BEX2421 Harnessing Big Data for Business and Society
The Complete Study & Assessment Guide · S2 2026BEX2421 Overview
- Monash University
- Semester 2, 2026
- an undergraduate unit
- 6 credit points
BEX2421 Harnessing Big Data for Business and Society develops critical, creative, ethical and analytical reasoning about big data, AI and evidence-led decisions across business and public problems. It is taught within Monash University Faculty of Business and Economics, Department of Econometrics and Business Statistics.
- Start with the decision Name who must act and what choice they face before selecting data; an impressive dashboard without a decision is not a case study.
- A row needs a meaning Define what one observation represents before proposing fields or charts, because the unit of analysis controls every valid comparison.
- Proposal is not findings Early feasibility work may demonstrate that a plan can run, but it must not be presented as a completed analysis or final conclusion.
What you get in the full BEX2421 Study & Assessment Guide
- 10 chapters, 28 pages — every assessed topic, week by week
- 13 original practice questions, each with a model answer
- 10 worked examples, solved the way BEX2421 marks
- 19 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the Project (40%) — what markers reward, what costs marks
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full study & assessment guide.
How BEX2421 is assessed
| Component | Weight | Format |
|---|---|---|
| Quiz / Test | 20% | Weekly quiz, poll and exit-ticket activity in the current Moodle structure |
| Written | 15% | Written proposal and peer-feedback work in the current Moodle structure |
| Presentation | 25% | Proposal and final case-study presentations |
| Project | 40% | Draft and final case-study submissions |
There is no final exam. The four Handbook categories total 100% and the current Moodle site decomposes them into weekly quiz/exit-ticket work plus staged proposal, peer-feedback, presentation and case-study deliverables. The Handbook is silent on hurdle rules, so the build treats hurdle status as unconfirmed rather than asserting that none exists.
What other students are asking about BEX2421
- Where can I find lecture notes or study guides for BEX2421 at Monash?
- The BEX2421 study guide in AskSia Library covers Harnessing Big Data for Business and Society in 10 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in BEX2421?
- No final exam is listed in the offering this AskSia Library guide is built from; BEX2421 is assessed by Quiz / Test 20%, Written 15%, Presentation 25%, Project 40%.
- What does BEX2421 cover on big data, the five vs and alternative data?
- In AskSia Library's BEX2421 guide, the Big Data, the Five Vs and Alternative Data chapter explains big data · Five Vs · alternative data · classify why a proposed dataset is challenging and decide which V creates the controlling analytical problem.
- What does BEX2421 cover on ai, machine learning and natural language processing?
- In AskSia Library's BEX2421 guide, the AI, Machine Learning and Natural Language Processing chapter explains artificial intelligence · machine learning · natural language processing · compare analytical approaches by target, evidence, output and consequence rather than by technical novelty.
- What does BEX2421 cover on surveillance capitalism, rights and power?
- In AskSia Library's BEX2421 guide, the Surveillance Capitalism, Rights and Power chapter explains surveillance capitalism · data subject · power asymmetry · analyse how a data practice creates value and redistributes visibility, choice and contestability among actors.
Current BEX2421 dates
| Date | Item | Control |
|---|---|---|
| 14 August 2026 at 11:55 pm | Proposal presentation | Part of the 25% Presentation category; confirm the live activity before submission. |
| 28 August 2026 at 11:55 pm | Written proposal | Part of the 15% Written category. |
| 2 October 2026 at 11:55 pm | Case-study draft | A staged Project submission. |
| 16 October 2026 at 11:55 pm | Final presentation | Part of the 25% Presentation category. |
| 23 October 2026 at 11:55 pm | Final case-study submission | The final staged Project submission; check Moodle for the current brief. |
Dates are as published in the current course materials. Confirm exact deadlines and submission settings in the live LMS.
What BEX2421 covers
Build the course in three arcs: Digital Transitions and the Age of Big Data establishes the frame, Decision Owners, Research Questions and Units of Analysis deepens it, and Visual Evidence, Peer Review and Case-study Defence tests the complete method.
Digital Transitions and the Age of Big Data
digital transition · datafication · technological megatrend · explain how a technological transition creates both a new data source and a changed decision contextDefine digital transition
The course material gives this chapter a concrete anchor: The opening module frames big data through mutually reinforcing technological changes rather than through storage size alone.
- digital transition
- datafication
- technological megatrend
- Applying digital transition
- Limits of datafication and technological megatrend
Big Data, the Five Vs and Alternative Data
big data · Five Vs · alternative data · classify why a proposed dataset is challenging and decide which V creates the controlling analytical problemDefine big data
The course material gives this chapter a concrete anchor: The current Big Data Deep Dive asks students to distinguish classical and big-data acquisition and to explain a formal multi-component definition.
- big data
- Five Vs
- alternative data
- Applying big data
- Limits of Five Vs and alternative data
AI, Machine Learning and Natural Language Processing
artificial intelligence · machine learning · natural language processing · compare analytical approaches by target, evidence, output and consequence rather than by technical noveltyDefine artificial intelligence
The course material gives this chapter a concrete anchor: The second published learning outcome requires critical comparison of AI, machine-learning and NLP strengths and weaknesses in business and social-science problems.
- artificial intelligence
- machine learning
- natural language processing
- Applying artificial intelligence
- Limits of machine learning and natural language processing
Surveillance Capitalism, Rights and Power
surveillance capitalism · data subject · power asymmetry · analyse how a data practice creates value and redistributes visibility, choice and contestability among actorsDefine surveillance capitalism
The course material gives this chapter a concrete anchor: The current Big Data & Us module explicitly names surveillance capitalism before moving into rights and regulation.
- surveillance capitalism
- data subject
- power asymmetry
- Applying surveillance capitalism
- Limits of data subject and power asymmetry
Data Governance, Regulation and Responsible Control
data governance · purpose limitation · accountability · map collection, access, analysis, sharing and review to an accountable owner and a testable controlDefine data governance
The course material gives this chapter a concrete anchor: The rights-and-regulation topic and public-sector case options require governance to travel with the proposed use of data.
- data governance
- purpose limitation
- accountability
- Applying data governance
- Limits of purpose limitation and accountability
Decision Owners, Research Questions and Units of Analysis
decision owner · research question · unit of analysis · turn a broad social or business topic into a decision-led question with an explicit row definitionDefine decision owner
The course material gives this chapter a concrete anchor: The 2026 case-study bank requires each proposal to name the decision, focused question, row meaning and fields before proposing analysis.
- decision owner
- research question
- unit of analysis
- Applying decision owner
- Limits of research question and unit of analysis
Data Provenance, Bias and Limits of Inference
data provenance · selection bias · inference boundary · audit how observations enter a dataset and state the strongest conclusion the design can supportDefine data provenance
The course material gives this chapter a concrete anchor: The case bank repeatedly flags surveillance, reporting and selection limits, while the proposal brief requires a statement of what the data cannot establish.
- data provenance
- selection bias
- inference boundary
- Applying data provenance
- Limits of selection bias and inference boundary
Literature, Conceptual Frameworks and Source Verification
literature review · conceptual framework · source verification · connect a focused research question to reviewed concepts and independently verifiable sourcesDefine literature review
The course material gives this chapter a concrete anchor: The proposal guide allocates distinct criteria to purpose, literature and conceptual framing and requires source verification rather than citation volume alone.
- literature review
- conceptual framework
- source verification
- Applying literature review
- Limits of conceptual framework and source verification
Feasibility, Ethics, Teamwork and Responsible AI
feasibility analysis · ethical risk · responsible AI · design a feasible team workflow that identifies risks, mitigations, fallbacks and independent verification of AI-assisted workDefine feasibility analysis
The course material gives this chapter a concrete anchor: The current proposal guide requires milestones, ethical risks, mitigation, fallback and a record of how generated or revised material was independently verified.
- feasibility analysis
- ethical risk
- responsible AI
- Applying feasibility analysis
- Limits of ethical risk and responsible AI
Visual Evidence, Peer Review and Case-study Defence
data visualisation · peer feedback · case-study defence · integrate visual analysis, written reasoning and oral defence while preserving the distinction between proposal, draft and verified final findingDefine data visualisation
The course material gives this chapter a concrete anchor: The current structure stages proposal presentation, writing, peer feedback, draft, final presentation and final submission across the 25% and 40% categories.
- data visualisation
- peer feedback
- case-study defence
- Applying data visualisation
- Limits of peer feedback and case-study defence
It is an undergraduate unit. It carries 6 credit points.
It is positioned as an integrating business-and-society unit using case-based and problem-based learning.
Students work from a decision owner and research question through a data plan, literature, ethics, visual analysis, proposal, peer feedback, presentation and final case study rather than preparing for a final examination.
Assessment in BEX2421 is distributed as follows: 20% Quiz/Test, 15% Written, 25% Presentation and 40% Project.
The current Moodle assessment summary decomposes these categories into weekly quiz and exit-ticket work, proposal presentation, written proposal, peer feedback, project draft, final presentation and final submission.
The operational assessment conditions matter here. There is no final exam in the published BEX2421 assessment set.
Assessment is completed through quizzes, written and oral proposal work, peer feedback and a staged case-study project; the live Moodle task instructions control delivery details.
What makes BEX2421 demanding is concrete: Keeping a case study decision-led and evidence-bounded: students must define the decision owner and unit of analysis, connect literature to a feasible data plan, distinguish association from a supported inference, address ethics and bias, and communicate a recommendation without turning a proposal into fabricated findings.
Treat the BEX2421 hurdle status as unconfirmed.
Check the current Monash Handbook and learning site for any component-level pass rule before relying on the overall mark.
For enrolment planning, The 2026 Handbook publishes an enrolment-rule panel; students should use the current Handbook and course advice rather than infer eligibility from the BEX code.
Build the course in three arcs: Digital Transitions and the Age of Big Data establishes the frame, Decision Owners, Research Questions and Units of Analysis deepens it, and Visual Evidence, Peer Review and Case-study Defence tests the complete method.
Coverage note: Module 1 is deeply captured, while Modules 2 and 3 are represented by the current schedule, learning pages, case-study brief and assessment artifacts rather than a complete transcript of every workshop slide.
Worked example · free
Turn a broad data topic into a defensible case-study proposal
- 1Name the decision owner, available action and target population.
- 1Write a focused research question tied to that decision.
- 1Define one row, key fields and the period represented by the proposed dataset.
- 1Specify a visual or comparison and the outside evidence needed to interpret it.
- 1State selection, reporting and ethical limits plus one mitigation.
Key terms
- Big data
- Data whose scale, speed, diversity or complexity makes conventional acquisition and analysis practices inadequate for the intended task.
- Surveillance capitalism
- A critical account of business models that convert behavioural data and prediction into economic value and influence.
- Five Vs
- A common big-data framing through volume, velocity, variety, veracity and value, each of which describes a different design or interpretation problem.
- Alternative data
- Information produced outside traditional reporting channels and repurposed to inform a decision, subject to provenance, access and representativeness limits.
- Decision owner
- The person or institution responsible for choosing and acting on an option within the case-study scope.
- Research question
- A focused, answerable inquiry that links the decision to the evidence and method needed to inform it.
- Unit of analysis
- The entity represented by one observation or row and about which the analysis draws a conclusion.
- Data provenance
- Recorded information about where data originated, how they were generated and how they were transformed before use.
- Selection bias
- Systematic distortion arising when observed cases differ from the target population through the process that made them available.
- Data governance
- The allocation of decision rights, responsibilities, standards and controls for data across its lifecycle.
- Conceptual framework
- An organised set of concepts and relationships used to explain what the study will examine and why the proposed evidence is relevant.
- Feasibility analysis
- A structured check of whether the question, data, method, time, skills and dependencies permit the proposed study to be completed responsibly.
- Responsible AI
- The design, use and oversight of AI with explicit attention to validity, fairness, transparency, accountability and affected people.
BEX2421 FAQ
How is BEX2421 assessed?
20% Quiz/Test, 15% Written, 25% Presentation and 40% Project. The current Moodle assessment summary decomposes these categories into weekly quiz and exit-ticket work, proposal presentation, written proposal, peer feedback, project draft, final presentation and final submission.
What is the BEX2421 final assessed-task format?
There is no final exam in the published BEX2421 assessment set. Assessment is completed through quizzes, written and oral proposal work, peer feedback and a staged case-study project; the live Moodle task instructions control delivery details.
Where do students usually lose marks in BEX2421?
Keeping a case study decision-led and evidence-bounded: students must define the decision owner and unit of analysis, connect literature to a feasible data plan, distinguish association from a supported inference, address ethics and bias, and communicate a recommendation without turning a proposal into fabricated findings.
Does BEX2421 have a hurdle or component-level pass rule?
Treat the BEX2421 hurdle status as unconfirmed. Check the current Monash Handbook and learning site for any component-level pass rule before relying on the overall mark.
Which current BEX2421 dates are captured?
Proposal presentation: 14 August 2026 at 11:55 pm; Written proposal: 28 August 2026 at 11:55 pm; Case-study draft: 2 October 2026 at 11:55 pm; Final presentation: 16 October 2026 at 11:55 pm. Confirm any change and the exact submission setting in the live LMS.
Which offering does this BEX2421 guide cover?
It is aligned to Semester 2, 2026; confirm your enrolled class and timetable in the current institutional system.
Is this BEX2421 resource an official university guide?
No. It is an independent BEX2421 study resource; current institutional instructions remain authoritative for assessment operation.
What prerequisites or restrictions apply to BEX2421?
The 2026 Handbook publishes an enrolment-rule panel; students should use the current Handbook and course advice rather than infer eligibility from the BEX code.
Questions students ask in each BEX2421 chapter
- What is the main task in Digital Transitions and the Age of Big Data?
- Explain how a technological transition creates both a new data source and a changed decision context. Chapter 01 · Digital Transitions and the Age of Big Data
- What is the main task in Big Data, the Five Vs and Alternative Data?
- Classify why a proposed dataset is challenging and decide which v creates the controlling analytical problem. Chapter 02 · Big Data, the Five Vs and Alternative Data
- What is the main task in AI, Machine Learning and Natural Language Processing?
- Compare analytical approaches by target, evidence, output and consequence rather than by technical novelty. Chapter 03 · AI, Machine Learning and Natural Language Processing
- What is the main task in Surveillance Capitalism, Rights and Power?
- Analyse how a data practice creates value and redistributes visibility, choice and contestability among actors. Chapter 04 · Surveillance Capitalism, Rights and Power
- What is the main task in Data Governance, Regulation and Responsible Control?
- Map collection, access, analysis, sharing and review to an accountable owner and a testable control. Chapter 05 · Data Governance, Regulation and Responsible Control
- What is the main task in Decision Owners, Research Questions and Units of Analysis?
- Turn a broad social or business topic into a decision-led question with an explicit row definition. Chapter 06 · Decision Owners, Research Questions and Units of Analysis
- What is the main task in Data Provenance, Bias and Limits of Inference?
- Audit how observations enter a dataset and state the strongest conclusion the design can support. Chapter 07 · Data Provenance, Bias and Limits of Inference
- What is the main task in Literature, Conceptual Frameworks and Source Verification?
- Connect a focused research question to reviewed concepts and independently verifiable sources. Chapter 08 · Literature, Conceptual Frameworks and Source Verification
- What is the main task in Feasibility, Ethics, Teamwork and Responsible AI?
- Design a feasible team workflow that identifies risks, mitigations, fallbacks and independent verification of ai-assisted work. Chapter 09 · Feasibility, Ethics, Teamwork and Responsible AI
- What is the main task in Visual Evidence, Peer Review and Case-study Defence?
- Integrate visual analysis, written reasoning and oral defence while preserving the distinction between proposal, draft and verified final finding. Chapter 10 · Visual Evidence, Peer Review and Case-study Defence
How to prepare for the assessments
Retrieve the course map, practise the recurring method—name the decision owner and action, define the research question and unit of analysis, trace data provenance, compare feasible analytical approaches, test bias and ethical risk, and communicate a bounded recommendation supported by literature and visual evidence—on changed scenarios, and verify every operational assessment detail in the live institutional system.
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