MN5816 Business Analytics in Practice Assignment Brief | RH

The Assignment

This assignment is a business analytics project requiring you to work with real-world datasets of your choice to develop Alteryx models to yield valuable insights and recommendations. Your objective is to construct models that include descriptive, diagnostic, and, ideally, predictive and prescriptive analytics using Alteryx (with embedded code if needed). In addition, you are required to compose a business report of approximately 2,000 words. This report must communicate and justify the processes and assumptions that underpin the development of your models, as well as discuss the insights and recommendations produced.

The final submissions must include:

The Business Report is your formal report presenting your analytics project. The template and guidance for this report have been provided.

The Artefact, which must be an Alteryx yxzp1 file: This is the final version of the models you created. Your models must be well annotated with comments that help the marker understand your logic and process.

MN5819 Proposal: This is the document you submitted for MN5819 Research and Consulting Methods via Turnitin. This document will NOT be marked, it will only be used for reference.

You are also required to maintain a Project Development Diary (see Section 1.4), which must be included as an appendix of your report.

This assignment is tightly linked to MN5819 Research and Consulting Methods and MN5821 Consulting Project (see Figure 1).

MN5816 Assignment Brief 2024-25 Business Analytics in Practice

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1.1 Assignment Checklist

The checklist below should be used to ensure that your assignment is within the remit.

1. Identify and select your primary datasets and define the project topic.

Ensure the uniqueness of your choice.
Maintain your Project Development Diary.

2. Initial Data Analysis & Story Formation.

Commence data cleaning, augmentation, and exploratory analysis.
Begin using analytical tools for preliminary descriptive, diagnostic, predictive, and prescriptive analysis. It should be noted that if data cannot be obtained to enable predictive and/or prescriptive analysis, you may document what data would have enabled this, as well as the methods and models you would have used if you had access to the required data.
Start crafting narrative outlines and initial data visualisations.
Develop the first draft of your business report.
Maintain your Project Development Diary.

3. Detailed Analysis and Report Drafting.

Conduct a more comprehensive data analysis.
Refine your data story and improve visualisations for clarity and impact.
Further refine your business report based on the more comprehensive data analysis carried out.
Maintain your Project Development Diary.

4. Complete Draft and Preliminary Review.

Complete a full draft of the report, including insights and preliminary recommendations.
Conduct a self-review and refine the draft for coherence and narrative flow.
Maintain your Project Development Diary.

5. Project Completion and Final Submission.

Perform final checks on the report, ensuring that all the requirements are met.
Compile and review the appendices and analytical models.
Complete and incorporate your Project Development Diary into your final report as an appendix.
Submit your final MN5816 project report, the final and fully annotated version of your Alteryx yxzp file, and your MN5819 Research and Consulting Methods proposal using the relevant Turnitin tabs. This must be done by noon on 6th May 2025. THREE Turnitin tabs are made available to you via Moodle.

1.2 Selecting a Topic

All topics and primary datasets chosen for your project are subject to your tutor’s approval. All data sources used and outputs created must comply with ethical standards.

This checklist can be used to methodically evaluate the feasibility and applicability of your chosen topic. This process will help you critically assess your proposed project, ensuring that it is fully aligned with the goals of the assignment and the broader MSc Business Analytics programme.

1. Relevance to Business Analytics

Confirm the topic addresses a meaningful problem or opportunity.
Validate that the topic allows for effective application of analytics.

2. Feasibility

Evaluate resource availability, including time, data, and tools (Alteryx).
Assess whether the project’s scope is realistic within the given timeframe.

3. Intellectual Challenge

Choose a topic that requires the application of advanced analytical skills.
Ensure the topic demands critical thinking and problem-solving.

4. Sustained Engagement

Select a topic that you are passionate about and that will maintain your interest.
Consider the topic’s relevance to personal and professional growth.

5. Balance of Theory and Practice

Check that the topic offers opportunities to apply theoretical concepts.
Plan for practical application and real-world testing of these concepts.

6. Accessibility of Information

Identify all required data sources and confirm access.
Obtain necessary permissions for proprietary or sensitive data. (NB. All formal permissions must be included as appendices to your final business report.

7. Skill and Knowledge Requirements

Map out the skills and knowledge needed for the project.
Plan for acquiring any skills or knowledge you currently lack.

Selecting a well-aligned and feasible topic sets the foundation for a meaningful and impactful learning experience, allowing you to demonstrate the breadth of your analytical and consulting capabilities.

1.3 Evidence of Analysis

Your final report must provide comprehensive evidence of your analytical journey. This evidence demonstrates your proficiency in applying the skills and knowledge acquired during your course to a commissioned or self-selected business or societal issue. Below is a detailed breakdown of the essential activities and tasks that must be completed and documented:

1. Dataset Selection and Preparation

Choosing Datasets: Select your primary datasets that may hold topical business or societal value.
Document the topic and datasets you have selected (see Section 1.2).
Data Preparation: Clean, complete, augment, format, and explore the data to prepare for thorough analysis. Ensure that your datasets are sufficiently robust to support your aims, providing the necessary depth and breadth.

2. Analysis and Interpretation

Employing Analytical Tools: Use Alteryx with your choice of data to perform descriptive, diagnostic, predictive, and prescriptive data analyses. Your report must showcase your ability to effectively develop and employ analytical models.
Narrating Data Stories: Develop and narrate cohesive data stories that clearly articulate the business problem or opportunity addressed. Your narrative should align with the objectives of the MN5816 module and your academic interests.
Data Visualisation: Create compelling and contextually relevant visualisations to communicate the findings clearly and engagingly (see Section 1.4).

3. Insights and Recommendations

Insight Identification: Document how you identified and discussed the insights gained from your analysis. What stories did the data reveal? What does the data reveal about the problem or opportunity you are exploring?
Strategic Recommendations: Offer tactical and strategic recommendations based on narrated data stories and insights. Your conclusions should provide actionable and insightful suggestions stemming from your thorough analysis.

4. Proficiency and Compliance

Demonstrating Proficiency: Throughout the project, demonstrate proficiency in the software, tools, techniques, and skills taught during the course. If undertaking a commissioned project, adhere to any stipulations set by the commissioning organisation.
Ethical and Appropriate Use of Artificial Intelligence (AI): If you opt to use AI tools for any part of your project, your report must include an explicit declaration of AI tool usage as an appendix. You must clearly articulate and reference any AI tool used in your report. Demonstrate that you have complied with RHUL AI requirements, Academic Misconduct, and using AI responsibly.

5. Documentation of Process

Articulating Assumptions and Justifying Choices: Clearly articulate all assumptions made and explain each decision made along with its justification and reasoning.
Addressing Challenges: Document each challenge experienced and how it was overcome or worked around.
Other Crucial Information: Include any other information that provides context, explanation, and/or clarification relevant to your project.
Your final report should not just detail the tasks completed but also reflect your analytical skills, your ability to weave a viable and compelling data story, your communication skills (both verbal and visual), your creativity, and your journey as a business analyst.

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1.4 Data Visualisations

Data visualisation is an essential aspect of your business report, playing a crucial role in clarifying, supporting, and highlighting your analysis and findings. Rather than serving as mere additions, visual elements should be integrated throughout your report as key narrative tools. They turn complex data into clear, engaging insights. Thoughtfully placed visualisations within your report enhance comprehension and effectively engage the reader. Consider the following suggestions for strategically embedding data visualisations in various sections of your report:

1. Introduction:

Generally, visualisations in the introduction are minimal. However, a simple, high-level visual might be used to set the scene or present the context of your research, but only if relevant.

2. Trends and Theoretical Perspectives:

Trends: Use charts or graphs to depict any significant trends or patterns you have identified related to your topic. Visuals here can help illustrate shifts or developments over time.
Theoretical Perspectives: While this section is generally text-heavy, conceptual diagrams or models related to the theoretical frameworks discussed can be included to aid understanding.

3. Analytical Framework and Method:

Method Overview: Flowcharts or diagrams can be useful to outline your analytical process or framework, providing a visual summary of the steps taken and the methods used.

4. The Data:

Dataset Overview: Incorporate visuals such as sample data snapshots, infographics, or simple charts to represent the structure, source, and nature of the data.
Data Quality and Preparation: Before-and-after visuals can effectively show cleaning, transformation, or imputation changes in your data.

5. The Analysis – This section is the heart of data visualisation in your report:

Descriptive Analysis and Diagnostic Analysis: Use charts, graphs, and maps to present the data’s distribution, trends, relationships, or summaries. For example, bar charts, line graphs, histograms, and scatter plots, as well as other visualisations such as choropleths and infographics.
Predictive Analysis: Visuals may include model summaries, performance charts such as ROC curves, or predictions visualised against actual values.
Prescriptive Analysis: If applicable, decision trees, flow diagrams, or scenario visualisations can help illustrate the outcomes or strategies suggested by your analysis.

6. Findings and Recommendations:

Findings: Here, visuals play a crucial role in emphasising the insights gleaned from your data. Use clear and impactful charts or graphs to highlight the key points and ensure that they stand out.
Recommendations: Visuals may include diagrams or models that represent the proposed actions, their potential impacts, or flowcharts showing the steps for implementation.

7. Conclusion:

While the conclusion is typically a summary text, a powerful final visual (like an infographic summarising the key findings or a diagram of the proposed recommendation framework) can leave a lasting impression.

Ensure that each visualisation has a clear purpose, is referenced and explained in the text, and is well- labelled and accessible. Include an interpretation linking them to the narrative of your report In your

Development Diary; note instances where a visualisation clarified your understanding or when a specific chart or graph effectively presented your data, adding depth to your analytical journey’s narrative.

1.5 Development Diary

The Development Diary is an essential element of this project, functioning as a personal chronicle of your journey. It is designed to capture pivotal moments, critical decisions, fundamental assumptions, and any challenges encountered along the way. This diary is your space to meticulously record every assumption you make, every decision and choice along with their justifications and reasoning, every obstacle faced, and how you navigated or circumvented it. Regular updates are crucial for tracking the development of your project and your analytical thought process. This consistent documentation provides a valuable narrative for learning and development throughout the project. This diary will not only illustrate your progress but also act as a crucial reference when compiling your final report.

Below is a table outlining the types of entries you might make, with clear explanations and relevant examples to guide you in chronicling your project experience.

Code	Definition	Examples
A	Assumption: A belief or statement taken to be true with limited evidence.	⁻ Assuming a certain customer demographic will respond positively to a new product based on market trends. ⁻ Presuming that historical sales data can predict future trends.
D	Decision: A conclusion or resolution reached after consideration.	⁻ Opting to use a linear regression model based on the data’s characteristics. ⁻ Choosing to focus on a particular dataset after evaluating its relevance and completeness.
C	Challenge: Any obstacle or issue encountered during the project.	⁻ Encountering missing data and deciding to use imputation techniques. ⁻ Dealing with unexpected results in data analysis and revisiting the methodology to ensure accuracy.
M	Miscellaneous: Any other observations or experiences not covered above.	⁻ Noting a particularly useful resource or piece of advice. ⁻ Reflecting on a personal learning moment or a change in perspective during the project.

Remember that the more detailed and consistent your entries are, the more valuable the Development Diary will be as a resource for understanding and improving your project approach. Your diary entries are likely to be simple notes written in the first person. These entries are only for your own use, and should not be repeated verbatim when writing your report.

A template for the Development Diary will be available via Moodle.

1.6 Risk of Relying on AI Tools

Artificial Intelligence (AI) tools can aid in productivity and creativity; however, they are not without risks. The most obvious risk is the potential breach of academic integrity standards (see the Generative AI page on the College’s website).

In addition to these concerns, using AI to generate academic or business content comes with significant risks, notably the phenomenon known as “hallucination” where the AI fabricates information or presents false data. This can be particularly problematic in academic and business contexts where accuracy and credibility are paramount. AI systems, although sophisticated, do not discern truth from fiction; they generate responses based on patterns in the data on which they have been trained. This means that AI can confidently present incorrect or misleading information as factual. The likelihood of this occurrence is not trivial, particularly when the topics are obscure, complex, or outside the AI training range.

In academic settings, reliance on AI-generated content can lead to the submission of work that contains inaccuracies, potentially undermining the integrity and credibility of your work. In the business world, decisions based on incorrect information can have severe financial and reputational consequences. Furthermore, the uncritical use of AI content can lead to the homogenisation of thought, as diverse perspectives and critical thinking are sidelined in favour of AI’s often predictable outputs.

Therefore, while AI can be a powerful tool to generate ideas and help with narrative flow, it is crucial for users to critically evaluate and verify information, understand inherent risks, and take responsibility for the final content. This approach ensures that the final output is not only original, but also accurate and reliable, maintaining the integrity of academic or business endeavours.

If you have ethically employed any AI tool for your analysis, you must declare which tool you used (name and version), why and how it was used, and the specific content in your report that was developed with the aid of AI tools. You must provide this information by completing the declaration of AI tool usage form in this document and including it as an appendix in your final submission.

Writing The Report

2.1 Length, File Name and Text Format

The word limit for this report is 2,000 words. References, tables, and figures are not included in the word count.
Please include your student ID and the total word count on your submission’s cover page.
Please name your assignment’s electronic file as follows: Mn5816_candidate id
All texts should be Times New Roman, with sizing and style as follows:
Main text  12pt
Heading level 1  14pt bold
Heading level 2  13pt bold
Heading level 3  12pt bold
The text, not in captions or tables, must be left-justified with 1.5 line spacing.

2.2 Report Structure

Your final report should be structured to facilitate a comprehensive and coherent presentation of your work, ensuring that every relevant aspect is articulated effectively. Your Development Diary, a meticulous record of the evolution of your project, will be instrumental in providing the depth and detail required for each section. Below is a detailed breakdown of the sections expected in the report, with pointers on how your diary entries can enrich your narrative.

4. Introduction, this section covers the following aspects:

Context and Significance: Describe the background/environment and importance of your chosen topic. Here, you can use your diary entries, where you explored and justified your choice of topic.
Scope and Purpose: Define the boundaries and aims of your project. Here, you can utilise your diary notes on the purpose of your analysis and the questions you sought to answer.
Assumptions: Make explicit the initial assumptions you made about the topic or project, as recorded in your Development Diary, and explain how these assumptions shaped the direction and scope of your research.
Report Structure: Provide a roadmap for the layout of your report.

5. Trends and Theoretical Perspectives, this section should cover:

Current Trends: Analyse recent developments related to your topic. Here, you can use your diary entries, where you have noted emerging patterns or shifts in the field.
Theoretical Foundations: Discuss relevant theories and frameworks. Here, you should consider your diary reflections on key readings and how they shaped your understanding.
Critical Understanding: Critically discuss the various theoretical perspectives you have considered. Here, your diary insights, where you have critiqued or supported various theoretical perspectives, will help you shape a deeper analysis.
Assumption: Examine how your theoretical perspective may have been influenced by the assumptions noted in your diary. Consider whether and how these assumptions were challenged or changed based on the research findings.

6. Analytical Framework and Method: This section should cover:

Analytical Approach: Detail the methodologies you have employed. Here, you can use diary entries about your decision-making process and any methodological adjustments.
Scope and Focus: Clarify the extent and concentration of your analysis. Your diary extracts highlighting how and why you narrowed your focus should be of help.
Ethical Considerations: Discuss the ethical aspects of your work. Your diary records of ethical dilemmas and how you addressed them will be helpful.
Assumptions: Discuss any assumptions that underpin your choice of methodology. Use your diary to reflect on how these have been validated, challenged, or modified throughout your analytical journey.

7. The Data, this section should cover:

Dataset Description: Describe the data used. Your diary entries on how you selected and sourced your datasets will help you.
Relevance and Preparation: Explain the significance and steps taken to clean and prepare the data. Your diary notes on any challenges faced and how they were resolved should be of help.
Rationale: Justify your choices. Here, you can use diary records of where you deliberate on various options and their implications.
Assumptions: Detail any assumptions made about the data’s validity, relevance, or limitations as documented in your diary. Reflect on how these assumptions were confirmed or adjusted after data collection and analysis.

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