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INFS5700 Individual Assignment
UNSW Business School/
Information Systems and Technology Management
Case Study on Business Analytics Solution
Type Written Report Weighting 15%
Due 6:00 pm Friday, Week 4 (4
October)
Length 1200 words (+/- 10%)
Objective This assignment requires you to write a case study on a recent analytics
solution implemented by an organisation within the past 24 months (i.e.,
September 2022 or after). The objective is to demonstrate your ability to
critically analyse a business problem, examine the application of an analytics
solution to address it, assess its impact on the organisation, reflect on your
own learning and insights gained from the analysis, draw informed
conclusions based on evidence, and effectively communicate your analysis in
writing.
Learning
Outcomes
• CLO1: Critically evaluate the role of data in supporting management
decision-making and gaining competitive advantage.
• CLO2: Discuss and evaluate the Business Analytics framework,
techniques and tools used in gathering, analysing and managing data
and apply them to enhance decision-making.
• CLO4: Investigate the challenges, critical factors and organisational
impacts associated with being business analytically capable.
• CLO6: Research the emerging and global trends of business analytics
tools and practices in industry.
Assessment
Instructions
Your case study should present a real-life situation or challenge faced by an
organisation and explore how it was addressed using analytics. The case
study should include a clear problem definition, a detailed analysis of the
analytics solution, a self-reflection on your understanding and insights, and a
well-supported conclusion. It should be written in a way that it could be used
as a learning resource to help other students learn about the application of
business analytics.
1. Case Selection: Select an organisation that has implemented an analytics
solution (or first publicly announced the implemented analytics solution)
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within the past 24 months. The organisation can be from any industry,
including but not limited to finance, healthcare, retail, manufacturing, or
technology. The chosen case must have sufficient publicly available
information to allow for a comprehensive analysis. The analytics solution
should focus on areas such as predictive analytics, machine learning, data
visualisation, big data analytics, optimisation, or any other relevant area
within business analytics.
2. Case Study Structure: Your case study should be structured as follows,
with suggested word counts for each section:
Introduction (150-200 words)
Provide a brief overview of the organisation, the context in which the
analytics solution was implemented, and the relevance of the solution to
the organisation's goals or challenges. Clearly state the purpose of the
case study and what it aims to achieve.
Problem Definition (200-250 words)
Clearly define the business problem or opportunity that the organisation
aimed to address using analytics. Explain why this problem was significant
and how it aligned with the organisation's strategic objectives. Discuss the
key factors influencing the problem, including external and internal factors.
Analytics Solution Overview (200-250 words)
Provide a general overview of the analytics solution that was implemented.
Describe the type of analytics used (e.g., predictive, descriptive,
prescriptive) and the data sources involved. Highlight the goals of the
analytics initiative and the expected outcomes.
Analysis of the Impact (250-300 words)
Analyse the impact of the analytics solution on the organisation. Discuss
the results achieved, focusing on both qualitative and quantitative
outcomes. Evaluate how the solution helped the organisation achieve its
strategic goals, solve the business problem, or gain a competitive
advantage. Consider both the short-term and long-term impacts on the
organisation's performance, including any challenges faced and how they
were addressed.
Self-Reflection (150-200 words)
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Reflect on your learning experience while conducting this case study.
Discuss any new insights gained about the application of analytics in
business, the complexities of decision-making in real-world scenarios, and
how the case has enhanced your understanding of business analytics.
Consider how this learning might influence your future studies or career in
business analytics.
Conclusions (100-150 words)
Provide a brief summary of the key findings from your analysis. Highlight
the main takeaways and briefly suggest one or two recommendations for
the organisation based on your findings. Ensure your conclusions are
concise and directly supported by the evidence presented in your analysis.
3. Presentation style/format: The assignment needs to be typed in the
following format: Font size 12 point, spacing 1.5, left and right margins set
at 2.54 cm. The pages need to be numerated. It should be written using
correct spelling, grammar and punctuation. Harvard referencing is
required. Please apply the following file naming conversion for your
submission file, which includes the course code, the code of the tutorial
session you’re enrolled in, your zID and your full name:
INFS5700_T11A_z1234567_Firstname Lastname
4. Supporting resources: Topics covered and activities performed in weeks
one to four may help in completing the assessment.
For guidance and support on writing a report, go to:
• The UNSW Business School student site document for writing
reports. (https://www.business.unsw.edu.au/Students- Site/Documents/Writingareport.pdf)
• The UNSW Sydney Learning Centre page for report writing support.
(https://student.unsw.edu.au/writing)
• Studiosity – accessible via
https://www.student.unsw.edu.au/feedback-hub (Studiosity is
UNSW's officially sanctioned online writing support platform made
available to you in this course. You can access Studiosity to receive
comprehensive feedback on the quality of your writing (e.g. clarity
of ideas, organisation, grammar) from a Studiosity tutor).
Submission
guidelines
This assessment will be submitted through Turnitin under the Assessments
Hub on Moodle page.
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Assessment
criteria
Inquiry (30 marks): Inquiry is a systematic process of exploring issues,
objects or works through the collection and analysis of evidence that results in
informed conclusions or judgments. Inquiry includes appropriate problem
definition that demonstrates a good understanding of the existing knowledge
and views on the topic.
Analysis and Conclusions (40 marks): Analysis is the process of breaking
complex topics or issues into parts to gain a better understanding of them,
which should lead to logical conclusions. Your conclusions should provide a
synthesis of key findings drawn from research/evidence with a critique of the
process or evidence on how results apply to the specific business case.
Self-reflection (20 marks): Self-reflection involves a thoughtful consideration
of one's own learning, growth, and understanding throughout the process of
completing the case study. It requires you to critically evaluate your own
experiences, insights gained, and how they might influence your future
academic or professional journey.
Written Communication (10 marks): Written communication is the
development and expression of ideas in writing. Written communication
involves learning to work in many genres and styles. It can involve working
with many different writing technologies, mixing texts, data, and images.
See APPENDIX for Marking Rubrics.
Use of AI
Permission
Level
Assistance with Attribution
This assessment requires you to write/create a first iteration of your
submission yourself. You are then permitted to use generative AI tools,
software or services to improve your submission in the ways set out below.
Any output of generative AI tools, software or services that is used
within your assessment must be attributed with full referencing.
If outputs of generative AI tools, software or services form part of your
submission and are not appropriately attributed, we will determine whether
the omission is significant. If so, you may be asked to explain your
submission. If you are unable to satisfactorily demonstrate your
understanding of your submission you may be referred to UNSW Conduct &
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Integrity Office for investigation for academic misconduct and possible
penalties.
For more information on Generative AI and permitted use please see:
https://www.student.unsw.edu.au/assessment/ai
How to Cite, Reference or Acknowledge Use of AI Tools in Your Work:
https://www.student.unsw.edu.au/ai-referencing
Feedback Feedback will be provided by Friday, 25 October (Week 7) with comments on
the assessment criteria.
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INFS5700 Individual Assignment
UNSW Business School/
Information Systems and Technology Management
APPENDIX: Marking Rubric: Individual Assignment
STANDARDS
Assessment Criteria High Distinction (HD)
85–100 marks
Distinction (DN)
75-84 marks
Credit (CR)
65-74 marks
Pass (PS)
50-64 marks
Fail (FL)
>50 marks
Criterion 1
Inquiry
30 marks
Demonstrates deep and
thorough inquiry with clear
problem definition,
considering multiple
perspectives. Critically
engages with evidence
beyond AI-generated content,
showing awareness of context
and gaps in existing
knowledge.
Strong inquiry with a well- defined problem, supported
by relevant evidence. Shows
some critical engagement
beyond AI-generated insights.
Basic inquiry with an
adequately defined problem
but limited depth and
reliance on AI-generated
content. Minimal critical
engagement.
Limited inquiry with a
vague problem definition
and minimal engagement
beyond AI-generated
insights. Relies heavily on
AI content without critique.
Fails to demonstrate a
systematic inquiry process.
Problem definition is
unclear or missing, with
almost no engagement
beyond AI-generated
content.
Criterion 2
Analysis and
Conclusions
40 marks
Provides a comprehensive
analysis breaking down
complex issues, integrating
multiple sources of evidence
beyond AI-generated insights.
Conclusions are logical,
insightful, and synthesised,
critiquing evidence and its
relevance to the business
case.
Clear analysis with logical
conclusions, integrating AI- generated insights with
original critique. Some
synthesis of evidence, but
may lack depth in critiquing
evidence’s applicability
Basic analysis that relies
heavily on AI-generated
content. Conclusions are
present but may be
generic, with limited
synthesis or critique.
Limited analysis that lacks
depth, relies on AI- generated content.
Conclusions are vague,
unsupported, or not clearly
tied to the analysis.
Lacks meaningful analysis
or presents a superficial
overview based on AI- generated content.
Conclusions are missing,
illogical, or unrelated to the
analysis.
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Criterion 3
Self-reflection
20 marks
Provides a deep and insightful
self-reflection that
demonstrates significant
personal learning and growth.
Clearly articulates new
insights and considers future
academic or career
implications.
Provides meaningful self- reflection with good insights
on learning and future
implications. Shows some
depth in reflection.
Provides adequate self- reflection with some
insights on learning, but
lacks depth or connection
to future implications.
Provides minimal self- reflection with limited
insights on learning. Does
not clearly connect to future
academic or career
implications.
Lacks self-reflection or
presents a superficial
overview with no insights
on learning or future
implications.
Criterion 4
Written
Communication
10 marks
Writing is clear, coherent, and
well-organised, with a strong
personal voice. Effectively
integrates text, data, and
images creatively. Properly
references AI tools and clearly
distinguishes between AI- generated content and
original analysis. Few or no
errors in grammar, spelling, or
formatting.
Writing is clear and
organised, with a good
personal voice. Integrates
text, data, and images
effectively. Minor errors in
grammar, spelling, or
formatting. Satisfactory
attempt to reference AI tools
properly.
Writing is generally clear
but lacks coherence or
engagement. Relies on AI- generated text without
much personal voice.
Adequate integration of
text, data, and images.
Some errors in grammar,
spelling, or formatting.
Writing lacks clarity or
organisation. Limited
personal voice; relies
heavily on AI-generated
text. Minimal use of data or
images. Noticeable errors
in grammar, spelling, or
formatting. Little or no
referencing of AI tools.
Writing is unclear,
disorganised, and difficult
to follow
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