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Summary Compulsory articles HR analytics
- Vak
- HR Analytics
- Instelling
- Tilburg University (UVT)
Alle verplichte artikelen per college voor het vak HR analytics
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Voorbeeld 4 van de 112 pagina's
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Voorbeeld van de inhoud
Lecture 1: Introduction to HR Analytics – Compulsoryarticles
Edwards & Edwards (2019) - Predictive HR Analytics: Mastering the HR
Metrics (2nd ed). London: Kogan Page.
Chapter 1: understanding HR analytics
‘’Arguably the most practical tool and greatest potential for organizational management is the
emergence of predictive analytics’’ – Fitz-enz and Mattox 2014
‘’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’’
Claims about the potential that HR analytics could bring to business. A lot of people know it is
relevant, but little have a good understanding of ‘’predictive’’ HR analytics. – Huselid 2014
Predictive HR analytics defined
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. E.g. what might drive high performance or what might
cause employee turnover.
Understanding the need (and business case) for mastering and utilizing predictive HR analytic
techniques
Often in HR departments nowadays: descriptive reports (picture/snapshot of what is occurring in the
organization at that time). There is a limit to what these reports can tell us. Descriptive reports do
very little more than describe what is happening; they lack the capability to help understand and
account for why things are happening in the organization. Fails:
- Fails to interrogate data fully for other possible explanatory factors
- Fail to check if the data is robust and valid
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 an organization and having evidence for why things have happened
(knowing the drivers of behaviours within the organization) better decisions making.
Human capital data storage and ‘big (HR) data’ manipulation
To realize the potential of predictive HR analytics we are reliant upon current and historical data
availability.
Useful HR-related data is made up of many different types of information and might include the
following:
- Skills and qualifications, measures of particular competencies, training attended, levels of
employee engagement, customer satisfaction data, performance appraisal records, pay,
bonus and remuneration data.
The data available is the key determining factor on what kind of analysis can be carried out and what
business questions can be answered. The other important factors is respect for the ‘’head space’’
required to be able to fully engage with the data, the analysis, and what is all means for the
organization.
Predictors, prediction and predictive modelling
Three uses of the term prediction:
-‘’predictors’’ or potential ‘’causal’’ factors: they help explain why a particular feature or measure
shows variation (e.g. why performance levels vary amongst employees). Assuming that we find a
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,range of significant features of our people related data where variation is associated (in a unique
way) with an increase or decrease in what we hope to account for, we can say that we have found
potential predictors. Predictors = potential drivers of our outcome = potential causes of variation on
the feature we are trying to predict.
-Predictive modelling: 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.
-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.
Current state of HR analytic professional and academic training
The majority of HR functions do not have the core capabilities to carry out predictive HR analytics
activities.
Business applications of modelling
Almost all of the analyses presented in this book will have significant business implications and
applications.
HR analytics and HR people strategy
It is possible to use analytical models to help steer, adjust and even drive business strategy.
Khan, N., & Millner, D. (2020). Introduction to People Analytics: A practical
guide to Data-Driven HR. London: Kogan Page.
Chapter 1 Redefining HR
1. The context for change
The world is full of data, and it is impacting on all our lives. The companies that view data as a
strategic asset are the ones that will survive and thrive, but what about HR in this area?
This chapter will cover HR and the new world of work, the shift of HR into a people function, and
tomorrow’s people function.
HR and the new world of work: The three Ds
It is a constant challenge to keep up with the fast-paced changing workplace with technology and
changing demographics. The following figure highlights the recurring themes that have emerged
from the different challenges and summarizes the major themes that have to be considered if you
and your organization want to be ‘future proof’.
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,There are several continually changing disruptors that impact all organizations, such as the constantly
changing markets, external forces, increasing shift to contingency workers and increase of
automation.
Supervisors, managers and leaders are having to manage more key challenges than ever before,
which revolve around:
1. Execution: a desire to achieve “more with less” driven by the challenging cost agenda that is
understandably a feature of most organizations
2. Employee expectations: An increasing need to learn new processes, skills and practices as
automation increases. Having time to learn vs. compete with the rapid changes.
3. Manager priorities: Finding the right balance between operational task/process completion
vs the increasing desire for the “human touch”, which is so vital when creating an
environment that employees want to work in.
4. Leader “bandwidth”: A clear execution focus driven by demanding multiple stakeholders and
expectations.
5. Wellbeing: Adding to the wellbeing agenda 🡪 more of a workforce issue than ever before
(stress and ill-health).
Constant change is the new normal – common challenges:
Resilience to change for years resistance to change has been the challenge, but the fact is that
change affects everyone in the workplace. It’s about building up resilience to change within the
workplace, as resistance is now futile; it’s going to occur whatever happens, so start embracing it!
Change programs: large-scale programs still exist in organizations, but the key difference is that
these are now a series of smaller pilots and projects that inform the bigger picture. That means high
workforce involvement to obtain buy-in, and real-time insights that not only help with the quality of
the solution that is being developed, but also build a real connection with the workforce through that
involvement.
Employee experience: making work personal
The employee experience is very important, because
there are different expectations across the workforce.
Employee experience: “the perceptions and feelings of
the employees towards their job experience at work”.
Research highlighted five dimensions that captured the
key elements of the employee experience:
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, 1. Belonging: feeling part of a team, group or organization;
2. Purpose: understanding why one’s work matters;
3. Achievement: a sense of accomplishment in the work that is done;
4. Happiness: the pleasant feeling arising in and around work;
5. Vigor: the presence of energy, enthusiasm and excitement at work.
Figure 1.2 focuses on automation driving innovation and approaches that are designed to make the
experience at work more individualized and future focused. – not only focus on improved
experience, but also improved efficiencies across HR practices.
The strength of an approach with technology-based techniques is that with the data that these
methods generates, cones insights and evidence to back up why something should be done; it is
about making better informed quality decisions based on the evidence that the new technology
systems can provide.
Reshaping jobs: new skill demands
Whilst the employee experience
focuses on the emotional aspect
and impact of organizational
change, the business-led
automation debate means that
there is a clear need to determine
an approach that optimizes the
combination of human and
automated work. Figure
1.3 outlines a process and some of
the key considerations and
questions to consider.
Ravin Jesuthasan and John Boudreau suggest that this automation review process follows two major
steps; namely, deconstructing the work and evaluating the return on improved performance (ROIP),
and optimizing human and automated work by considering the types of available automation, and
whether automation will replace, augment or reinvent the human worker.
Whatever would change, the three major dimensions of work (figure 1.4) will remain in place:
The work: What work is being completed? (totally automated processes and practices to increasing
machine-based work, partially automated practices and the existing “human-led” domain
knowledge-based work that is required)
The workplace: Where is the work being completed? (static workplace, different working methods,
work locations)
The workforce: Who does the work? (full-time and part-time employees, freelancers, gig workers
and managed services/contractors providing support).
New business models and structures
Organizational structure issues revolve around the fact that
the structures in place today were originally designed for an
environment of stability, predictability, and control – which
are not the features that drive digital or business
transformations.
Every organization, whether a competitive business,
government or non-profit organization is a data business. The
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