Module Code: H8HRA
Long Title HR Analytics 
Title HR Analytics 
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 10
Module Coordinator: COLETTE DARCY
Module Author: Michael Cleary-Gaffney
Departments: School of Business
Specifications of the qualifications and experience required of staff  
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Critically evaluate the major theories of HRM data and analytics and evaluate the importance of aligning HRM analytics to the wider organisational context and strategy.
LO2 Describe the role of data in demonstrating return on investment (ROI) of HRM strategies and initiatives such as L&D, recruitment, reward etc.
LO3 Ability to critique the concepts & theories underpinning data and analytics, design & development, evidence-based practice and critical decision-making.
LO4 Demonstrate how to translate data analysis and results into tangible predictive business applications i.e.: demonstrate the ability to use analytics to build the case for L&D and other HR initiatives.
LO5 Analyse the contemporary, emerging and changing technological developments in HR and other business functions.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements  
 

Module Content & Assessment

Indicative Content
Overview and purpose of HR analytics and data.
Types of HR metrics and data  Balanced Scorecards & KPIs  Strategic Workforce Planning  Strategy & data driven decision-making  Measuring performance & potential  Human Capital reporting  Linking Human Resources to ROI - financial HR, cost of absenteeism, L&D, turnover etc.
Defining Metrics
Evaluate and appraise different types of data, graphics and statistical measures and their appropriateness in a range of scenarios. Key areas include;  Descriptive analytics and use of multidimensional data  Predictive analytics  Prescriptive analytics  Understanding qualitative performance metrics i.e., L&D, performance, workforce planning etc.
Data Overview
Appreciate the importance of data integrity and quality  Use of various data sources - qualitative and quantitative, correlation and causation. Importance of consistency and reliability of data inputs for reporting  Practical techniques to assess the integrity of data and avoid common pitfalls  How to analyse data  Examine the theoretical concepts of big data, data mining etc.  Comprehend and critically review the General Data Protection Regulation (GDPR) and ethical issues concerning analytics
Role of analytics in HRM strategy
Building the business case for HR metrics  How to build support amongst stakeholders  Application of data analysis for business strategic goals
Examination of key HR analytics and data
How to examine, evaluate and provide insights from HR data in areas such as absenteeism, turnover, pay, legislation - gender pay gap, performance management, talent management, L&D, culture (staff surveys), employee demographics etc.  How to design a data system through case studies and practical examples
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment % of total: 100
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: No
Assessment Description:
Analysis of a case study of the student's choice. Evidence to be produced  This consists of a written submission requiring students to critically analyse the role of technology and HRM analytics to enhance organisational effectiveness and efficiency on an organisation of their choice. Students will be assessed on the basis of a 2,500 word assignment.
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: Yes
Assessment Description:
Formative assessment will be included by the provision of class case studies and short answer questions. Feedback will be provided individually or as a group in written and oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom and Demonstrations 32 Per Semester 2.67
Directed Learning Directed e-learning 6 Per Semester 0.50
Independent Learning Independent Learning 212 Per Semester 17.67
Total Weekly Contact Hours 3.17
 

Module Resources

Recommended Book Resources
  • Khan, M., Milliner, D. (2019) Introduction to People Analytics, A practical guide to data-driven HR, Kogan Page..
Supplementary Book Resources
  • Barends, E. and Rousseau, D. (2018) Evidence-based management: how to use evidence to make better organizational decisions. London: Kogan Page.
  • Ferrar, J. and Green, D. (2021) Excellence in People Analytics, How to Use Workforce Data to Create Business Value. London: Kogan Page.
  • Marr, B. (2018) Data-driven HR: how to use analytics and metrics to drive performance. London: Kogan Page..
  • Mattox, J.R., Parsky, P. and Hall, C. (2020) Learning analytics: using talent data to improve business outcomes. 2nd ed. London: Kogan Page..
  • Sclater, N. (2017) Learning analytics explained. Abingdon: Routledge.
Recommended Article/Paper Resources
  • Marler, J.H. and Boudreau, J.W. (2017) An evidence-based review of HR analytics. International Journal of Human Resource Management. Vol 28, No 1. pp3–26. S.
Other Resources
  • CIPD, (2019), People Analytics factsheet available at https://www.cipd.ie/knowledge/world-w ork/analytics/factsheet.
  • CIPD, (2018), Getting started with People Analytics – A Practitioners Guide available at: https://www.cipd.ie/knowledge/world- work/analytics/practitioner-guide.
  • CIPD. (2017) Human capital analytics and reporting: exploring theory and evidence. London: Chartered Institute of Personnel and Development. Available at: https://www.cipd.co.uk/knowledge/str ategy/analytics/human-capital-analytics- report.
  • CIPD/Workday. (2018) People analytics: driving business performance with data. London: Chartered Institute of Personnel and Development. Available at:  https://www.cipd.co.uk/knowledge/st rategy/analytics/people-data-driving-per formance.
  • CIPD (2016) In search of the best available evidence. Chartered Institute of Personnel and Development. Available at:  https://www.cipd.co.uk/knowledge/st rategy/analytics/evidence-based-decision -making.
  • Chartered Institute of Personnel and Development. CIPD Toolkits, http://shop.cipd.co.uk/shop/bo okshop/toolkits.
  • European Commission. Eurostat, http://ec.europa.eu/eurostat.
  • European Central Bank,http://www.ecb.int.
  • Central Statistics Office, http://www.cso.ie.
  • Economic and Social Research Institute, http://www.esri.ie/.
  • World Bank. Data, http://data.worldbank.org/.
  • Institute for Statistics Education, http://www.statistics.com/.
  • OECD. Data, https://data.oecd.org/.
Discussion Note: