University of Technology Sydney

36109 Data and Decision Making

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2021 is available in the Archives.

UTS: Analytics and Data Science: Transdisciplinary Innovation
Credit points: 8 cp
Result type: Grade, no marks

There are course requisites for this subject. See access conditions.

Requisite elaboration/waiver:

Any student wishing to enrol in first- and second-year subjects concurrently must apply for a waiver.


This subject introduces students to diverse ways of assisting people to make decisions in organisational settings. The key themes of uncertainty and ambiguity are emphasised in all stages of the decision-making process, right from identifying stakeholder needs and acquiring relevant data through to supporting decision making and influencing stakeholder behaviours. Using both a hard and soft systems thinking approaches, the subject explores various decision analysis methods and discusses the practical challenges to rational decision making. Working individually and in teams students ask probing questions and gather/present evidence that supports decision making in situations of varying complexity. Most importantly, the subject helps students develop an understanding of the different types of decision problems they are likely to encounter in their professional lives and the diverse approaches that can be used to tackle them.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Understand contemporary decision theory approaches and their applications to real life decision problems
2. Critically evaluate sources and variability of data to work with uncertainty, ambiguity and complexity
3. Draw upon system thinking to understand data and decision making in a complex organisational ecology, including external influences
4. Discuss the impact of risk and uncertainty on decision making and on the implementation of decisions
5. Discuss behavioural aspects of human and managerial decision making in the context of organisational culture
6. Develop and implement strategies and criteria for ethical data-driven decisions for different community, organisational or cultural contexts, identifying specific stakeholder needs and values

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following course outcomes:

  • Exploring and testing models and describing behaviours of complex systems
    Explore and test models and generalisations for describing the behaviour of sociotechnical systems and selecting data sources, taking into account the needs and values of different contexts and stakeholders (1.2)
  • Making predictions and informing data discovery
    Analyse the value of different models, established assumptions and generalisations, about the behaviour of particular systems, for making predictions and informing data discovery investigations (1.3)
  • Critiquing trends and theoretical frameworks
    Critique contemporary trends and theoretical frameworks in data science for relevance to one's own practice (2.1)
  • Understanding uncertainty, ambiguity and complexity
    Understand and deal critically and openly with the uncertainty, ambiguity and complexity associated with people, systems and data (2.3)
  • Examining and articulating data value
    Critically examine the perceived value of data analytics outcomes and clearly articulate implications for different stakeholders and organisations (3.2)
  • Working together
    Develop a collaborative and team-oriented mindset to harness value for stakeholders to produce innovative solutions to challenges (3.3)
  • Informing decision making
    Develop, test, justify and deliver data project propositions, methodologies, analytics outcomes and recommendations for informing decision-making, both to specialist and non-specialist audiences (4.3)
  • Becoming a reflective data practitioner
    Engage in active, reflective practice that supports flexible navigation of assumptions, alternatives and uncertainty in professional data science contexts (5.1)

Teaching and learning strategies

Blend of online and face to face activities: This subject is offered through a series of block sessions and blends online with face-to-face learning. Students participate in interactive learning experiences in timetabled on-campus sessions, where they make use of the subject materials that they have already engaged with online. In between campus sessions, students will engage in individual and collaborative online activities designed to support the understanding of the decision-making process in data environment.

Collaborative work: A strong emphasis is placed on group activities and interaction, given that graduates of this course will need to approach professional projects and challenges from a collaborative and consensus position. Insights obtained and developed within the groups is then reworked by individual students to develop the final summative assessment activity. Group assessments and activities enable students to leverage peer-learning and demonstrate effective skills associated with the topics covered in this subject.

Transdisciplinary approaches: Starting from an elemental perspective on data and data science, students will approach learning from their specific professional and potential future contexts. As the subject progresses, the students will be able to combine their analytical and technical skills in developing and communicating approaches to making decisions in data environment, as well as to consider standards and ethical implications of their work.


Assessment task 1: Looking for Gold. A business optimisation scenario


Students will gain insight into managing and communicating uncertainty in business decision making using quantitative decision making methods and models.


1 and 2

Type: Report
Groupwork: Individual
Weight: 35%

Part A: Working model + one paragraph outlining your forecasts with appropriate supporting visuals.

Part B: Working model + written report on findings (1000 words max).

Important Note: All claims in should be justified via models (incl assumptions), data and sources. This material is part of the assessment and must be included as appendices to the report.


Submissions will be assessed on the following criteria

  1. Understanding of Monte Carlo simulation.
  2. Estimating required variables correctly.
  3. Communicating results / findings in a manner appropriate to the task.
  4. Critically examining the provided scenario and discovering hidden factors (if appropriate).

Assessment task 2: Developing an entrepreneurial business case


Students gain experience in dealing with uncertainty and ambiguity through developing an entrepreneurial or intrapreneurial business case supported by decision making techniques.


1, 3, 4 and 5

Type: Report
Groupwork: Group, group assessed
Weight: 30%

1,500-2000 words (excluding appendices)


Submissions will be assessed on the following criteria

  1. Depth and breadth of research and its relevance to the proposal
  2. Quality of decision model and supporting arguments
  3. Appropriateness of risk and challenge considerations and development of strategies for mitigation
  4. Relevance of ethical implications (if any) identified
  5. Clarity and persuasiveness of the proposal

Assessment task 3: Collaborative decision making


Students gain experience with visualizing and modelling socially complex (wicked) decision problems. The techniques used help in making diverse viewpoints explicit, thereby highlighting points of difference and agreement. Based on the shared understanding achieved by these techniques, students will develop a business case using the rational decision-modelling techniques learnt earlier.


1, 2, 3, 4 and 5

Type: Project
Groupwork: Individual
Weight: 35%

1. Issue map showing staged development of map with commentary (500 words)

2. Ranking of options canvassed with appropriate discussion (500 words)

3. Blog post - 1000-1500 words.


Deliverable 1 (Issue Map & commentary)

  1. Completeness and of map and alignment to points raised in discussion
  2. Accuracy of IBIS
  3. Coherence of commentary

Deliverable 2 (Implementation of decision making method)

  1. Accuracy in decision making approach
  2. Coherence and consistency in explanation of choices made

Deliverable 3 (blog post)

  1. Depth of reflection
  2. Clarity in articulating learnings/insights gained from subject

Minimum requirements

Students must participate in all online and face to face requirements, as well as complete assessment tasks.

Recommended texts

March, J. G. (1994). Primer on decision making: How decisions happen. Simon and Schuster.

Ariely, D. (2008). Predictably irrational (p. 20). New York: HarperCollins.

Kahneman, D. (2011). Thinking, fast and slow. Macmillan.

Silver, N. (2012). The signal and the noise: Why so many predictions fail-but some don't. Penguin.

Kurt, Will (2019). Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks. No Starch Press.

McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC press.

March, J. G. (2010), The Ambiguities of Experience, Cornell University Press

Culmsee, P., & Awati, K. (2013). The Heretic's Guide to Best Practices: The Reality of Managing Complex Problems in Organisations. iUniverse Star.

Culmsee, P., & Awati, K. (2016). The Heretic's Guide Management: The Art of Harnessing Ambiguity. Heretics Guide Press.

Note: some of the above books have been held on reserve for 36109 students at the UTS Library. Please enquire at the library front desk for details.


The following is an Indicative List of Suggested Reading. Students will be provided lists of suggested readings to prepare for Block classes and in relation to each cluster throughout the semester.

1. March, J. G. (1991). How decisions happen in organizations. Human-computer interaction, 6(2), 95-117.

2. Silver, N. (7 Sept, 2012). The weatherman is not a moron.?The New York Times.

3. Snowden, D. J., & Boone, M. E. (2007). A leader's framework for decision making.?Harvard business review,?85(11), 68.

4. Hammond, J. S., Keeney, R. L., & Raiffa, H. (1998). The hidden traps in decision making.?Harvard business review,?76(5), 47-58.

5. Kahneman, Daniel, Dan Lovallo, and Olivier Sibony. "Before you make that big decision."?Harvard business review?89.6 (2011): 50-60.

6. Alvesson, Mats, and André Spicer. "A stupidity?based theory of organizations."?Journal of management studies?49.7 (2012): 1194-1220.

7. Awati, K. (2011). Mapping project dialogues using IBIS: a case study and some reflections.?International Journal of Managing Projects in Business,?4(3), 498-511.

8. Culmsee, P., & Awati, K. (2012). Towards a holding environment: building shared understanding and commitment in projects.?International Journal of Managing Projects in Business,?5(3), 528-548.

9. Snowden, D. (2015). Propensities and dispositions. The Journal of Corporate Citizenship, (58), 41-44.

10. French, S. (2013). Cynefin, statistics and decision analysis.?Journal of the Operational Research Society,?64(4), 547-561. (Advanced)

11. French, S. (2015). Cynefin: uncertainty, small worlds and scenarios.?Journal of the Operational Research Society,?66(10), 1635-1645. (Advanced)

12. Pauleen, D. (2017). Dave Snowden on KM and big data/analytics: interview with David J. Pauleen. Journal of Knowledge Management, 21(1).

Note: Many of the above papers are available to UTS students via