Module Code: |
H8HRA |
Long Title
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HR Analytics
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Title
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HR Analytics
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Module Level: |
LEVEL 8 |
EQF Level: |
6 |
EHEA Level: |
First Cycle |
Module Coordinator: |
COLETTE DARCY |
Module Author: |
Michael Cleary-Gaffney |
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 |
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).
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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
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 performance metrics i.e., L&D, performance, workforce planning etc.
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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
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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
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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
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Assessment Breakdown | % |
Coursework | 100.00% |
AssessmentsFull 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. |
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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. |
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No End of Module Assessment |
Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
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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 |
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 |
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Khan, M., Milliner, D. (2019) Introduction to People Analytics, A practical guide to data-driven HR, Kogan Page..
| 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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Marr, B. (2018) Data-driven HR: how to use analytics and metrics to drive performance. London: Kogan Page..
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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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Sclater, N. (2017) Learning analytics explained. Abingdon: Routledge.
| Recommended Article/Paper Resources |
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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.
| Other Resources |
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CIPD, (2019), People Analytics
factsheet available
at https://www.cipd.ie/knowledge/world-w
ork/analytics/factsheet.
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CIPD, (2018), Getting started with
People Analytics – A Practitioners
Guide available
at: https://www.cipd.ie/knowledge/world-
work/analytics/practitioner-guide.
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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.
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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.
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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.
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Chartered Institute of Personnel and
Development. CIPD
Toolkits, http://shop.cipd.co.uk/shop/bo
okshop/toolkits.
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European Commission.
Eurostat, http://ec.europa.eu/eurostat.
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European Central Bank,http://www.ecb.int.
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Central Statistics
Office, http://www.cso.ie.
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Economic and Social Research
Institute, http://www.esri.ie/.
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World Bank.
Data, http://data.worldbank.org/.
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Institute for Statistics
Education, http://www.statistics.com/.
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OECD. Data, https://data.oecd.org/.
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