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:BEMM464
Module level:M
Academic year:2021/2
Module lecturers:
  • Professor Thomas Birtch - Convenor
Module credit:15
ECTS value:



This module is closed to MSc Business Analytics only



Duration of module: Duration (weeks) - term 3:


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. P2: Demonstrate knowledge and understanding of key business processes and structures, and the role of business analytics in decision support.
  • 2. P3: Critically analyse and discuss current issues and influences relevant to the ongoing development of business analytics, and its application.
  • 3. P4: Draw on knowledge of current research and practice to identify and apply appropriate analytics methods and tools to a range of business situations.
  • 4. P5: 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

  • 5. P6: Critically analyse the use of data within a business context, identifying strengths and limitations.
  • 6. P8: Work with stakeholders from a range of backgrounds to identify the need for, design, develop, and deploy, analytics solutions within a business environment
  • 7. P10: Contribute effectively to managerial decision processes within a business context.

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching Activity36Lectures, seminars and workshops
Guided Independent Study60Guided reading and preparation for lectures
Guided Independent Study54Assignment preparation

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
In class discussionsIn class1,2,3,5Verbal

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Group case study301250 words1,2,3,6Written
Individual report602500 words1-5,7Written
Participation10In class7Written and verbal

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Group case studyIndividual reflective report (30%) 1, 2, 3, 6Summer reassessment period
Individual reportIndividual report (70%)1 – 5, 7Summer reassessment period

Re-assessment notes

Re-assessment will be in nature to the original assessment, but the topic, data, and materials must be new.

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

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., & Harriott, J. S. (2016). People analytics in the era of big data: Changing the way you attract, acquire, develop, and retain talent. Hoboken: Wiley.
  • Fink, A. A., & Sturman, M. C. (2017). HR metrics and talent analytics. In D. G. Collings, K. Mellahi, & W. F. Cascio (eds.), Oxford handbook of talent management (pp. 375-395). Oxford, UK: Oxford University Press.

Module has an active ELE page?


Origin date


Last revision date