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Delivering Performance Excellence (DPE): Business Analytics
Business Analytics Assessment: Individual Assignment
Mark: 35% of DPE Module marks
Submission date: 5th March 2026
Required: Business Analytics Report
Format: 1500-2000 words (Excluding graphs and charts) based on the guidelines below.
I. Assignment Brief
This assignment requires you to produce an academically grounded business analytics report.
You are required to select one dataset from the pool of datasets provided on the assignment
Loop submission link. All datasets have been sourced from open-access repositories and are
approved for use for educational purposes only.
Choose a dataset that aligns with an industry sector of interest to you (e.g. Healthcare
Management, Human Resources, Marketing, Inventory Management, Transport, Education,
etc.). Your role is to identify a business problem or opportunity that can be addressed
analytically using the variables available in the selected dataset.
Your task is to conduct the appropriate analytics processes to address the identified problem or
opportunity and to present your findings in a business analytics report.
In brief, a business analytics report is a structured document that presents data-driven insights to
inform business decision-making. Using your chosen dataset, you are required to conduct descriptive,
predictive, and prescriptive analytics.
1. 2. II. Analytics Report Framework
1. Organisational Context and Decision Challenge (20%)
This section must demonstrate that the analytics work is grounded in a business need. You should
include:
Industry Context: Introduce the sector and explain the relevance of the dataset to a real
industry setting.
Decision Problem or Strategic Opportunity: Clearly define the business problem or
opportunity. Business Value and Strategic Importance: Explain why this issue matters and what
organisational value is sought (e.g., efficiency, growth, risk mitigation, optimisation).
Analytics Objectives and Key Questions: Frame clear, data-answerable business questions
aligned with the decision challenge.
2. Working with Data and Analytical Design (20%)
This section must demonstrate the use of the dataset to answer the business questions, not just
technical execution. You should include:
Dataset Overview and Variable Classification: Identify key predictors (independent variables)
and targets (dependent variables).
Data Exploration and Assumptions: Discuss patterns, outliers, and potential limitations.
Data Preparation and Transformation: Explain cleaning steps and justification.
Analytical Approach and Justification: Describe why specific descriptive, predictive, and
prescriptive techniques were selected (you can limit the techniques to those taught in class).
3. Analytical Execution and Evidence (30%)
This section presents the analytic process and techniques in a structured analytical output.
Descriptive steps and insights
Predictive modelling results
Prescriptive analysis and decision scenarios
Analytics Dashboard: All key charts, tables, and visualisations must be presented together.
Each visual must include a short managerial insight statement.
4. Critical Evaluation and Managerial Insight (20%)
This section discusses your evaluation of the results.
Interpretation of results
Discussion of reliability, assumptions, risks, and limitations.
Managerial implications
Demonstrate how analytical outputs are combined with your industry understanding to inform
decisions.
5. Recommendations and Decision Communication (5%)
This section translates your analysis into action.
Actionable recommendations
Expected organisational impact
Implementation considerations
6. Housekeeping (5%)
Harvard or APA referencing (include DOIs where available)
Logical structure and coherent argumentation
Table of contents
Professional presentation of dashboard and appendices
II. Minimum Requirements for Technical Analytics
1. Descriptive Analytics
a) Select four (4) variables from the dataset and formulate four (4) descriptive analytics
questions that are relevant to your stated business problem.
b) Produce data visualisations to support your descriptive analysis and summary statistics.c) Each visualisation must include a brief insight statement explaining what the visual shows
and what decision or action it may inform.
All descriptive analytics visualisations should be compiled and presented together in an
analytics dashboard.
2. Predictive Analytics
Formulate and analyse at least one (1) predictive analytics question. Explain how the results of the
analysis could influence or support the business decision or action.
3. Prescriptive Analytics
Formulate and analyse at least one (1) prescriptive analytics question. Clearly explain how the
resulting recommendation would change or improve the business decision or action.
Notes:
1. 2. 3. Academic work at MSc level is expected to demonstrate independent research and critical
judgement, supported by academic evidence and reputable third-party sources. Please use the
Harvard or APA referencing style throughout your work.
A wide range of relevant peer-reviewed journal articles covering all areas of analytics is
available and should be consulted where appropriate.
Plagiarism will not be tolerated. All sources must be properly acknowledged in accordance
with the chosen referencing style in short : This is not a data exercise.
It is a decision-focused business analytics report.
You are expected to:
Choose one dataset
Identify a real business problem or opportunity
Move through:
o Descriptive
o Predictive
o Prescriptive analytics
Translate everything into managerial decisions
Its 35% of the module. High stakes.
The structure is fixed. Marks are allocated per section.
1. Organisational Context & Decision Challenge (20%)
This is where most students go wrong.
You must:
Introduce the industry
Explain why the dataset is relevant
Define a clear business problem
Identify your target variable
Explain:
o Why this problem matters
o What decision will be made
o What business value is expected
Important:
Do NOT write data questions like:What is the average X?
Instead write: What factors influence X so that management can decide Y?
This section is strategic. Not technical.
Working with Data & Analytical Design (20%)
Now you move into:
Variable classification (dependent vs independent)
Data exploration (patterns, outliers, assumptions)
Data cleaning and justification
Why you selected:
o Descriptive methods
o Predictive model
o Prescriptive technique
Key rule:
Do not dump everything you tried in Excel.
Only include what supports your decision logic.
Analytical Execution & Evidence (30%)
This is your technical core.
Descriptive (Minimum Requirement)
4 variables
4 descriptive questions
Visualisations
Each visual must include a short managerial insight
Not just: Mean = 20
But:The high variance suggests instability in X, which may affect Y decision.
Predictive (Minimum 1)
One clear predictive question
One target variable
Regression (if continuous)
Logistic regression if categorical (be careful here)
Explain:
What does the model tell management?
What decisions does it support
Prescriptive (Minimum 1)
Use Solver or scenario analysis
Show how decision changes
Show improvement logic
This is about: Given what we know, what should we do?
Dashboard
All visuals must be presented together.
Each must include:
Clear label
Short managerial insight
No random graphs.
4. Critical Evaluation & Managerial Insight (20%)
This is where MSc-level thinking shows.
You must:
Interpret results in business terms
Discuss:
o Reliability
o Assumptions
o Risks
o Limitations
Show understanding beyond Excel
This is not repeating results.
This is critical reflection.
5. Recommendations (5%)
Clear.
Actionable.
Decision-focused.
Explain:
What should management do?
Expected impact
Implementation considerations
6. Housekeeping (5%)
15002000 words
Harvard or APA
Table of contents
Professional structure
Proper placement of figures (not dumped in appendix)
Minimum Technical Requirements (Non-Negotiable)
From the brief :
4 descriptive questions
1 predictive
1 prescriptive
Dashboard compiled
Each visual includes insight statement
If one of these is missing, marks drop immediately.
The Most Important Warnings from the Transcript
From your professors explanation :
You must define your own business problem.
Target variable choice is critical.
Keep regression constraints in mind.
Do not overwhelm with technical noise.
Business value > technical complexity.
Critical evaluation separates high grades from average ones.
Use DCU grade descriptor to aim for distinction level thinking
What I Need From You Now
To properly guide you:
1. Which dataset are you choosing?
2. What industry is it from?
3. Do you already have a business problem in mind?
4. Is your target variable continuous or categorical?
Once I know that, I can:
Help you refine your decision challenge
Make sure your target variable works for regression
Structure your descriptive questions correctly
Design your predictive + prescriptive logic properly
Make sure your report hits distinction level
THE PROFESSOR IMPORTANT REQUIREMENTS:
This Is a Decision Report Not a Data Report
She repeated this multiple times.
Your work must always answer:
What decision will this analysis support?
If your report sounds like:
We analysed X.
The mean is Y.
The correlation is Z.
Thats weak.
It must sound like:
Understanding X allows management to decide Y.
If variable A increases, the company should consider B.
Everything must point toward decision-making.
Target Variable Choice Is Critical
She warned clearly about this.
If your target variable is:
Categorical you cannot run linear regression.
Binary you would need logistic regression.
She explicitly mentioned students making this mistake and realising too late.
So before you start:
Confirm your target variable works with the regression method taught in class.
This is a technical constraint you must respect.
Do Not Overcrowd With Analytics
She said:
You will try many things in Excel.
Do NOT put everything in the report.
Only include analysis that supports your decision logic.
This means:
No random charts.
No unnecessary statistics.
No just because I can analysis.
Be selective. Strategic. Intentional.
Insight After Every Analysis
This was strongly emphasised.
For:
Every descriptive statistic
Every visual
Every model result
You must add 12 sentences explaining:
What does this mean?
Why does it matter?
What decision does it influence?
No raw outputs
Section 1 and Section 4 Require the Most Thinking
She clearly said:
Section 2 and 3 are more technical.
Section 1 (context) and Section 4 (critical evaluation) require real thinking.
These sections determine distinction-level work.
Especially Section 4:
Reliability
Assumptions
What you would improve
What data is missing
Risks of using this model
Thats where MSc depth shows.
Use Academic Support to Justify Importance
She suggested:
Read 12 peer-reviewed papers in your datasets area.
Use them to justify:
o Why your problem matters.
o Why certain variables are important.
o What might be missing.
This strengthens Section 1 and Section 4 significantly.
Many students skip this and lose quality.
Business Value Over Technical Complexity
She made this very clear.
Doing:
5 regressions
10 models
Complex analysis
Does NOT equal higher marks.
Clear logic + decision value = higher marks.
Dashboard Must Be Clean and Purposeful
She emphasised:
All visuals together.
Each must serve the business question.
No visual noise.
Each visual must have insight.
Dumping visuals into appendix is wrong.
Putting visuals randomly in text is wrong.
They must be placed logically and referenced properly.
This Is Structured Like a Research Paper
She compared it to:
Methodology
Results
Interpretation
Recommendations
That means:
Clear flow.
Logical progression.
Not jumping between sections.
Work Within Constraints
She said something very important conceptually:
Business analytics also means working within:
Data constraints
Technical constraints
Skill constraints
If something cannot be done with your dataset or tools,
acknowledge it and justify your approach.
That shows maturity.
The Real Hidden Message
What she really emphasised overall:
This assignment tests whether you can:
Think like a decision-maker
Think like an analyst
Connect technical outputs to business logic
Critically evaluate your own analysis
Not just use Excel.