BAE_6_BIN Business Intelligence Coursework 2 Assignment Brief 2025-26 | LSB
BAE_6_BIN Business Intelligence Coursework 2 Assignment Brief
Learning Outcomes
This assignment assesses the following module learning outcomes. On completion of the module, you will be able to:
Knowledge and Understanding
- Demonstrate a clear understanding of the techniques used in business intelligence and to compare their strengths and weaknesses.
Intellectual Skills
- Distinguish between and evaluate the appropriateness of the various data techniques that can be used to support the solving of business problems.
Practical Skills
- Interpret and manipulate data using current software e.g. Tableau.
Transferable Skills
- Undertake problem analysis and problem-solving.
Assignment Overview
Assessment Type: Individual Report
Assignment Title: “Business Intelligence Case Study: Evaluating Emerging Technology Implementation”
The Business Intelligence module aims to develop students’ skills in applying business intelligence techniques to analyse and derive actionable insights for organisations. This individual report requires you to analyse a case study examining how an organisation could implement or has implemented an emerging technology to enhance its business intelligence capabilities.
You will use concepts from the module to analyse the case study organisation and make strategic recommendations regarding their BI technology adoption. You should use BI software (e.g., Tableau, Power BI, SPSS, or Excel) to create visualisations that support your analysis and enhance the interpretability of your findings.
The report should be 3,000 words, excluding references. All relevant literature and resources should be properly cited using the Harvard referencing style.
Select ONE Case Study Focus
Choose one of the following case study scenarios. Each focuses on how emerging technologies are transforming business intelligence in different contexts:
| Case Study | Scenario & Focus |
|---|---|
| Case A: AI-Powered Retail Analytics | A major UK retailer is considering implementing AI and machine learning to enhance their BI capabilities for demand forecasting, customer segmentation, and personalised recommendations. Analyse how AI could transform their traditional BI approach and recommend an implementation strategy. |
| Case B: Blockchain in Supply Chain | A global logistics company wants to use blockchain technology to improve data integrity, transparency, and real-time tracking across their supply chain BI systems. Evaluate how blockchain could enhance their BI capabilities and address current data quality challenges. |
| Case C: Real-Time Healthcare BI | An NHS Trust is exploring real-time analytics and IoT integration to improve patient flow management, resource allocation, and predictive health monitoring. Analyse how streaming analytics could transform their decision-making capabilities. |
| Case D: Generative AI in Financial Services | A financial services firm is evaluating how Large Language Models (LLMs) and generative AI could automate report generation, enable natural language queries to databases, and enhance their BI accessibility. Assess the opportunities and risks of this approach. |
| Case E: Own Case Study | You may propose your own case study focusing on an organisation and emerging technology of your choice. This must be approved by the module tutor before commencing work. Contact Dr Gift Kugara with your proposal. |
Assignment Structure & Marking Criteria
| Weightage | Section & Details |
|---|---|
| 20% | Section 1: Introduction and Case Study Context (~1,000 words) Introduce the case study organisation and the business problem to be addressed:
|
| 15% | Section 2: Data Processing and Exploration (~500 words) Select and explore a relevant dataset for your case study analysis:
|
| 25% | Section 3: Business Intelligence Techniques and Interpretation (~800 words) Apply BI techniques to analyse the case study, demonstrating how emerging technology enhances traditional approaches:
|
| 20% | Section 4: Data Insights and Recommendations (~700 words). Provide insights drawn from your analytics and make recommendations for the case study organisation:
|
| 20% | Section 5: Writing, Styling, and References Professional academic standards throughout:
|
Recommended Data Sources
You may select datasets from the following sources or propose your own (subject to tutor approval):
- Kaggle: kaggle.com/datasets – extensive collection for various industries
- UK Government Data: data.gov.uk – public sector datasets including NHS data
- Office for National Statistics: ons.gov.uk – UK economic and social data
- Tableau Public Datasets: public.tableau.com/app/resources/sample-data
- World Bank Open Data: data.worldbank.org – global development indicators
- Google Dataset Search: datasetsearch.research.google.com
Submission Instructions
This assignment is to be submitted electronically.
- This assignment must be submitted electronically by 5:00pm on the submission date.
- To submit electronically you must upload your work to the e-submission area within the respective module on Moodle.
- Multiple drafts can be submitted up to the submission date.
- Please remember you must leave at least 24 hours between submissions if you make changes to your work. Each submission will overwrite the previous one until the due date and time has passed.
- You are reminded of the University’s regulations on cheating and plagiarism. In submitting your assignment, you are acknowledging that you have read and understood these regulations.
- You are reminded that it is your responsibility to keep an electronic copy of your assignment for future reference.
- Your citation needs to follow the Harvard style referencing.
- Once you identify your case study and dataset, please contact the module leader/tutor before conducting your project.
Assessment Criteria
Your work will be assessed on:
- Selection of a relevant and challenging case study
- Thorough data exploration and preprocessing
- Application of appropriate business intelligence techniques
- Clarity and quality of visualisations
- Interpretation of analysis results and impact assessment
- Quality of documentation and reporting
- Reflection on the challenges and limitations of the analysis
Each task is being marked according to the rubric available on the VLE for this module.
