Module Code: |
H9AEBDM |
Long Title
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HR Analytics and Evidence based Decision Making
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Title
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HR Analytics and Evidence based Decision Making
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Module Level: |
LEVEL 9 |
EQF Level: |
7 |
EHEA Level: |
Second Cycle |
Module Coordinator: |
COLETTE DARCY |
Module Author: |
Isabela Da Silva |
Departments: |
School of Business
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Specifications of the qualifications and experience required of staff |
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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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. |
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).
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No recommendations listed |
Co-requisite Modules
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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.
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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
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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.
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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
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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
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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
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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
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Assessment Breakdown | % |
Coursework | 60.00% |
End of Module Assessment | 40.00% |
AssessmentsFull Time
Coursework |
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. |
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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. |
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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 |
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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.
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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 |
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Barends, E. and Rousseau, D.. 2018, Evidence-based management: how to use evidence to make better organizational decisions. London: Kogan Page.
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Ferrar, J. and Green, D.. 2021,Excellence in People Analytics, How to Use Workforce Data to Create Business Value. London: Kogan Page.
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Kahneman, D.. 2011, Thinking fast, thinking slow. London: Penguin.
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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.
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Mattox, J.R., Parsky, P. and Hall, C. 2020, Learning analytics: using talent data to improve business outcomes. 2nd ed. London: Kogan Page.
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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 |
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Other Resources |
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[Website], People Analytics factsheet,
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[Website], CIPD, (2018), Getting started with
People Analytics – A Practitioners Guide,
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[Website], CIPD. (2017) Human capital analytics and
reporting: exploring theory and
evidence. London: Chartered Institute of
Personnel and Development.,
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[Website], CIPD/Workday. (2018) People analytics:
driving business performance with data.
London: Chartered Institute of Personnel
and Development,
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[Website], CIPD (2016) In search of the best
available evidence. Chartered Institute
of Personnel and Development,
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[Website], Houghton, E. and Green, M. (2018) People
analytics: driving business performance
with people data. Report. London:
Chartered Institute of Personnel and
Development.,
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[Website], Sclater, N. (2017) Learning analytics
explained. Abingdon: Routledge.
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[Website], Chartered Institute of Personnel and
Development. CIPD Toolkits,
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[Website], European Commission. Eurostat,
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[Website], European Central Bank,
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[Website], Central Statistics Office,
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[Website], Economic and Social Research Institute,
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[Website], World Bank. Data,
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[Website], Institute for Statistics Education,
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[Website], OECD. Data,
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