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  3. Tilburg University (UVT)
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  5. HR Analytics (760819M6)

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Summary HR Analytics - articles, books and lecture slides

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

This is a summary of the course HR Analytics. I passed the course with an 8.1. I read all the articles and books and attended all the lectures. Based on all this information, the summary was created. Furthermore, I included a glossary to find information more easily during the exam.

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Document information

  • October 23, 2023
  • 135
  • 2023/2024
  • Summary

Subjects

  • hr analytics
  • lectures
  • articles
  • 760819 m 6
  • human resources studies
  • books
  • summary
  • master human resource studies

Written for

  • Tilburg University (UVT)
  • Master Human Resource Studies
  • HR Analytics (760819M6)
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Inhoudsopgave
Week 1 – Introduction to HR analytics .................................................................................................................................................................2
Articles ..............................................................................................................................................................................................................2
Edwards & Edwards (2019) CH1: Understanding HR analytics ...................................................................................................................2
Khan & Millner (2020) CH1: Redefining HR .................................................................................................................................................3
Edwards & Edwards (2019) CH2: The age of data and people analytics ..................................................................................................14
Lecture slides ..................................................................................................................................................................................................21
Week 2 – The HR analytics framework ...............................................................................................................................................................26
Articles ............................................................................................................................................................................................................26
Peeters et al. (2020): People analytics effectiveness: developing a framework ......................................................................................26

Levenson & Fink (2017): Human capital analytics – too much data and analysis, not enough models and business insights ................32
Cascio et al. (2019) CH1: HR measurement makes investing in people more strategic ...........................................................................35
Cascio et al. (2019) CH2: Analytical foundations of HR measurement .....................................................................................................39
Khan & Millner (2020) CH6: A people analytics framework – laying the foundations through metrics, reporting and core analytical
activity .............................................................................................................................................................................................................40
Khan & Millner (2020) CH7: A people analytics framework – Identifying business insights from analytics..................................................48
Lecture slides ..................................................................................................................................................................................................53
Week 3 - The Analytical toolset and big data .....................................................................................................................................................61
Articles ............................................................................................................................................................................................................61
Edwards & Edwards (2019) CH2: HR information systems and data ........................................................................................................61
Edwards & Edwards (2019) CH3: Analysis strategies................................................................................................................................64
Edwards & Edwards (2019) CH4: Case study 1 Diversity analytics ...........................................................................................................72
Khan & Millner (2020) CH5: Working with data........................................................................................................................................74

Lecture slides ..................................................................................................................................................................................................80
Week 4 – Presenting results and the road ahead...............................................................................................................................................85
Articles ............................................................................................................................................................................................................85
Nussbaumer & Knaflic (2015) CH7: lessons in storytelling .......................................................................................................................85
Nussbaumer & Knaflic (2015) CH8: pulling it all together ........................................................................................................................88
Heuvel & Bondarouk (2017): The rise (and fall?) of HR analytics .............................................................................................................91
Angrave et al. (2016): HR and analytics – Why HR is set to fail the big data challenge............................................................................94
Khan & Millner (2020) CH10: The road ahead ..........................................................................................................................................98
Lecture slides ................................................................................................................................................................................................108
Additional articles (not covered in the lectures) ..............................................................................................................................................112
Edwards & Edwards (2019) CH12: Reflection on HR analytics – usage, ethics and limitations. ..................................................................112
Chamorro-Premuzic, , Buchband, & Schettler. (2019): The Legal and Ethical Implications of Using AI in Hiring........................................115
Rasmussen & Ulrich (2015): Learning from practice – How HR analytics avoids being a management fad................................................116

C.I.P.D. (2018): People analytics – driving business performance with people data ...................................................................................118
Henke et al. (2018): Analytics translator – the new must-have role ............................................................................................................121
Van der Laken (2018) CH1: Data-driven human resource management .....................................................................................................123
Khan & Millner (2020) CH8: Delivering people analytics projects................................................................................................................125
Begrippenlijst .....................................................................................................................................................................................................132




1

,Week 1 – Introduction to HR analytics

Articles
Edwards & Edwards (2019) CH1: Understanding HR analytics


Predictive HR analytics: The systematic application of predictive modelling using inferential statistics
to existing HR people-related data in order to inform judgements about possible causal factors
driving key HR- related performance indicators.

Whilst HR metrics and HR analytics teams do process and report on vast amounts of people-related
data, very few apply statistical techniques that enable predictive inferences to be made.

Descriptive reports do very little more than describing what is happening: they lack the capability to
help understand and account for why things are happening in the organization. Furthermore, when
running these reports, the analysts generally fail to interrogate the data fully for other possible
explanatory factors (which can help to clarify why something might be happening). They also tend to
fail to test or check the degree to which their data might be robust and valid. Furthermore,
descriptive reports do not in any way help us to make predictions about what we might find in the
future.

By identifying trends and patterns, HR professionals and management teams can make better
strategic decisions about the workforce challenges that they may soon face. Predictive HR analytics
therefore offers the opportunity to help model and analyse historical data and interrogate patterns
in order to help understand causal factors.

Knowing what has happened in our organization and having evidence for why things have happened,
in particular what the drivers are of certain behaviours within our organization, will undoubtedly help
us to make better decisions.



The success of HR analytics is completely reliant on the availability of good people-related
information. However, there can be a lack of data or too much data. Once we have sufficient HR-
related data, one of the biggest challenges is getting that data into the right format for analysis.

Useful HR related data is made up of many different types of information and might include:

Skills and qualifications Customer satisfaction data
Measures of particular competencies Performance appraisal records
Training attended Pay, bonus and remuneration data
Levels of employee engagement


Ultimately, the data available (and the data that is missing) is the key determining factor on what
kind of analysis can be carried out and what business questions can be answered. The other
important factor is respect for the ‘head space’ required to be able to fully engage with the data, the
analysis, and what it all means for the organization.




2

,Prediction: identifying ‘predictors’ or potential ‘causal’ factors that help explain why a particular
feature or measure shows variation.

Predictive HR analytics: the use of ‘predictive modelling’. 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.

Finally, a third use of the term ‘prediction’ that we can use in the context of ‘predictive HR analytics’
is that we can translate the findings from our ‘predictive models’ where we identified ‘predictors’ of
variation in our particular outcome variable and use the resulting model to ‘predict’ how current or
future employees (or teams) may behave in the future.

One of the things that the HR analytics team will need to be able to do, is to translate analysis
findings into potential business applications.



Khan & Millner (2020) CH1: Redefining HR


This chapter will cover:

• HR and the NOW of work
• The shift of HR into a people function
• Tomorrow’s people function



HR and the NOW of Work
The NOW of work is about serving humanity by humanizing work.

Figure 1.1 highlights the recurring themes that have emerged from these numerous reports and
summarizes the major themes that that have to be considered if you and your organization want to
be ‘futureproof’.




3

, • The overwhelmed workforce
They key challenges revolve around:

1. Execution: achieve more with less
2. Employee expectations: there is an increasing need to learn new processes, skills,
and practices as automation increases.
3. Manager priorities: the challenge to find the right balance between operational tasks
and the desire for the ‘human touch’.
4. Leader bandwidth: beyond their responsibility to execute the demands of
stakeholders, issues are becoming increasingly complex (e.g., different cultures).
5. Wellbeing: operational challenges versus wellbeing challenges.

Whatever the challenge may be in this area, listening to the workforce and actioning the
workforce data that can be collected are vital to understanding the motivation and feelings
of the workforce.

• Creating the new normal
Some of the common challenges are:

• Resilience to change -> It’s about building up resilience to change within the
workforce, as resistance is now futile; it’s going to occur whatever happens, so start
embracing it!
• Change programmes -> Large-scale programmes still exist in organizations, but the
key difference is that these are now series of smaller pilots and projects that inform
the bigger picture.

• Leveraging the new employee experience
The employee experience attains more importance as different expectations across the
workforce, and employees, are more prevalent than ever before. There is no longer an
acceptance of certain organizational practices as there has been in the past.

Employee experience: the perceptions and feelings of the employees towards their job
experience at work.

Five dimensions that capture the key elements of the employee experience: belonging,
purpose, achievement, happiness and vigour.




4

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