36109 Data Driven 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 2017 is available in the Archives.
Credit points: 8 cp
Result type: Grade, no marks
Requisite(s): 36100 Data Science for Innovation AND 36106 Data, Algorithms and Meaning AND 36103 Statistical Thinking for Data Science
There are course requisites for this subject. See access conditions.
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 action 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)
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
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
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
|Groupwork:||Group, group assessed|
1,500-2000 words (excluding appendices)
Submissions will be assessed on the following criteria
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
1. Issue map should show staged development of map with commentary.
2. Analysis of each of the options canvassed, using any quantitative technique (1000-1500 words)
3. Blog post - 1000-1500 words.
1. Completeness of map wrt points raised in discussion (deliverable 1).
2. Coherence of map (deliverable 1)..
2. Appropriate choice and application of decision model (deliverable 2).
3. Demonstration of a critical understanding of the role of ambiguous role of data in organisational decision making (deliverable 3).
4. Understanding of how human behaviours influence organisational decision making (deliverable 3).
Students must participate in all online and face to face requirements, as well as complete assessment tasks.
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.
March, J. G. (2010), The Ambiguities of Experience, Cornell University Press
Schwartz, B. (2004), The Paradox of Choice, Harper Perennial.
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 https://drr.lib.uts.edu.au/search.html?q=36109