7B014E - Digital HRM Analytics: from Insight to Action 01 Sep 2026 - 31 Aug 2032 | Version 0
Associated Module Information
| Module Code: | 7B014E | ||
|---|---|---|---|
| Module Title: | Digital HRM Analytics: from Insight to Action | ||
| Faculty: | Faculty of Business and Creative Industries | ||
| Faculty Group: | Business Management | ||
| Faculty Sub Group: | Business Management | ||
| Module Leader: | Linda Hamweemba | ||
| Module Team: | Tiru Madahar, Shehla Khan | ||
| First Intended Intake: | SEP 2026 | Final Year of Intake: | 2031 |
| Date Closed: | |||
| Credit Value: | 30 | Credit Level: | 7 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100085 - human resource management | ||
| HECOS Code Weighting: | 100 | ||
Document Version Information
| Version | 0 |
|---|---|
| Valid From | 01 Sep 2026 |
| Valid To | 31 Aug 2032 |
Module Aims
The main aims of the module are:
· Develop critical understanding of how digitalisation and people analytics reshape HRM strategy, decision-making and workforce engagement, linking integrated people data to individual and team performance and the ethical use of AI (fairness, privacy, bias, explainability).
· Equip students with practical skills in problem definition, data interpretation and ethical analysis, and in translating insights into action through objective setting, success measures and review cadences that enable evaluation of impact.
· Foster implementation-ready leadership capable of designing and leading responsible digital transformation that enhances people and organisational outcomes, while communicating decisions with influence and accountability
Content Summary
This module examines how digital transformation and people analytics reshape work and the HR role. Students learn to integrate data from HRIS, automation, AI and predictive models to frame questions, interpret patterns and inform decisions that improve individual and team performance, with attention to ethics, fairness, privacy and algorithmic bias.
Grounded in Control Theory, High-Performance Work Systems and Sustainability Theory, the module follows the full analytics value chain: problem definition, data governance, interpretation and implementation planning. Learners translate insights into clear objectives, success measures and review cadences, and manage risks (privacy, bias, explainability) when using AI-enabled insights.
A core feature is the Interpretive HR Management Simulation, where students practise strategic decision-making in a dynamic, data-rich environment and convert dashboard signals into action plans they can monitor over time. The essential text is The Power of People (Guenole et al., 2017), supported by weekly readings that build technical fluency and critical judgement.
By combining conceptual understanding with applied skills, students learn to use people data responsibly and creatively to drive ethical, inclusive and performance-enhancing outcomes in the digital workplace.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Guided Study | 10.5 |
| Summative Assessment | 60 |
| Scheduled Learning and Teaching | 56 |
| Independent Study and self-directed learning | 173.5 |
| Total Hours Selected | 116 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | Critically evaluate the role of digital technologies, analytics and AI in shaping HRM strategy and decision-making, including the ethical governance required when translating insights into behaviour change. |
| LO2 | Apply analytic and diagnostic frameworks to support strategic people decisions that create organisational and social value, designing implementation plans (objectives, success measures, review cadence) to enable and evidence impact. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Asynchronous Assessment | Student Choice 1 | • Applied Analytics Portfolio • Consultancy Dossier + Live Briefing • Analytics Product & Screencast | 0 | 5000 | 100 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Student Choice 1 | ✔ | ✔ | |