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MODULE DESCRIPTOR
Module Title
Digitalisation and Human Resource Analytics
Reference	BSM853	Version	1
Created	March 2021	SCQF Level	SCQF 11
Approved	February 2022	SCQF Points	15
Amended		ECTS Points	7.5

Aims of Module
This is not a module about statistics. Instead, the aim of this module is to provide students with the confidence and understanding of how a people function can utilise, deploy and embed digitalisation, insights and analytics to drive value and improve the employee experience and business performance.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1	Understand and assess the importance of digitalising HR and implementing people analytics on the success of the people function.
2	Evaluate how HR digitisation and people analytics supports the business through evidence-based decision making that creates value and drives an informed focus on outcomes, including understanding common people metrics and how to interpret them.
3	Understand and analyse the people analytics and data lifecycle and how to use this successfully as HR practitioners to drive positive outcomes.
4	Critically analyse how technology impacts employees and can affect the employee experience.
5	Identify and evaluate the challenges developing digitalisation and people analytics capability can present for the HR function and the wider business.

Indicative Module Content
Forms of digitalisation; stakeholders in digitalisation and analytics; the difference between reporting and analytics; developing digital and analytics capability in the HR function; the benefits of data democratisation, ethical data and privacy considerations arising from implementing new technology; sources of people data and the analytics process; descriptive, predictive and prescriptive analytics; how people analytics can inform decision making, create value and identify risks in organisations, how to interpret and tell a story with data; the rise of data science in the people function.

Indicative Module Content

Forms of digitalisation; stakeholders in digitalisation and analytics; the difference between reporting and analytics; developing digital and analytics capability in the HR function; the benefits of data democratisation, ethical data and privacy considerations arising from implementing new technology; sources of people data and the analytics process; descriptive, predictive and prescriptive analytics; how people analytics can inform decision making, create value and identify risks in organisations, how to interpret and tell a story with data; the rise of data science in the people function.

Module Delivery
This module is delivered for on campus students via a series of interactive live lectures and tutorials in addition to pre-recorded content. The live sessions are student-led and often require group work to analyse case studies. Students are also required to undertake directed and independent self-study. Online students will have access to a range of materials and tools to facilitate their learning including topic guides, recorded lectures, online tutorials, exercises, group-work areas and discussion forums. In addition to the sessions with a tutor, online quizzes and knowledge checkers may be used to assist online learners to benchmark their knowledge as they go. External speakers and lab sessions with MS Excel may also be used to further contextualise the module content and help students understand how digitalisation and HR analytics are applied in practice.

Module Delivery

This module is delivered for on campus students via a series of interactive live lectures and tutorials in addition to pre-recorded content. The live sessions are student-led and often require group work to analyse case studies. Students are also required to undertake directed and independent self-study. Online students will have access to a range of materials and tools to facilitate their learning including topic guides, recorded lectures, online tutorials, exercises, group-work areas and discussion forums. In addition to the sessions with a tutor, online quizzes and knowledge checkers may be used to assist online learners to benchmark their knowledge as they go. External speakers and lab sessions with MS Excel may also be used to further contextualise the module content and help students understand how digitalisation and HR analytics are applied in practice.

Indicative Student Workload	Full Time	Part Time
Contact Hours	36	36
Non-Contact Hours	114	114
Placement/Work-Based Learning Experience [Notional] Hours	N/A	N/A
TOTAL	150	150
Actual Placement hours for professional, statutory or regulatory body

ASSESSMENT PLAN
If a major/minor model is used and box is ticked, % weightings below are indicative only.
Component 1
Type:	Coursework	Weighting:	100%	Outcomes Assessed:	1, 2, 3, 4, 5
Description:	Consultancy based report based on a case study scenario

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
Component 1 comprises 100% of the module grade. to pass the module, a D grade is required.
Module Grade	Minimum Requirements to achieve Module Grade:
A	A
B	B
C	C
D	D
E	E
F	F
NS	Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module	None.
Corequisites for module	None.
Precluded Modules	None.

INDICATIVE BIBLIOGRAPHY
1	FERRAR, J. and GREEN, D., 2021. Excellence in People Analytics. London: Kogan Page.
2	SOUMYASANTO, S., 2020. Digital HR Strategy: Achieving Sustainable Transformation in the Digital Age. London: Kogan Page.
3	TIMMS, P., 2020. Transformational HR: How Human Resources can Create Value and Impact Business Strategy. London: Kogan Page.

Robert Gordon University, Aberdeen

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