Module Code: H9AEBDM
Long Title HR Analytics and Evidence based Decision Making
Title HR Analytics and Evidence based Decision Making
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 10
Module Coordinator: COLETTE DARCY
Module Author: Isabela Da Silva
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 Understand the role of data in demonstrating return on investment (ROI) of HRM strategies and initiatives such as L&D, recruitment, reward etc.
LO3 Critically evaluate how Information Human Resource Systems (IHRS) systems can be implemented in organisations to improve organisational effectiveness.
LO4 Critique the concepts & theories underpinning data and analytics, design & development, evidence-based practice and critical decision-making.
LO5 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.
LO6 Understand the changing technological developments in HR and other business functions. How technology and analytics impacts upon the strategy, design and practice of both HR and the business and how the variety of technological solutions can enable enhance the HR function and its initiatives.
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

There are no additional entry requirements for this module.  The programme entry requirements apply.  No pre-requisites or co-requisites apply.   


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 HRM performance metrics i.e., L&D, performance, workforce planning etc.
Data Overview
Understanding 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 Understand the theoretical concepts of big data, data mining etc. Understanding of 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 Implementation of HR metrics for strategic and operational purposes
HR analytics and evidence-based decision-making
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 Examination of data visualisation methods to report for impact
Information Human Resources Systems (IHRS)
Types of IHRS Relevance of IHRS to the organisation and the business strategy How to project plan the implementation of an IHRS Advantages and disadvantages of IHRS
Assessment Breakdown%
End of Module Assessment40.00%


Full Time

Assessment Type: Project % of total: 60
Assessment Date: n/a Outcome addressed: 1,2,3,4,5,6
Non-Marked: No
Assessment Description:
2,000-2500 word assignment pertaining to HR analytics.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 40
Assessment Date: End-of-Semester Outcome addressed: 1,2,3,4,5,6
Non-Marked: No
Assessment Description:
Learners are required to complete an unseen three-hour examination where they answer three questions from a total of five.
No Workplace Assessment

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 36 Per Semester 3.00
Independent Learning Independent Learning 178 Per Semester 14.83
Directed Learning Directed e-learning 36 Per Semester 3.00
Total Weekly Contact Hours 6.00

Module Resources

Recommended Book Resources
  • Khan, M., Milliner, D.. 2019, Introduction to People Analytics, A practical guide to data-driven HR, Kogan Page. Available in the NCI library on eBook and hard copy.
  • Marr, B. 2018, Data-driven HR: how to use analytics and metrics to drive performance. London: Kogan Page. Available in the NCI library on eBook and hard copy.
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.
  • Kahneman, D.. 2011, Thinking fast, thinking slow. London: Penguin.
  • 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.
  • Mattox, J.R., Parsky, P. and Hall, C. 2020, Learning analytics: using talent data to improve business outcomes. 2nd ed. London: Kogan Page.
  • Taylor, D.H. (2017) Learning technologies in the workplace: how to successfully implement learning technologies in organizations. London: Kogan Page.
This module does not have any article/paper resources
Other Resources
Discussion Note: