HRM40940 Data Analytics for HRM Individual Assignment 2026 | DCU

HRM40940 Individual Assignment

This assignment offers you the opportunity to demonstrate your ability to perform people analytics supported by the use of generative AI and traditional analytics programs. You will develop and submit a professional analytical report and dashboard that showcases your technical capabilities, demonstrates clear analytical insight, and critically reflects on the tools, techniques, and ethical considerations involved in analyzing workforce data.

The dataset provided for this assignment simulates real-world Human Resource Management (HRM) data. It has been deliberately designed to include common data quality issues, such as missing values, formatting inconsistencies, and data-entry anomalies. As part of your task, you are required to identify and resolve these issues, conduct a series of descriptive, inferential and advanced analyses, generate meaningful HRM insights, and illustrate these insights through a dashboard. You will also reflect critically on your use of generative AI tools in the analysis process, including the ethical and practical implications of using AI in HRM decision-making.

You have been presented with two stakeholder challenges, each representing requests that a People Analyst may receive from across the business. You are required to address both challenges and develop your analysis around them. Specifically, your report should demonstrate that you have chosen appropriate variables, applied suitable analytical techniques, and generated insights relevant to the issue presented, responding to each selected stakeholder’s concern.

Stakeholder Challenges

1. Director of Employee Experience and Well-being – “Employee engagement and wellbeing have been declining across the organization. We are not exactly sure what is driving this drop. Can you investigate the data and help us figure out what might be going on and what we could do to improve engagement and well-being across the organization?”

 2. Head of People Operations – “We are getting more reports of stress, burnout, and increased sick leave in some teams. There is a general sense that people are struggling, but we are unsure why or where this is concentrated. Can you investigate the data and help us figure out what might be contributing to increased absenteeism?”

Report Outline

Students are required to submit an individual analytical report that follows a clear structure as outlined below:

The report should begin with a concise introduction outlining the purpose of the analysis and the two stakeholder challenges it addresses. This section should also present your overall approach to using both generative AI and statistical software, the rationale for selecting the chosen analysis techniques, and how they best support addressing the two stakeholder challenges.

The second section should detail the data cleaning and preparation process. Here, you should describe the issues identified in the dataset, the steps taken to resolve these, and how generative AI may have been used to support this process. You should clearly document any prompts used, responses received, and decisions made in consultation with AI tools. Overall, this section should reflect a professional approach to data integrity and preparation for further analysis.

The third section should focus on your descriptive and inferential analysis. This is the core analytical component of the report and should demonstrate your ability to select appropriate variables, apply suitable analytical techniques, and interpret the results in a way that directly addresses the stakeholder challenges you have chosen.

1. Descriptive Analysis

Begin by summarising the key patterns and trends relevant to your selected challenges using measures of central tendency and HRM metrics where appropriate. Depending on the focus of your analysis, this may include metrics such as engagement scores by demographic group, performance distributions, absence patterns, or headcount. The choice of metrics should be explicitly linked to the questions raised in the stakeholder prompts, and you should explain why each was selected and how it contributes to understanding the issue at hand.

2. Inferential Analysis

Following your descriptive analysis, you are then required to conduct inferential analysis to deepen your understanding of relationships within the data. Specifically, you are required to conduct a correlation analysis to better understand the associations among variables as they pertain to the two stakeholder challenges.

3. Regression Analysis

You are also required to conduct a minimum of two forms of regression to deepen your understanding of relationships within the data for each stakeholder challenge.  

This must include:

• One simple regression model to investigate predictive relationships or to explain variation in a key outcome.
• One multiple regression model to investigate predictive relationships or to explain variation in a key outcome.

You may provide additional regressions relevant to each stakeholder challenge.

The choice of method and variables should also be driven by the stakeholder problem you are responding to. You must clearly justify:

• Why did you select specific dependent and independent variables?
• How does your model address the stakeholders’ concerns?
• What assumptions did you consider in conducting your analysis?

All inferential and regression results should be reported using appropriate statistical outputs (i.e., R², beta coefficients, p-values) and interpreted in clear, accessible language that links back to your stakeholders’ needs. Throughout this section, you are encouraged to show how generative AI supported your decisions and provide a brief evaluation of its accuracy, usefulness, or limitations in the analytical work.

4. Advanced Analysis

Finally, you must also perform analysis using natural language processing to deepen your understanding of the relationships within the unstructured data obtained from employee feedback and comments.  

The fourth section should showcase data visualization and dashboarding. Using Excel, JASP, Looker Studio, or AI-supported tools, you should create a summary dashboard that visually communicates your descriptive statistics and key findings. The dashboard should reflect best practices in data storytelling, including clarity, simplicity, and relevance, and utilize tables, charts, or other visuals to communicate insights clearly where appropriate. It should enable a nontechnical HRM stakeholder to easily interpret core insights related to the stakeholder challenges you have addressed

You may include screenshots of various elements of your dashboard in the body of the report, accompanied by brief explanations of each visual element and its relationship to your analytical findings. However, you must also submit the complete original dashboard as an appendix to your report to facilitate a technical review of your visualization structure and design logic.

The fifth section should offer a critical reflection on the use of AI in your analytical process. In this section, you should evaluate how generative AI supported or hindered your understanding, what limitations or surprises you encountered, and how you navigated discrepancies between AI outputs and statistical tools. This section should also consider the ethical implications of using AI in HRM contexts, including issues such as bias, transparency, and accountability when working with employee data.

The report should conclude with a summary of your main insights, lessons learned, and final reflections on the future of AI in HRM. You may also wish to consider what competencies you now view as essential for analytics work in HRM and how this assignment has informed your approach to data-driven decision-making.

Referencing

While this assignment is practical in nature, it is also an academic piece of work. Accordingly, you are expected to critically engage with and reference key literature where appropriate. A reference list (using Harvard or APA style) must be provided. References do not count towards the overall word count of the report. Appendices are permitted, if required, and do not count toward the word count.  

Submission Requirements

• One report should be submitted per student.
• The final report should be typed, double-spaced, using 12-point Times New Roman font.
• References should be in Harvard or APA style.
• Please include a cover page with the following details: o Module Code & Title o Full name & Student ID  o Word count
• A comprehensive appendix should be provided, including all prompts and conversations with ChatGPT
• Signed AI declaration form

Total word count: 2,500 words (+/- 10%), excluding cover page, references, and appendices.

Individual Reflection should be submitted via Brightspace by 11:59 p.m. on August 21, 2026.