Module
Strategic and HR Analytics
Module description
In this module you will deepen your understanding of how analytics can be used to develop business strategies. In addition, the module will cover how analytics can be used from a HR perspective and in particular the use of machine learning in HR analytics. Machine learning methods can be used to analyse large data sets and are an important part of the toolkit of HR managers. You will learn how to convert unstructured text to structured data and then analyse it using various dimension reduction techniques. Key concepts taught include pattern recognition, classification, categorisation, and concept acquisition. You will also learn about deep learning and neural networks.
Full module specification
| Module title: | Strategic and HR Analytics |
|---|---|
| Module code: | BEM3063 |
| Module level: | 3 |
| Academic year: | 2023/4 |
| Module lecturers: | |
| Module credit: | 15 |
| ECTS value: | 7.5 |
| Pre-requisites: | BEM1024 OR BEE1022 OR BEE1025 OR BEA1012 AND BEM1025 |
| Co-requisites: | |
| Duration of module: |
Duration (weeks) - term 2: 12 |
Module aims
It is increasingly necessary to be able to provide data driven facts and use them effectively in order to proactively address critical HR and performance issues and strategically support the business. We will cover how data driven HR analytics and metrics provide information that helps drive strategic business decisions and deliver competitive advantage over competitors. We will cover how key HR deliverables are identified, how data analytics can be used to solve HR problems, how strategic performance frameworks and measurement systems are designed and implemented, and how different metrics, benchmarks, indicators, research and gathering techniques, analysis and reporting can be used to support decision-making. Key concepts, theory and best practice related to workforce psychology and analytics will be discussed, including text analysis, machine learning and network analytics.
ILO: Module-specific skills
- 1. identify and apply appropriate analytics methods and tools to a range of business situations;
- 2. demonstrate knowledge and understanding of fundamental, and domain-specific, analytics methods and tools;
- 3. create, manage, interrogate, interpret and visualise data from a wide range of different sources, types and including structured and unstructured forms.
ILO: Discipline-specific skills
- 4. critically analyse the use of data within a business context, identifying strengths and limitations.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 36 | 114 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching Activity | 36 | Lectures, seminars and workshops |
| Guided Independent Study | 60 | Guided reading and preparation for lectures |
| Guided Independent Study | 54 | Assignment preparation |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| In class discussions | In class | 1-4 | Verbal |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Group case study | 30 | 1250 words | 1-4 | Written |
| Individual report | 70 | 2500 words | 1-4 | Written |
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Group case study | Individual reflective report (30%) | 1-4 | August/September Reassessment Period |
| Individual report | Individual report (70%) | 1-4 | August/September Reassessment Period |
Syllabus plan
The following content will be covered in an integrated fashion throughout the course
- Defining business strategy and building the case for HR as a strategic function
- Identifying the HR architecture and key HR deliverables
- Recognising how HR analytics supports business decisions and goals
- Designing and implementing strategic performance frameworks and measurement systems that are aligned with business strategy
- Understanding workforce psychology and how data analytics can be used to solve HR problems
- Using a systematic approach to collecting, analysing and interpreting data right for the organisation and industry
- Building and improving measurement systems in order to gain significant business insights, for example, using descriptive and diagnostic analytics, benchmarking, and balanced scorecards
- Leveraging information technology to record, retrieve and report on HR information and convert data, metrics, benchmarks and indicators into strategic decision–making information
- Ethics, regulation and governance in HR analytics
- Introduction to machine learning in HR analytics
- Using text based data to generate insights
Indicative learning resources - Basic reading
Guided reading will be provided via the module ELE pages, the type of information that you might be expected to review includes:
Isson, J. P; Harriot, J.S & Fitz-enz, J. (2016) People Analytics in the Era of Big Data: Changing the Way You Attract, Acquire, Develop, and Retain Talent. Wiley
Fink, A. A & Sturman, M.C (2017) HR Metrics and Talent Analytics. pp375 – 395 of The Oxford Handbook of Talent Management. Oxford University Press.
Module has an active ELE page?
Yes
Origin date
06/01/2020
Last revision date
10/03/2022