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Summary HR Analytics

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  • Course
  • HR Analytics (760819M6)
  • Institution
  • Tilburg University (UVT)

Summary of the following literature: 1. Edwards, M., & Edwards, K. (2019). Predictive HR Analytics: Mastering the HR Metrics (2nd ed). London: Kogan Page. Chapters 1, 2, 3, 4 & 12 (use other chapters to refresh your statistical knowledge and see applications of statistical tools on real HR prob...

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  • October 7, 2022
  • October 7, 2022
  • 73
  • 2022/2023
  • Summary

Subjects

  • hr analytics
  • tsb
  • nussbaumer
  • chamorro premuzic
  • angrave
  • rasmussen
  • summary
  • hr
  • tilburg university
  • edwards amp edwards
  • khan amp millner
  • cascio et al
  • van den heuvel amp bondarouk
  • levenson amp fink

Written for

  • Tilburg University (UVT)
  • Master Human Resource Studies
  • HR Analytics (760819M6)
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Summary HR Analytics
Literature
1. Edwards, M., & Edwards, K. (2019). Predictive HR Analytics: Mastering the HR Metrics (2nd ed). London:
Kogan Page. Chapters 1, 2, 3, 4 & 12 (use other chapters to refresh your statistical knowledge and see
applications of statistical tools on real HR problems)
2. Khan, N., & Millner, D. (2020). Introduction to People Analytics: A practical guide to Data-Driven HR. London:
Kogan Page. Chapters 1, 2, 5, 6, 7, 8 & 10
3. Cascio, W. F., Boudreau, J. W., & Fink., A. A. (2019). Investing in people: Financial impact of human resource
initiatives (3rd ed). Alexandria, VA: Society for Human Resource Management. Chapter 1 & 2 (p.25-32)
4. Nussbaumer Knaflic, C. (2015). Storytelling with data: A data visualization guide for business professionals.
New York: Wiley. Chapters 7 & 8.
5. Chamorro-Premuzic, T., Buchband, R., & Schettler, L. (2019). The Legal and Ethical Implications of Using AI in
Hiring. Harvard Business Review.
6. Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: why HR is set
to fail the big data challenge. Human Resource Management Journal, 26(1), 1-11.
7. Rasmussen, T., & Ulrich, D. (2015). Learning from practice: how HR analytics avoids being a management fad.
Organizational Dynamics, 44(3), 236-242.
8. Van den Heuvel, S., & Bondarouk, T. (2017). The rise (and fall?) of HR analytics: A study into the future
application, value, structure, and system support. Journal of Organizational Effectiveness: People and
Performance, 4(2), 157-178.
9. Levenson, A., & Fink, A. (2017). Human capital analytics: too much data and analysis, not enough models, and
business insights. Journal of Organizational Effectiveness: People and Performance, 4(2), 145-156.
10. C.I.P.D. (2018). People Analytics: Driving business performance with people data. (online resource)
https://www.cipd.co.uk/knowledge/strategy/analytics/people-data-driving-performance
11. Henke, N., Levine, J., McInerney, P. (2018). Analytics translator: The new must-have role. McKinsey. (online
resource) www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/analytics-translator
12. Van der Laken, P. (2018). Data-Driven Human Resource Management. Dissertation. Tilburg University.
Chapter 1.
13. Peeters, T., Paauwe, J., & Van De Voorde, K. (2020). People analytics effectiveness: developing a framework.
Journal of Organizational Effectiveness: People and Performance.




1

,Table of contents
Literature Lecture 1........................................................................................................................................................... 3
Book 1: Chapter 1. Understanding HR analytics (Edwards & Edwards, 2019).............................................................. 3
Book 2: Chapter 1. Redefining HR (Khan & Millner, 2020) ........................................................................................... 3
Book 2: Chapter 2. The age of data and people analytics (Khan & Millner, 2020) ....................................................... 8
Literature Lecture 2......................................................................................................................................................... 11
Book 3: Chapter 1. HR Measurement makes investing in people more strategic (Cascio et al., 2019) ..................... 11
Book 2: Chapter 6. A people analytics framework (Khan & Millner, 2020) ................................................................ 14
Book 2: Chapter 7. Business insights from people analytics (Khan & Millner, 2020) ................................................. 17
Article 13: People analytics effectiveness: developing a framework (Peeters, Paauwe & Van de Voorde, 2020)....... 20
Article 9: Human Capital Analytics: too much data analysis, not enough models, and business insights (Levenson &
Fink, 2017)................................................................................................................................................................... 24
Literature Lecture 3......................................................................................................................................................... 27
Book 1: Chapter 2. HR information systems and data (Edwards & Edwards, 2019) .................................................. 27
Book 1: Chapter 3. Analysis strategies (Edwards & Edwards, 2019) .......................................................................... 29
Book 1: Chapter 4. Diversity analytics (Edwards & Edwards, 2019) ........................................................................... 37
Book 2: Chapter 5. Working with data (Khan & Millner, 2020) .................................................................................. 41
Literature Lecture 4......................................................................................................................................................... 46
Book 4: Chapter 8 Pulling it all together (Nussbaumer Knaflic, C., 2015)................................................................... 46
Book 2: Chapter 10. The road ahead: turning intent into tomorrow’s people function through people analytics
(Khan & Milner, 2020) ................................................................................................................................................. 49
Article 8: The rise (and fall?) of HR analytics: a study into the future application, value, structure, and system
support (Van den Heuvel & Bandourak, 2017). .......................................................................................................... 53
Article 6: HR and analytics: why HR is set to fail the big data challenge (Angrave et al., 2016) ................................. 58
Other compulsory literature: .......................................................................................................................................... 61
Book 1: Chapter 12. Reflection on HR Analytics: Usage, Ethics, and limitations (Edwards & Edwards, 2019) .......... 61
Book 3: Chapter 2. Analytical foundation of HR measurement (Cascio et al., 2019) ................................................. 64
Article 5: The Legal and Ethical Implications of Using AI in Hiring (Dattner, Chamorr-Premuzic, Buchband &
Schettler, 2019) ........................................................................................................................................................... 65
Article 7: Learning from practice: how HR analytics avoids being a management fad (Rasmussen & Ulrich, 2015) . 66
Article 10: People analytics: driving business performance with people data (C.I.P.D., 2018) .................................. 68
Article 11: Analytics translator: The new must-have role (Henke et al., 2018) .......................................................... 70
Book 12: Chapter 1. Data-Driven Human Resource Management (Van der Laken, 2018) ......................................... 72




2

,Literature Lecture 1
1. Edwards & Edwards (2019): Chapter 1
2. Khan & Millner (2020): Chapter 1 & 2

Book 1: Chapter 1. Understanding HR analytics (Edwards & Edwards, 2019)
Analytics present a tremendous opportunity to help organizations understand what they don’t yet know by identifying
trends and patterns, HR professionals and management teams can make better strategic decisions about the workforce
challenges that they may soon face (Huselid, 2014).

Predictive HR analytics → the systematic application of predictive modeling using inferential statistics to existing HR
people-related data to inform judgments about possible causal factors driving key HR-related performance indicators.
This makes it possible to predict causes and to predict results and outcomes.

Understanding the need (and business case) for mastering and utilizing predictive HR analytic techniques
Currently, many HR teams produce descriptive reports again and again. However, these reports only present a picture
or snapshot of what is occurring at that time. Unfortunately, they say little about what is going on, they do not give us
insights into why things are happening in the organization. Besides, they lack to check whether the data is robust
and/or valid. Also, these kinds of reports do not predict what will happen in the future. Predictive HR analytics offers
the opportunity to help model and analyze historical data and interrogate patterns to help understand causal factors.
This, in turn, will help to make better decisions.

Human capital data storage and ‘big (HR) data’ manipulation
The success of HR analytics is completely reliant on the availability of good people-related information. However, HR
managers will be probably facing the problem that modeling too much data available to know what to do with it. The
biggest challenge is to get the data into the right format for analysis.

Predictors, prediction, and predictive modeling
1. The term ‘prediction’ can be used in various ways. According to the authors of this book, prediction entails the
idea of identifying ‘predictors’ or potential ‘causal’ factors that help explain why a particular feature or measure
shows variation.
2. A second use of the term in the context of ‘predictive HR analytics’ is ‘predictive modeling’. Here, we take
features and findings of our analysis, then we apply our model to help demonstrate or ‘predict’ what would
happen to our key outcome variable if we could do something to change or adjust the key drivers that we
have identified.
3. A third use of the term ‘prediction’ that we can use in the context of ‘predictive HR analysis’ is that we can
translate the findings from our predictive models where we identified ‘predictors’ of variation in our particular
outcome variable, and we use the resulting model to ‘predict’ how current or future employees may behave in
the future.

Current state of HR analytics professional and academic training
At the time, most HR functions do not have the core capabilities to carry out predictive HR analytics activities. Besides,
most people who enter the HR profession do not have the required skills to carry out predictive HR analytics. However,
whether the individual is an HR generalist, a specialist in one area, or the head of HR for a large MNC, the need to
identify and understand trends and patterns, and to predict organizational challenges is something that will set them
apart in becoming a credible, high-performing HR professional. Consequently, it can be stated that this gap needs to
be filled.

Book 2: Chapter 1. Redefining HR (Khan & Millner, 2020)
HR and the new world of work: The Three Ds
Through the fast pace of changes, businesses and employees are expected to embrace innovations like never before.
The following figure highlights the most important themes that must be ‘futureproof’.




3

, The digital world of work
The key elements of digital work are
driven by changing disruptors.




1. The overwhelmed workforce
Expectations of employees increase all the time. At the same time, managers must manage more complex relationships.
Through increasing automation and ongoing changes, the key challenges revolve around:
− Execution: achieve more with less.
− Employee expectations: there is an increasing need to learn new processes, skills, and practices as automation
increases.
− Manager priorities: the challenge to find the right balance between operational tasks and the desire for the
‘human touch’.
− Leader ‘bandwidth’: beyond their responsibility to execute the demands of stakeholders, issues are becoming
increasingly complex (e.g., different cultures).
− Wellbeing: operational challenges versus wellbeing challenges.
Data can be collected to understand how the workforce is feeling.

2. Constant change is the new normal
Within change, challenges are:
− Resilience to change: resistance towards change always has been a challenge. But the changes will come
anyways, so resilience is needed.
− Change programs: large-scale programs through a series of smaller pilots and products. As a result, a real
connection with the workforce is built. Employees need to be involved to make changes. Also, more and more
change succeeds with data and insights from the workforce.

3. Employee experience: making work personal
The ‘battle for the hearts and minds of employees. It is hard to manage the workforce with three different generations.
You never really know how someone responds best until you’ve worked with them for a while. Therefore, employee
experience is important. Besides, in an increasingly automated world, it is hard to remain work personal. Employee
experience = the perceptions and feelings of the employees towards their job experience at work and captures the
following key elements:
a. Belonging: feeling part of a team, group, or organization.
b. Purpose: understanding why one’s work matters.
c. Achievement: a sense of accomplishment in the work.
d. Happiness: the pleasant feeling arising in and around work.
e. Vigor: the presence of energy, enthusiasm, and excitement at
work.

Automation driving innovation occurs throughout the whole employee
lifecycle. These approaches are designed to make the experience at
work more individualizedand future-focused.




4

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