions In this live session, we will review the module material, and then focus on assessments requirements with a final workshop addressing Q&A as a class. Learning outcomes: L01 |
|
Week 8 |
Not Timetabled |
Topic 2.2: Data-driven transformation This topic explore organisational transformation driven by data and analytics with a focus on structural and cultural changes. This topic provides a foundation for the analysis of the case studies addressed in this week's live session. Learning outcomes: L02 |
Seminar |
Live session In this live session we will analyse several case studies and reflect on the other topics addressed in this module. Learning outcomes: L02 |
|
Mid-sem break |
Not Timetabled |
In-Semester Break Learning outcomes: L02 |
Week 9 |
Not Timetabled |
Topic 3.1: Data Governance Maturity This topic discusses data governance, data governance maturity, its relationship with legislative compliance and ethics. Two case studies are considered this week: IAG and Ruby Rae's. Learning outcomes: L02 |
Practical |
Live session Learning outcomes: L02 |
|
Week 10 |
Not Timetabled |
Topic 3.2: Data Governance Paradigms This topic explores decision rights, paradigms of data governance (including silo-oriented, functional, and platform-based data governance). This week we consider the Intel case study. Learning outcomes: L02 |
Seminar |
Live session In this live session we will analyse several case studies and reflect on the other topics addressed in this module. Learning outcomes: L02 |
|
Week 11 |
Not Timetabled |
Topic 3.3: Data Governance Overview In this topic, we consider our discussions in the prior week as an overview and then consider how to build data governance in support of data monetisation. This topic provides a foundation for the analysis of the case studies addressed in this week's live session. Learning outcomes: L02 |
Seminar |
Live Session In this live session we will analyse several case studies and reflect on the other topics addressed in this module. Learning outcomes: L02 |
|
Week 12 |
Not Timetabled |
Topic 4.1 Data Monetisation Strategy This topic explores data monetisation opportunities and goals, strategy archetypes, and the role of data network efforts in creating strategic value. Learning outcomes: L03 |
Seminar |
Live Session In this live session, we will review the module material, and then focus on assessments requirements with a final workshop addressing Q&A as a class. Learning outcomes: L03 |
|
Week 13 |
Not Timetabled |
Topic 4.2: Building Data Monetisation Strategy This topic explores how to build data monetisation strategies. Learning outcomes: L03 |
Seminar |
Live Session In this live session, we will share and discuss your progress on the strategies developed for the final assessment, discuss and validate approaches to the assessment through a Q&A discussion, and finally reflect on the key learnings from the course. Learning outcomes: L01, L02, L03 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.