BUSA8030 Management of Data, Analytics and Change
The Complete Study & Assessment Guide · S2 2026BUSA8030 Overview
- Macquarie University
- Macquarie Business School
- Session 2, 2026
- Coursework with three graded tasks
- Taught through iLearn
BUSA8030 Management of Data, Analytics and Change is a Macquarie Business School unit that treats business analytics as an organisational capability rather than a programming skill.
- Assessed by An individual critical analysis report, a two-post learning diary and a group project plan with a pitch.
- Hardest step Turning a description of an organisation's technology into a judgment about decisions, people and change.
- How to prepare Keep weekly diary notes tied to class examples and file case evidence under unit concepts.
How BUSA8030 is assessed
| Component | Weight | Format |
|---|---|---|
| Assessment 1: Critical Analysis Report | 35% | Individual business report of about 2,500 words on one organisation; due Friday 4 September 2026, 23:55, via Turnitin |
| Assessment 2: Unit Learning Diary (ULD), Learning Reflection | 20% | Two linked iLearn forum posts of up to 1,000 words each covering Weeks 1 to 10; due Sunday 18 October 2026, 23:55 |
| Assessment 3: Business Analytics Project Plan | 45% | Group written report (10%) and oral dashboard presentation in the Week 13 seminar (35%) |
The three graded tasks total 100%. A weekly learning diary also runs from Week 2 as formative assessment of ongoing learning; it has no weight and is not included in the summative grade. The Assessment 3 brief calls the task the Analytics Digital Transformation Project Plan. Sources give different dates for its written report: Friday 30 October 2026 in the task brief, Friday 31 October 2026 in the iLearn assessment table, and 30 November in some later lecture slides. Check the live iLearn submission page for the date that applies.
Current BUSA8030 dates
| Date | Item | Control |
|---|---|---|
| 2026-09-04 | Assessment 1: Critical Analysis Report | Friday, 23:55 Sydney time, via Turnitin |
| 2026-10-18 | Assessment 2: Unit Learning Diary | Sunday, 23:55; both forum posts in the Week 10 area |
Dates are as published in the Session 2, 2026 assessment briefs and iLearn assessment table. Confirm exact deadlines and submission settings in the live LMS.
What BUSA8030 covers
BUSA8030 moves from the professional mindset, decisions and the DIKW ladder through decision theory, data quality, big data and the five types of analytics, then into digital transformation, readiness, design thinking and agile delivery, and finally data ethics, law, emerging technologies and an assessment playbook.
Why Analytics Needs Management
The data to change chain, three teaching arcs, five learning outcomes and the weekly rhythmBusiness analytics is often taught as a technical toolkit, yet the Macquarie unit starts somewhere else. Its first lecture asks four plain questions about data, analytics, change and management, and the answers form a chain.
- The data to managed change chain
- What the unit description promises
- Three teaching arcs across the session
- Why analytics needs management
- Learning outcomes and the weekly rhythm
- Generative AI rules for submissions
Professional Mindset: Soft Skills, Hard Skills and Roles
Two skill families, collaboration frictions and the main analytics rolesWeek 2 asks what kind of professional business analytics needs, and the answer is someone who combines two families of skill. Hard skills such as coding, statistical and mathematical modelling and testing let a person produce a credible result.
- Soft skills and hard skills compared
- Five frictions between skill profiles
- Six analytics roles and their needs
- Business analysis versus business analytics
- What employers report about graduates
- Repairing a model nobody uses
The DIKW Ladder and the Manager-Technologist Bridge
Decisions as commitments, data to wisdom, and two bridge questionsBefore it defines analytics, the unit defines a decision.
- A decision as a commitment of resources
- Traditional and data-supported decision-making
- The DIKW ladder with a churn example
- DIKW revised for big data
- Managers and information technologists
- Two questions that keep the bridge open
Rational and Naturalistic Views of Decision-Making
Simon, bounded rationality, System 1 and System 2, and the reflective managerWeek 3 opens with Herbert Simon's habit of treating decision-making as almost the same thing as managing. Almost every managerial act ends in a choice, from discount levels to market entry to handling a data breach.
- Deciding as the core of managing
- Obvious and hidden decision situations
- Rationalist and naturalist views
- Bounded rationality
- System 1 and System 2
- The reflective manager
Decision Types and Types of Decision-Makers
Ackoff's layers, Snowden and Boone's contexts, manager types A to DNot all decisions call for the same blend of data and judgment, and Week 3 offers two ways to classify them. By longevity and impact, following Ackoff, decisions are operational, tactical or strategic.
- Operational, tactical and strategic decisions
- Five decision contexts
- When contexts shift
- Four types of manager
- Balanced, high-judgment and high-data styles
- Decision-driven analytics
Data Quality Dimensions and Measurement Scales
Metrics, the four measurement scales and six data quality dimensionsWeek 4 defines business analytics as combining data, IT, statistics, quantitative methods and models so that managers understand their business better and decide on the basis of facts.
- Business analytics and business value
- Metrics, measures and the four scales
- Six dimensions of data quality
- Scoring a data set against a target
- Descriptive and predictive techniques
- Matching technique to scale
Big Data, Analytics Types and AI Oversight
The five Vs, descriptive to autonomous analytics, and human oversightWeek 4 defines big data as data whose size exceeds what traditional database systems and analytics tools can handle, and describes it through five Vs: volume, velocity, variety, veracity and value.
- Big data and the five Vs
- Managing big data
- Ten challenges in four clusters
- Big data versus analytics
- Five types of analytics
- Machine learning, oversight and accountability
Digital Transformation: Strategy, Change and Projects
People and technology, Lewin's stages, the iron triangle and four talent areasWeek 5 turns to the soft side of digital transformation and begins with a basic question: what is the relationship between people and technology?
- Three perspectives on people and technology
- Strategic management and alignment
- Change management, resistance and champions
- Projects, the iron triangle and deliverables
- Outputs versus outcomes
- Digital transformation and its four talent areas
Organisational Readiness and Analytics Maturity
The ecological framework, culture and the adoption phase of maturityWeek 6 starts from a simple premise: no decision is made in a vacuum.
- Context matters for collaborative analytics
- Questions at team, organisation and industry level
- The ecological framework
- Influences that push decisions toward data
- Analytics maturity and the adoption phase
- Adoption strategies and common mistakes
Design Thinking for Analytics-Driven Change
Two paradigm shifts, human-centred problem framing, empathy maps and lotus blossomsWeek 7 introduces design thinking through two paradigm shifts.
- Barriers to business innovation
- Two paradigm shifts
- What design thinking is
- Human-centred and problem-oriented foundations
- Analytical and intuitive thinking
- Empathy maps and lotus blossoms
Agile Methods and Bimodal IT for Analytics Projects
Clear and blurred problems, agile principles and the bimodal hand-overWeek 8 contrasts analytics for business, which focuses on good models with an engineering mindset, with analytics in business, which focuses on business outcomes and treats the system and the work around it as evolving together.
- Analytics for business versus in business
- Starting from the problem, not the tool
- Clear and blurred problems
- Traditional and agile project management
- Agile principles in analytics work
- Bimodal IT and the hand-over
Data Ethics, Privacy and Governance
Concerns, stakeholders, consent and the move from ethics to lawWeek 9 opens the ethics and governance arc by pairing every new data capability with a matching concern. Smart advertising can wound people, as when baby products are shown after a stillbirth.
- Possibilities and concerns in four functions
- Ethics, security, privacy and governance
- Stakeholders and multi-layered responsibility
- Consent questions and two views of privacy
- From ethics to law
- Designing for accountability and fairness
Data Law, Security and Emerging Technologies
GDPR principles, security as maturity, IoT, cloud, AI and blockchainWeek 10 moves from ethics to law.
- Law, ethics and business uncertainty
- Security as a maturity issue
- National data laws
- The seven GDPR principles
- Emerging technologies and decision-making
- Case practice and pitching
Assessment Playbook and Submission Strategy
Critical analysis report, learning diary and group project plan, with timingBUSA8030 is assessed entirely through coursework.
- Three graded tasks and a formative diary
- Critical Analysis Report
- Unit Learning Diary
- Business Analytics Project Plan
- Dates and details that conflict
- Planning backwards from deadlines
Its description puts decision-making, people management and soft skills first, then asks how data and analytics can influence decisions, run processes, projects and people, and lead change.
It also covers enterprise information systems and contemporary digital technologies, business-analytics alignment, business process management, change management, and accountability and ethics in analytics, along with how to judge the value of analytics projects and engage stakeholders.
The session is organised in three arcs.
Foundations and backdrop, in Weeks 1 to 4, build the professional mindset, the centrality of decisions, the DIKW ladder, rational and naturalistic decision-making, decision and manager types, data quality and the five types of analytics. Management and people, in Weeks 5 to 8, cover the soft side of digital transformation, organisational readiness and analytics maturity, design thinking and agile project management.
Ethics and ecologies, from Week 9, cover data ethics, privacy and governance, legal requirements such as the GDPR, security and emerging technologies, followed by case practice and group presentations.
This guide follows that structure across fourteen chapters.
Each chapter rebuilds the lecture concepts in plain language, redraws the key framework as a diagram, works an original example through to a recommendation and ends with practice questions and answers. Cases are fictional unless a set reading is named, and they model reasoning rather than offering text to submit.
The final chapter turns the three graded tasks into a working plan for the session.
Students who do well in this unit tend to share one habit: they connect every piece of data to a decision, every decision to the people who make or feel it, and every proposal to the change needed for it to stick.
The chapters are written to build that habit step by step, so the critical analysis report, the diary and the group plan all draw on the same toolkit.
Worked example · free
AskSia practice weighting: diagnosing why analytics did not change a decision
- 2Chain: data and analytics exist, but no resource has been committed differently, so no data-driven decision has occurred.
- 1DIKW: the reports are information; knowledge about causes and a committed response are missing.
- 2People: budget managers resemble judgment-led decision-makers who need peer evidence and leadership signals.
- 1Readiness: the culture still rewards precedent, a middle-circle block in the ecological framework.
- 2Fix: tie one budget decision to the reports, appoint an analyst to brief the committee, and review outcomes after a year.
Key terms
- Business analytics
- The use of data, technology, statistical and quantitative methods to give managers insight and ground their decisions in facts.
- Decision
- A firm commitment to act, normally by committing money, people, time or other resources.
- DIKW model
- A ladder showing how data becomes information, knowledge and wisdom ready for action.
- Bounded rationality
- The limits on rational choice set by understanding, computation and available information.
- Data quality
- The fitness of data for use, judged on accuracy, completeness, timeliness, validity, integrity and consistency.
- Analytics maturity
- An organisation's analytics capability and how far it is used for decisions and anchored in culture.
- Digital transformation
- Embedding digital technology across an organisation so that its operations and the value it delivers change fundamentally.
- Design thinking
- A human-centred approach to innovation that integrates people's needs, technology and business requirements.
- Bimodal IT
- An arrangement with a stable traditional IT mode alongside a fast, experimental agile mode.
- Data governance
- The policies and legal aspects of acquiring, storing and using data.
BUSA8030 FAQ
Does this unit involve programming or statistics assignments?
No. The lecturer describes BUSA8030 as a unit that is not technical. You will meet terms such as machine learning, regression and data quality, but the graded work asks you to analyse decisions, people, processes and change.
How is the unit assessed?
Through an individual Critical Analysis Report worth 35%, a Unit Learning Diary worth 20% and a group Business Analytics Project Plan worth 45%, with a weekly formative diary that carries no weight.
What does the critical analysis report involve?
You choose an industry and one specific organisation and critically evaluate how analytics has affected its business practice, covering data, technologies, strategy, people and management activities, and future opportunities, challenges and ethics, in about 2,500 words.
What is the group project about?
Teams act as business analyst consultants to solve a problem linked to UN Sustainable Development Goal 7, affordable and clean energy, for one locality through a real organisation, producing a project plan, a change-management plan and a Week 13 pitch.
Which frameworks come up most often?
The DIKW ladder, rational and naturalistic decision-making, dual-process theory, manager types, the six data quality dimensions, the five types of analytics, Lewin's change model, the ecological framework, design thinking tools, agile methods and the GDPR principles.
How should I prepare across the session?
Keep your weekly diary posts specific and tied to class examples, file evidence about your chosen organisation under unit concepts, and plan backwards from each deadline with time for revision.
Questions students ask in each BUSA8030 chapter
- Is this a coding unit?
- No. The opening lecture states that the unit is not technical. You still meet technical vocabulary such as machine learning and data quality, but you use it to reason about decisions, people, processes and organisational change. Chapter 01 · Why Analytics Needs Management
- Which matters more in analytics, soft or hard skills?
- Neither wins outright. Week 2 treats them as two halves of one outcome: technique produces a credible result, while communication and judgment determine whether managers trust and act on it. Chapter 02 · Professional Mindset: Soft Skills, Hard Skills and Roles
- What exactly counts as a decision in this unit?
- Following Mintzberg and colleagues, a decision is a firm commitment to act, normally by committing resources. A meeting that ends with a follow-up and no committed budget, staff or policy has not yet made one. Chapter 03 · The DIKW Ladder and the Manager-Technologist Bridge
- What is the difference between rationalist and naturalist decision-making?
- Rationalist accounts prescribe how decisions should be made, through logical steps and objective criteria. Naturalist accounts describe how experienced people actually decide, often using heuristics under complexity and time pressure. Chapter 04 · Rational and Naturalistic Views of Decision-Making
- How do operational and strategic decisions differ in their use of data?
- Operational decisions are routine and well defined, so data and automation can carry most of the load. Strategic decisions use plenty of data, but the final call often rests on senior executives' experience and judgment. Chapter 05 · Decision Types and Types of Decision-Makers
- Why does the measurement scale matter to a manager?
- The scale decides which comparisons and summaries make sense. Ratios work for ratio data such as sales but not for interval data such as dates, and averaging ordinal survey ranks assumes equal gaps nobody has shown. Chapter 06 · Data Quality Dimensions and Measurement Scales
- What are the five Vs of big data?
- Volume, velocity, variety, veracity and value. The first three describe the data itself, veracity asks whether it can be trusted, and value asks whether anyone benefits from it. Chapter 07 · Big Data, Analytics Types and AI Oversight
- Which perspective on people and technology does the unit take?
- The reciprocal one. Week 5 concludes that technology and people influence each other, so the impact of using a technology depends on both the system and the people working with it. Chapter 08 · Digital Transformation: Strategy, Change and Projects
- What does the ecological framework explain about decisions?
- It shows decisions nested inside layers of influence: the manager and decision type at the centre, colleagues and culture around them, and industry, government and society on the outside, each affecting how data and judgment are used. Chapter 09 · Organisational Readiness and Analytics Maturity
- Why does an analytics unit teach design thinking?
- Because the unit sees analytics as embedded in business activity. Design thinking helps teams find the right human problem before building analytics, and helps managers lead analytics-driven change. Chapter 10 · Design Thinking for Analytics-Driven Change
- What is the difference between analytics for business and analytics in business?
- Analytics for business focuses on building good models with an engineering mindset. Analytics in business focuses on business outcomes and treats the technology and the work around it as evolving together. Chapter 11 · Agile Methods and Bimodal IT for Analytics Projects
- What four concerns does big data raise according to Week 9?
- Ethics, security, privacy and data governance. Governance is defined as the legal aspects of acquiring, storing and using data, and managers must understand all four to avoid scandals and legal consequences. Chapter 12 · Data Ethics, Privacy and Governance
- How does the law differ from ethics in Week 10?
- The lecture treats ethics as what organisations ought to do and law as what governments require and enforce. Governments are turning more ethical expectations into law because technology moves faster than voluntary restraint. Chapter 13 · Data Law, Security and Emerging Technologies
- Which task carries the most weight?
- The group Business Analytics Project Plan, at 45%, outweighs the individual report at 35% and the diary at 20%. Within the group task, the presentation alone is worth more than the written report. Chapter 14 · Assessment Playbook and Submission Strategy
How to prepare for the assessments
Treat the session as one continuous project rather than three separate tasks. From Week 1, keep a single document with headings for the unit's main frameworks, and add an example from each lecture, seminar or reading under the right heading.
That document becomes your evidence bank for the critical analysis report in Week 6, your raw material for the Unit Learning Diary in Week 10 and your toolkit for the group plan.
Write analytically from the start. For every claim about an organisation, add the unit concept that explains it, a source, a judgment and a limitation.
Markers reward analysis and conclusions more than description, and this four-part habit is easy to practise on the practice cards in each chapter.
Use your group well. Agree roles, a shared source list and a meeting log early, use the empathy map and lotus blossom honestly rather than as decoration, and rehearse the five-minute pitch until every member can deliver their part.
When sources disagree about a requirement, ask your tutor, record the answer and follow the live iLearn page.
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