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Summarise and characterise the problem that needs a solution through data analysis. Formulate a question that will be answered through analysis of the data set.

Assessment Brief

BUS5015 Data Analytics and Management

Module title: Data Analytics and Management

Module code: BUS5015

Assignment title: BUS5015 Assessment

Assignment format: Report or Narrated PowerPoint presentation

Word/time limit: 3000/ 15 minutes

File type : Single Word (MS Word) or PDF file for Option 1, or Narrated PowerPoint slides for Option 2.

Percentage of final grade: This assignment is worth 100% of your final grade for this module.

Submission deadline: See module iLearn page for date of submission (The submission portal on ilearn will close at 14:00 UK time on the date of submission).

Grade release: You will normally receive your provisional grade and feedback within 20 working days of the submission deadline

Useful terms:

Learning outcomes (LOs):  The skills and knowledge that you should be able to show in your work

Rubric: A set of rules or guidelines used to grade or assess work

Task summary:

Your organisation is facing a problem. Your manager has decided that the problem can be solved by analysing a data set. You have been asked to do the data analysis and report your findings and recommendations.

For the data set, choose one of two options.

  1. Use a data set from an organisation you are involved with.
  2. Use a data set provided for on iLearn.

An “organisation you are involved with” could be your employer but does not have to be.

You MUST use data that already exists. You MUST NOT create new data, because you don’t have ethical approval to collect new primary data.

If you choose to use a data set from your organisation, you MUST have the consent of a manager in the organisation. You are expected to anonymise the provided data to remove all information that identifies the company.

If you are using a data set from your organisation, you must construct a scenario which contains the problem, along with a question that can be answered by analysing the data set. You may ask colleagues, such as your manager, for ideas about a suitable scenario.

An example of a business problem is falling revenues from sales. The question could be: “Why are sales falling? The data set to analyse could be the last year of unit product sales for all product lines across all channels.

If you are not currently with a suitable organisation, or you have no access to a data set which you can analyse, please use one of the datasets provided on iLearn.

Assignment tasks:

Part 1

Summarise and characterise the problem that needs a solution through data analysis. Formulate a question that will be answered through analysis of the data set. (LO2) (25 marks)

Part 2

Critically analyse the methods that appear to have been used to gather the data. You may make assumptions. For example, you could assume that the data set was created from a survey of customers, so you could critically analyse the survey design, execution, and sampling. (LO1) (25 marks)

Part 3

Execute appropriate data analytics activities to answer the question you created in Part 1, and present your findings and recommendations based on your analysis. (LO3, LO4, LO5) (50 marks)

Notes

Because of the nature of the assignment, your report may contain lots of numerical information. Such numbers do NOT count to the word quota. You may also put numerical data in appendices, which also do not count to the quota.

End of questions

Assignment instructions:

As part of the formal assessment for the programme you are required to submit a Data Analytics and Management assignment. Please refer to your Student Handbook for full details of the programme assessment scheme and general information on preparing and submitting assignments.

For this assignment, you must choose one dataset from the list of datasets on iLearn or use a dataset from an organisation you are involved with.

For this assignment, you must produce either:

Option 1: A 3000 word written report, or

Option 2: A 15-minute narrated PowerPoint presentation.

Option 1 – 3000 word Written Report Guidelines

Maximum word count: 3000 words

Submission file type: MS Word Document or PDF file.

Option 2 – 15-minute Narrated PowerPoint Slides Guidelines

Your assignment should include: a title slide containing your student number, the module name, the submission deadline and the exact number of your submitted slides; the appendices if relevant; and a reference list in AU Harvard system(s).

You must not include your name in your submission because Arden University operates anonymous marking, which means that markers should not be aware of the identity of the student. However, please do not forget to include your STU number.

You are asked to produce an audio narrated PowerPoint presentation that covers all the assignment tasks above and fulfils all the Learning Outcomes below. Please note that tutors will use the assessment criteria set out below in assessing your work.

Recording time: 15 minutes

Maximum slides: 15 slides (Minimum slides – 12 slides)

Submission file type: Audio narrated MS PowerPoint. Please do not submit audio or video format files.

Use a good combination of text, data, and visuals.

Narrate the slides – add audio recordings for each slide explaining the content of the slides in answer to the assignment tasks (At least 1 minute recording per slide).

The slide count excludes title and reference slides.

Learning outcomes (LO)

By completing this assessment, you will have shown and be assessed on all five learning outcomes:

  1. LO 1: Analyse methods of gathering data and their value related to a specific problem.
  2. LO 2: Assess a business domain problem.
  3. LO 3: Demonstrate appropriate analytical methods based on the dataset and identified problem to be addressed.
  4. LO 4: Make appropriate recommendations based on the findings of analytical activities
  5. LO 5: Discipline Expertise: knowledge and understanding of chosen field. Possess a range of skills to operate within this sector, have a keen awareness of current developments in working practice being well positioned to respond to change.

You will be graded based on how well you meet these learning outcomes. Your marker will use a rubric to grade your work, and you can find this on the “My Assessment” tab on the module iLearn page. A copy is also provided below.

Guidelines and policies

You can find links to more useful information about the assignment and university policies below.

Word/time limit policy

Click here to view the Arden University word count/time limit policy

Referencing guidelines

Click here for Harvard referencing guidelines

Please follow the referencing guidelines that are appropriate for your degree programme. If you are unsure which you should be using, please contact your module team.

 

Academic integrity and misconduct policy

Click here to view Arden University’s policy on academic integrity and misconduct

Statement on use of artificial intelligence on assessment

Click here to view Arden University’s statement on the use of artificial intelligence on assessment

Support information

Click here to view guidance on how to apply for short-term extensions

Click here to view guidance on how to apply for extenuating circumstances

Please click here for link to academic skills team support

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All About BUS5015 Data Analytics and Management Assignment

BUS5015 Data Analytics and Management is a 3,000-word assignment where you use a real dataset to solve a business problem, critique how the data was collected, run analysis and present clear findings and recommendations.

BUS5015 Data Analytics and Management is an assignment that checks how well you can turn data into decisions. You choose or are given a dataset linked to a real problem in an organisation. You first explain the problem and write one clear research question. Then you discuss how the data was gathered and how reliable it is. After that, you apply suitable data analysis methods, interpret the results and finish with practical, evidence-based recommendations for the business.

Sample Answer

BUS5015 Data Analytics and Management – Data Analysis Report
Organisation: Horizon Telecom UK Ltd (hypothetical)

Part 1 – Problem definition and data analytics question (LO2)

1.1 The organisation and context

Horizon Telecom UK Ltd is a hypothetical medium-sized telecommunications provider based in Birmingham. It offers:

  • Mobile phone contracts

  • Home broadband packages

  • SIM-only deals

Customers are based across the UK. The company sells online, through its own retail stores, and via comparison websites.

Over the last 12–18 months, Horizon has seen a noticeable rise in the number of customers cancelling their contracts before or at renewal. This is known as customer churn. Management are worried because:

  • Acquiring new customers is more expensive than keeping existing ones.

  • High churn makes revenue less predictable.

  • Competitors are offering aggressive discounts and bundles.

Horizon already collects large amounts of data about customers and their behaviour. This includes:

  • Customer ID, age band, region, acquisition channel

  • Contract type (SIM-only, handset + contract, broadband)

  • Contract length (30-day rolling, 12-month, 24-month)

  • Monthly bill amount

  • Data usage band (low, medium, high)

  • Number of calls to customer service and number of complaints

  • Whether the customer paid on time or had late payments

  • Tenure (how many months they have been a customer)

  • A churn flag (1 = customer left in last 12 months, 0 = still active)

This data is stored in the company’s CRM and billing systems and is available as an anonymised dataset for analysis.

1.2 Business problem

The problem is:

Horizon Telecom is experiencing a rising customer churn rate, which is threatening revenue, growth, and market share.

In simple terms: too many customers are leaving, and the company does not clearly understand why.

This matters because:

  • Lost customers mean lost monthly income.

  • High churn reduces the lifetime value of each customer.

  • Marketing spend is wasted if new customers leave quickly.

  • Competitors can use low prices and attractive bundles to “steal” dissatisfied customers.

1.3 Why data analysis is needed

Different managers have different opinions about why customers are leaving. Some blame prices, some blame network quality, and others blame poor customer service. Opinions alone are not enough.

Because Horizon already holds a detailed customer dataset, it is possible to:

  • Measure churn rate across different customer groups

  • Compare churn for different contract types, regions, and price levels

  • Identify which factors are most strongly associated with churn

Data analysis therefore gives a fact-based way to understand the problem and to design targeted actions.

1.4 Main question and sub-questions

The main analytics question is:

Which customer and contract factors are most strongly associated with churn at Horizon Telecom UK Ltd, and how can the company use these insights to reduce churn?

From this, we can form sub-questions:

  1. Does churn differ by contract type (SIM-only vs handset vs broadband)?

  2. Is churn higher for short contracts (30-day rolling) than for longer contracts?

  3. Are customers with more complaints or service contacts more likely to churn?

  4. Does monthly spend or late payment behaviour affect the likelihood of churn?

  5. Are there any regional patterns in churn?

The rest of the report will use the existing Horizon dataset to explore these questions and provide recommendations.

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