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The rise (and fall?) of HR analytics: A study into the future application, value, structure, and system support

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Purpose Driven by the rapidly accelerating pace of technology-enabled developments within human resource management (HRM), human resource (HR) analytics is infiltrating the research and business agenda. As one of the first in its field, the purpose of this paper is to explore what the future of HR analytics might look like. Design/methodology/approach Using a sample of 20 practitioners of HR analytics, based in 11 large Dutch organizations, the authors investigated what the application, value, structure, and system support of HR analytics might look like in 2025. Findings The findings suggest that, by 2025, HR analytics will have become an established discipline, will have a proven impact on business outcomes, and will have a strong influence in operational and strategic decision making. Furthermore, the development of HR analytics will be characterized by integration, with data and IT infrastructure integrated across disciplines and even across organizational boundaries. Moreover, the HR analytics function may very well be subsumed in a central analytics function – transcending individual disciplines such as marketing, finance, and HRM. Practical implications The results of the research imply that HR analytics, as a separate function, department, or team, may very well cease to exist, even before it reaches maturity. Originality/value Empirical research on HR analytics is scarce, and studies on scenarios, values, and structures of expected developments in HR analytics are non-existent. This research intends to contribute to a better understanding of the development of HR analytics, to facilitate business and HR leaders in taking informed decisions on investing in the further development of the HR analytics discipline. Such investments may lead to an enhanced HR analytics capability within organizations, and cultivate the fact-based and data-driven culture that many organizations and leaders try to pursue.
Journal of Organizational Effectiveness: People and Performance
The rise (and fall?) of HR analytics: A study into the future application, value,
structure, and system support
Sjoerd van den Heuvel, Tanya Bondarouk,
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Sjoerd van den Heuvel, Tanya Bondarouk, (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, Vol. 4 Issue: 2, pp.127-148, https://doi.org/10.1108/JOEPP-03-2017-0022
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The rise (and fall?) of
HR analytics
A study into the future application, value,
structure, and system support
Sjoerd van den Heuvel
University of Applied Sciences Utrecht, Utrecht, The Netherlands, and
Tanya Bondarouk
School of Management and Governance,
University of Twente, Enschede, The Netherlands
Abstract
Purpose Driven bythe rapidly accelerating pace of technology-enabled developments within human resource
management (HRM), human resource (HR) analytics is infiltrating the research and business agenda. As one of
the first in its field, the purpose of this paper is to explore what the future of HR analytics might look like.
Design/methodology/approach Using a sample of 20 practitioners of HR analytics, based in 11 large
Dutch organizations, the authors investigated what the application, value, structure, and system support of
HR analytics might look like in 2025.
Findings The findings suggest that, by 2025, HR analytics will have become an established discipline, will
have a proven impact on business outcomes, and will have a strong influence in operational and strategic
decision making. Furthermore, the development of HR analytics will be characterized by integration, with data
and IT infrastructure integrated across disciplines and even across organizational boundaries. Moreover, the
HR analytics function may very well be subsumed in a central analytics function transcending individual
disciplines such as marketing, finance, and HRM.
Practical implications The results of the research imply that HR analytics, as a separate function,
department, or team, may very well cease to exist, even before it reaches maturity.
Originality/value Empirical research on HR analytics is scarce, and studies on scenarios, values, and
structures of expected developments in HR analytics are non-existent. This research intends to contribute to a
better understanding of the development of HR analytics, to facilitate business and HR leaders in taking
informed decisions on investing in the further development of the HR analytics discipline. Such investments
may lead to an enhanced HR analytics capability within organizations, and cultivate the fact-based and
data-driven culture that many organizations and leaders try to pursue.
Keywords HR analytics, People analytics, Workforce analytics
Paper type Research paper
No one can predict the future course of the HR profession. No one can predict how HR practices will
change in the future. Thinking about the future, however, helps us to prepare for it. Thinking about
the future may lead to innovative insights. Thinking about the future may help to change todays
HR practices in positive ways (Ulrich, 1997, p. 231).
Many of the changes taking place today, and probably even more so in the future, will be
driven by new technological advancements and the increased availability of human
resource (HR) data.
Asthetitleofthispapersuggests,wearelookingtowardfuturedevelopmentsinHR
analytics. Critics could question the relevance of this topic since HR analytics has only just
started to mature in business and academia and, probably, we do not have yet enough data to
explore its future. However, we would argue that periods witnessing speedy business
transformations are exactly the right moments to look to the future developments of HR
analytics if human resource management (HRM) research is to increase its societal and academic
relevance (see discussion in the special issue of Human Resource Management, May-June, 2015).
Journal of Organizational
Effectiveness: People and
Performance
Vol. 4 No. 2, 2017
pp. 127-148
© Emerald Publishing Limited
2051-6614
DOI 10.1108/JOEPP-03-2017-0022
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2051-6614.htm
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There are various approaches to analyzing what will, could, or hopefully should happen
in the future,and in principle two different aims of a future study can be identified
(Höjer and Mattsson, 2000, p. 614). The first aim is related to the motivation to know
what the future will bring, so that timely business adjustments can be made and foreseeable
risks minimized. The other aim is related to the belief that, if we know the future, we can
influence its developments in a timely way by planning and implementing changes. In this
paper, we present a study pointing toward some possible developments and risks in HR
analytics. We first present a short historical outline that positions HR analytics in the HRM
contextual continuum.
In the 1980s, the early automation of some HRM processes (mainly payroll and data
administration) attracted scholarly attention that primarily focused on examining factors
that affected the adoption of an HR information system (HRIS) and the identification of HR
practices that could be automated (DeSanctis, 1986; Mathys and LaVan, 1982; Lederer, 1984;
Magnus and Grossman, 1985; Taylor and Davis, 1989). That research taught academics and
practitioners a great deal about establishing a set of technological requirements that has to
be met for HRISs to succeed, including the customization and integration of HRISs,
interfacing with corporate information systems, and centralizing records (Magnus and
Grossman, 1985). However, businesses reported only limited use of HRISs, and academic
research was very limited.
The 1990s saw more rapid and more intensive developments in both academic research
and everyday business practice. In this decade, the usage of information systems for HRM
was limited, albeit slowly increasing, and researchers started collecting evidence that HRISs
could reduce the administration in HRM processes. Organizations showed increasing
awareness of the broader possibilities of implementing computer systems in HRM
(Kossek et al., 1994; Mathieson, 1993; Hannon et al., 1996; Haines and Petit, 1997).
This decade showed increasing interest in HRISs among scholars, although academic
publications were still trying to catch upwith the growing HRIS practice. While these two
decades (1980s and 1990s) brought awareness and a slow acceptance, albeit still with
considerable doubt, of information systems in HR practice, the years that followed showed
rapid growth and an increasing interrelatedness between information systems and HR
practices, mostly due to developments with the internet. In historical terms, the internets
takeover of the global communication landscape was almost instant: it facilitated only
1 percent of information flows through two-way telecommunication networks in 1993,
but 51 percent by 2000, and more than 97 percent of the telecommunicated information
exchange by 2007 (Hilbert and López, 2011).
In the 2000s, the HRM function seemed to take on board many of the technological
developments becoming available. The term electronic HRM (e-HRM) appeared in practice,
and the academic community accelerated its efforts to understand the two decades of
e-HRM/HRIS practice. Companies broadened the scope of e-HRM applications: although
administrative e-HRM remained the most popular application (62 percent of companies),
there was increased use of strategic applications such as talent acquisition services
(61 percent), performance management (52 percent), and compensation management
(49 percent) (CedarCrestone, 2006). This decade also saw numerous publications and
academic discussions reported in the proceedings of newly emerging e-HRM conferences
(Bondarouk and Ruel, 2009).
Forward to today, and the explosion in self-reporting on social media facilitates the
datafication of sentiments, emotions, interactions, and relationships, with the outcome that our
personal and professional lives become increasingly datafied(Strong, 2015). Big datahas
made its entry into the business vocabulary, and has become a catch-all term to describe data
that is large in volume, high in velocity, diverse in variety, exhaustive in scope, fine-grained in
resolution, relational in nature, but still flexible (Kitchin, 2014; Strong, 2015). In the light of
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these developments, organizations are no longer cautious about digitizing personnel
management. Administrative applications for payroll administration and record keeping are
still the most popular, with many now operated by end-users through self-portals, but
companies are broadening the scope of HRM applications. Applications that are more
strategic, such as talent acquisition services, form a leitmotif on the digital HRM stage.
In parallel with the digitalization of HRM, opportunities are being created for HR
professionals to use the data generated by technologies to support HRM and business solutions,
and in particular to support decision making. Already in 2005, Boudreau and Ramstad (2005)
were advocating that the traditional service-oriented HR focus must be extended to a decision
sciencethat enhances decisions about human capital(p. 129). They argued that with such a
paradigm shift which in fact is comparable to the earlier evolutions in more mature strategic
functions such as finance and marketing theHRfunctioncouldactuallyfindoutwhatitmeans
to be strategic.The use of HR decision science could enhance decisions about people, just as
the marketing decision science enhances decisions about customers, and the finance decision
science enhances decisions about money(Boudreau and Ramstad, 2005, p. 131). Only now,
more than a decade later, does it seem that the paradigm has finally shifted. Businesses have,
however, opted for more a popular form of language, using the terms HR analytics, workforce
analytics, or people analytics. Inspired by success stories of organizations generating up to
$100 million in savings, while at the same time improving the engagement and productivity of
employees, advanced HR analytics is fast becoming mainstream (Fecheyr-Lippens et al., 2015)
and increasingly considered as an indispensable HR tool (Boston Consulting Group, 2014).
Overall, scholarly studies addressing the future of HRM have been less common than
in other fields. For example, in his literature review of Delphi studies published between
1995 and 2002, De Meyrick (2003) identified eleven studies concerning future
developments in information technology but none concerning HRM. Studies have
been conducted on anticipated practical business challenges, for example, concerning
e-commerce (Addison, 2003) and future tourism potential (Kaynak et al., 1994). Similarly,
management studies have focused on the future of management (Schwarz, 2008) and the
future of knowledge management systems (Nevo and Chan, 2007). The limited studies
concerning future HRM have addressed strategic HRM (Lepak and Shaw, 2008), the
development of future HRM practices (Robinson et al., 2007), and a survey predicting
future HR trends (Hays and Kearney, 2001). Other studies considering the future of HRM
have included human resource (HR) development (McGuire and Cseh, 2006; Hatcher and
Colton, 2007), country-specific studies (Lin, 1997), a workplace stress study among HR
professionals (Loo, 1996) and also suggestions for the focus of future HRM research
(Huselid, 2011). However, our search of scholarly databases, published books, various
conference proceedings, and the latest e-HRM and HRIS reviews (Bondarouk and
Furtmueller, 2012; Marler and Fisher, 2013; Van Geffen et al., 2013; Ruël and Bondarouk,
2014), and reviews on future study directions (De Meyrick, 2003) has failed to uncover any
scholarly articles addressing the future of HR analytics.
The present study therefore aims to contribute to the development of HR analytics, as a field
of research that can usefully inform business, by exploring how HR analytics will look in the
future. More specifically, this study investigates what practitioners of HR analytics, working for
major Dutch organizations, think HR analytics will look like in 2025. There is a danger that a
timespan of ten years could encourage wild speculation since todays environmental dynamics
often make organizations reluctant to look even three years ahead. However, information
technology landscapes, decision-making cultures, and analytical capabilities will not change
overnight within organizations. By adopting a timespan of ten years, we aim to elicit
perspectives that go beyond the limitations that harsher restrictions may place on
broadmindedness. The central research question addressed in this study is therefore:
RQ1. What will HR analytics look like in 2025?
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By providing a point of reference in the development of HR analytics, we hope to facilitate
business and HR leaders in taking informed decisions on investing in the further
development of the HR analytics discipline. Such investments may lead to an enhanced HR
analytics capability within organizations, and cultivate the fact-based and data-driven
culture that many organizations and leaders try to pursue.
Furthermore, based on the insights obtained from the study, we will offer a research
agenda that could improve the scientific robustness of debates concerning HR analytics.
Moreover, we will advocate a new wave of scholarly research focusing on the development
of HR analytics as a business discipline, including its impact on the HRM function and on
organizations as a whole.
Conceptualizing HR analytics
What do we actually mean by the term HR analytics? Analytics, in general, refers to the use of
analysis, data and systematic reasoning to make decisions(Davenport et al., 2010, p. 4). Adding
the HRcomponent to the concept implies that these analyses, data and systematic reasoning
concern the people who are (in whatever way) related to the organization. Although the very few
scholarly writings on HR analytics mostly lack explicit definitions of the concept, they inform us
that HR analytics includes rigorously tracking HR investments and outcomes(Ulrich and
Dulebohn, 2015, p. 202) and that statistical techniques and experimental approaches can be
used to tease out the causal relationship(Lawler et al., 2004, p. 4) between these HR practices or
policies and organizational performance outcomes. As such, HR analytics is a methodology for
developing innovative insights (Smeyers and Delmotte, 2013). This implies that HR analytics is
a process, and not simply a tool that produces valuable insights at the push of a button. It is
first a mental framework, a logistical progression, and second a set of statistical operations
(Fitz-enz and Mattox II, 2014, p. 2). The garbage in garbage outadage applies here: poor data
combined with brilliant analyses will produce little value, just as a terrific data set will not be of
much help if the analyses lack rigor. In a similar vein, without a relevant and well-formulated
research question, any insights derived from the data and analyses will lack strategic value.
Furthermore, providing innovative insights is not the ultimate goal of HR analytics: the object
is to bring decision making support to the management of people in organizations(KPMG,
2013, p. 4). Based on the above discussion, and inspired by Smeyers (2012), we define HR
analytics as: the systematic identification and quantification of the people-drivers of business
outcomes, with the purpose of making better decisions.
As already noted, the terms HR analytics, workforce analytics, and people analytics
coexist and are often used interchangeably. However, we would argue that the different
labels go beyond simple semantics because they determine what we consider to be outcomes
and how we determine success of HR/workforce/people analytics. HR analytics could, for
example, suggest that the responsibility for identifying and quantifying the people-drivers
of business outcomes lies within the HR function or department. However, from a business
perspective, it does not matter at all which team or department conducts the analytics.
Moreover, as Ulrich (1997) observes, strategic HR is owned, directed, and used by line
managers. Consequently, the business must bear the responsibility for employee-related
analytics of any kind. The label workforce analyticsis effectively detached from the HR
function, but may still have an exploitative association. Nevertheless, some leading software
vendors (e.g. Workday, SAPs SuccessFactors) use the term workforce analytics for their
products. People analyticsmay be the most neutral and employee-friendly label, and it is,
for example, consistently used by Google who, in general, avoid the term HRs and refer to
their HR department as People Operations.Usage of a specific label tends to be a matter of
consistency in specific product or business language and/or related to philosophy. In this
paper, we adopt the label HR analytics since this label dominates in the Dutch context where
our study was conducted.
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It is not unusual for the terms metrics and analytics to be used interchangeably.
However, in our view, it is important to distinguish between them. Influenced by
Fitz-enz(1984) work on the measurement of HRM and Kaplan and Nortons (1996)
introduction of the balanced scorecard, the use of metrics in HRM has gained in popularity
since the 1980s, Metrics such as absence rate,”“cost of hire,and time to fill jobshave
been increasingly applied in HRM practice. In essence, metrics allow data to be viewed
from different perspectives and in different formats(Liberatore and Luo, 2010, p. 315), for
example, by using tables, charts, and dashboards that summarize and visualize the raw data
in a more comprehensible manner. Although valuable, and perhaps even necessary (e.g. in
performance appraisal), metrics are insufficient to drive HRM since a clear understanding of
what causes whatis also required (Huselid, 2015). In other words, metrics do not provide a
robust insight into why something occurred, what explains differences in outcomes, or what
the likelihood is that an event will reoccur in the future. In the process of conducting
analytics in terms of Porters (1985) value chain, as Google does in their people analytics
activities, metrics precede analytics. Since a value chain essentially describes a series of
transformations in which inputs are transformed into outputs, and each transformation
adds value to the product, the value chain of HR analytics evolves from opinions through
data, metrics, analytics, and insights, and eventually leads to actions (Dekas, 2011). Given
that HR analytics explicitly involves linking people characteristics, HR practices or policies,
and business outcomes, the analytics concept is distinct and the term should not be used
interchangeably with the term metrics.
Although many HR strategists predict a promising future for HR analytics,
organizations are struggling to make HR analytics an organizational reality. Currently,
the capabilities required in HR analytics are not well-developed (Carlson and Kavanagh,
2012; Wolfe et al., 2006), and some even suggest that HR and people analyticsrepresents
one of the major capability gaps in todays HR practice (Deloitte, 2015). In a global study
conducted by Deloitte among more than 3,300 business and HR leaders from 106 countries,
only 35 percent of the respondents indicated that HR analytics was under active
developmentin their organization, and only 8.44 percent of the respondents believed that
their organizations had a strong HR analytics team in place. Most organizations, even large
multinationals, lack a clear vision of the future of HR analytics within their company.
Insights from academia may be expected, but empirical research on HR analytics and its
development is virtually non-existent (see, for an exception, Boudreau and Ramstad, 2005).
A search in the Web of Science database with the search term HR analyticsyielded only
a handful of results (e.g. Aral et al., 2012;Ulrich and Dulebohn, 2015), and the terms workforce
analyticsand people analyticswere no more successful. A possible explanation for the
apparent lack of scholarly attention to HR analytics may be that HR analytics is considered as
no more than yet another new HRM tool; or, perhaps, the organizational struggle to get HR
analytics implemented is perceived to be comparable with regular change management
challenges, and therefore does not attract widespread interest among scholars. However, in
our view, such perspectives underestimate the transformational potential of HR analytics.
That is, the emergence of HR analytics may very well enable existential changes in the HRM
function, and perhaps in organizations as a whole.
One can observe a rapidly accelerating pace of technology-enabled developments within
HRM. These enable HRM to be treated as a decision science and consequently allow HR
analytics to infiltrate the research and business agenda. In this paper, we argue that we as
scholars may not have as much time as we would like to think about and question the
usefulness, applicability, and/or complexity of HR analytics, and that we should
focus on delivering empirical evidence on its development and its relevance for science
and business practice. Particularly, we encourage scholars to leap into the future and
consider developments in HR analytics. In our view, this will bring real value to knowledge
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development and to businesses. As outlined above, given the rapid developments within
e-HRM/HRIS, we may miss crucial developments if we do not undertake joint research to
explore the future of HR analytics. Given that researchers and the business world are
already witnessing the future of HR analytics today, there is a growing demand to
understand it now in order to inform knowledge and the practice community about
scenarios, values, and structures of approaching developments in HR analytics.
A multidimensional view of the future of HR analytics
Having discussed the main terms and definitions related to HR analytics, we now turn to the
discussions about the future evolution of HR analytics, which is dominated by models that
primarily focus on the evolution of the analytical component itself. For example, in his book
The new HR analytics,Fitz-enz (2010) discusses the five-step value ladder of
measurement. Starting with the first step, recording,which, according to Fitz-enz
(2010), marks the beginning of HRs measurement in 1978, one can (via the steps relating,
comparing, and understanding) reach the fifth step predictingwhere you should be able
to predict organizational outcomes for a given human capital investment(p. 10). Other
classifications ascend from descriptive analytics, through correlation analytics, to
predictive/prescriptive analytics (Sesil, 2014), or from operational reporting, through
advanced reporting and advanced analytics, to predictive analytics (Bersin by Deloitte,
2013). Whatever classification system is applied, it only tells one small part of the story: that
organizations aim to move toward what they consider to be the holy grail of HR analytics:
predictive analytics (Harvard Business Review, 2013). However, there is much more to HR
analytics than measurements and statistics. Cascio and Boudreau (2011), for example,
emphasize the need to develop logic modelsin order to truly understand relationships
between variables or numbers. Without logic models, they argue, it is impossible to know
where to look for insights. Consequently, the positioning of HR analytics within the
organizational structure, such as within the HR department or within a general business
intelligence department, may considerably influence the logic models that are developed.
This in turn will influence the insights that are generated and the value added.
That there is much more to HR analytics than only measurements, statistics, and some
logical reasoning was illustrated by Coolen and IJselstein (2015). In their article A practitioners
view on HR analyticsthey introduce the HR analytics capability wheel and argue that only
those organizations that manage to create and maintain a balanced blend of different relevant
capabilities will be successful in HR analytics(Coolen and IJselstein, 2015, p. 1). These relevant
capabilitieswere described as perspectives, and include: the business perspective (having a
proper understanding of business challenges and strategy); the HRs perspective (knowing
about HR processes, available HR data, and ethics of analyzing employee data); the consultant
perspective (selling HR analytics to businessand presenting results in a convincing manner);
the data scientist perspective (conducting statistical analyses, and also being able to work with
more cutting edge developments such as machine learning algorithms); the IT architect
perspective (understanding the HR IT landscape and data warehousing); and the software
perspective (in-depth knowledge of working with analytical software, depending on whether
this is outsourced or conducted in-house) (Coolen and IJselstein, 2015). The last two perspectives
demonstrate the relevance of technology-enabled developments within HRM in general, and
specifically in HR analytics. One should not forget that information technology in HR analytics
is not only aimed at collecting and storing data, but also at linking and analyzing data, as well
as facilitating the convincing visualization and presentation of the results and insights.
Coolen (2015) even foresees that the next big thing in HR analytics is the use of business-user-
friendly self-service analytical software.
The above discussion at least shows that a one-dimensional perspective, focusing
exclusively on, for example, the statistical or Information Technology elements of HR
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analytics, provides an incomplete and likely distorted and unreliable picture of the future of
HR analytics. We therefore adopt a multidimensional perspective in studying HR analytics.
Inspired by a study on the State of HR analyticsconducted by the Center for Advanced
Human Resource Studies at the Cornell University (CAHRS, 2010), we will explore the future
of HR analytics using four central topics:
(1) application (goals, organizational themes, problems, and challenges of an application);
(2) value (added value as perceived by the organization, and influence on decision making);
(3) structure (positioning, organization, and involved actors); and
(4) system support (support from Information Technology).
Based on the identification of these core topics, we have refined our central research
question into:
RQ2. What will HR analytics look like in 2025 in terms of its application, value, structure,
and system support?
Method
Sample
We collected the data for this study from members of a Dutch HR analytics practitioners
group plus people suggested by these members. The HR analytics practitioners group was
formed in 2014 by HR analytics professionals from some of the Netherlandslargest
organizations who now meet on a quarterly basis with the main purpose of exchanging
knowledge and experiences related to HR analytics. Initially, 41 people were contacted by
e-mail or telephone to ask whether they were willing to participate in the research.
A questionnaire was sent out to the 29 people who had agreed to participate and, of these,
20 responded. In two cases, more than one respondent had jointly completed a single
questionnaire, resulting in 17 returned questionnaires. Most of the respondents had job titles
such as manager HR metrics and analytics,”“program manager HR analytics,”“consultant
HR analytics,and advisor HR analytics.The sample also included a manager HR
reporting,amanager HR operations,and an HR account manager,all of whom who were
responsible for aspects of HR analytics within their company. Further, two PhD candidates
who were employed by a company, and focusing exclusively on HR analytics, participated in
the survey. The 20 respondents were employed in a total of 11 large Dutch organizations, each
with between 4,000 and 90,000 employees, active in the banking, insurance, utilities, pensions,
biotechnology, petrochemicals, research, and consultancy sectors.
Data collection
Qualitative data were collected from open-ended questions through a survey that was
distributed by e-mail. The survey was made up of seven questions covering the four central
topics: application, value, structure, and system support. Two questions related to the
applicationtopic: What will be the main goals in applying HR analytics in 2025?and
On what organizational themes/problems/challenges will HR analytics be focused in 2025?
For value,there were again two questions: To what extent will organizations value HR
analytics in 2025?and To what extent will HR analytics influence decision-making in 2025?
Similarly, for structure,the questions were: Which internal and external actors will be
involved in conducting HR analytics in 2025 and what will be their roles and responsibilities?
and How will HR analytics be positioned/organized within organizations in 2025?Finally,
the system supporttopic was covered by a single question: How will information
technology support HR analytics in 2025?Each of the seven questions was followed up by
the question: and how does this differ from the current situation?
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Data analysis
The analytical hierarchy described by Spencer et al. (2003a) has been applied in analyzing the
data. Although this analytical structure can generally be applied in analyzing qualitative data,
it is especially suitable for thematic, largely cross-sectional analyses based on the interpretation
of meanings (Spencer et al., 2003a). The structure is made up of three phases: data management
(phase 1), descriptive accounts (phase 2), and explanatory accounts (phase 3). The aim of these
three phases is to gradually move up the ladder of analytical abstraction(Carley, 1990) while
progressing from description to explanation (Miles and Huberman, 1994).
Phase 1: data management. In the data management phase, data should be labeled,
sorted, and synthesized based on a generated set of themes and concepts (Spencer et al.,
2003b). In the present study, an index with an a priori set of themes was created. The index
comprised four main themes, based on the four central topics of the study (i.e. 1: application;
2: value; 3: structure; 4: system support). Since respondents were asked to describe how the
2025 situation differed from the current situation, each main theme was divided into two
subthemes: currentand future(e.g. 1.1 application current; 1.2 application future).
Finally, these subthemes were further divided, based on specific elements addressed in the
questions (e.g. 1.1.1 application current goals; 1.2.4 application future challenges).
Indexing the raw data involved labeling particular data elements with the appropriate
theme number as provided in the index (e.g. 1.1.1 or 1.2.4). The indexing was carried out using
Atlas.ti software and resulted in 320 labeled fragments. During the indexing process, two
additional themes were added to the index: dataand definition.The next step in the process
was to create a thematic chart. Thematic charting is a process that involves summarizing the
key point of each piece of data while retaining its context and the language in which it was
expressed and placing this in the thematic matrix(Spencer et al., 2003b, p. 231). In the
created matrix, each of the survey responses was allocated a row, while each subtheme was
allocated a column. Following this, the indexed fragments were then copied into the chart and
synthesized in additional columns to reduce the data to a more manageable amount. In line
with the guidelines of Spencer et al. (2003b), we aimed to retain as much as possible of the
original wording provided by the respondents in order to keep interpretation to a minimum
and also to retain material whose relevance was not immediately clear.
Phase 2: descriptive accounts. During the second phase of the data analysis, descriptive
accounts were created. In essence, this comes down to identifying key dimensions and
refining categories (Spencer et al., 2003a). The process involved looking for similarities
among the synthesized fragments across all cases (i.e. respondents) within a theme.
Subsequently, the fragments were sorted to distill the key dimensions within the range of
sorted data fragments, and to formulate categories. This process was largely iterative and
involved moving from the document in which the synthesized fragments were sorted, to the
thematic chart, to the original survey data, and back again. The categories and key
dimensions that were identified are presented in Table I.
Phase 3: explanatory accounts. The third phase of the analyses involved developing
explanatory accounts. During this phase, we sought patterns of associations within the data
and then attempted to account for why these patterns occurred. As described by Spencer
et al. (2003b, p. 252), this phase involves a mix of reading through synthesized data,
following leads as they are discovered, studying patterns, sometimes re-reading full
transcripts, and generally thinking around the data.For example, during the descriptive
accounts phase it became clear that respondents predicted a general trend toward decision
making becoming more evidence based, regardless of the discipline in which the decisions
were made. Delving deeper during this explorative accounts phase revealed that one of the
drivers of this trend was the entry of a new generation into management. The explanatory
accounts developed are described in the results section.
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2015 situation 2025 situation
Application
Goals
Establishing HR analytics
Proving added value
Exploring how to get started
Creating awareness
Building alliances
Laying foundations for analyses
Goals
Fostering fact-based organizational decision making
Developing evidence-based mindset within HR
Determining HR drivers for business outcomes
Proving effectiveness of HR analytics cycle
Transforming organizational models
Managing data privacy and increasing volumes
Analytical focus
Metrics and reporting
Historic and current situation
Simple statistics such as cross-tabulations
Analytical focus
Predictive analytics
Data integration
Standardization of measurements
Standardization of analytical approach and tools
Themes
Mainly driven by HR challenges
Often independent from business issues
Traditional KPI related
Themes
More overarching organizational themes
Largely the same HR elements in themes
Increased complexity of themes
Influenced by developments in data availability
Value
Relatively unknown
Added value largely unproven
Limited influence on decision making (due to
current general image and involvement of HR, lack
of readiness among HR business partners, and
general unfamiliarity with fact-based decision
making among business managers)
Established and valued discipline with proven impact
Strong influence on operational and strategic decision
making
Benefiting from general trend of evidence-based
decision making
Structure
Positioned within the HR function
Limited ties with other disciplines
Positioning
Scenario A: positioned within central HR function
Scenario B: positioned within central analytical
function (dominant scenario)
Internal actors involved
Analysts (executing analyses, securing quality of
insights)
Business (posing relevant questions, making data
available, interpreting results, supporting
interventions)
Consultants (translating business challenges into
research questions, advising on outcomes in a way
that makes them appealing)
Employees (how far do employees want to go?)
External actors involved
Educators (universities, research centers)
Knowledge partners (universities, consultancies)
Data providers
External data analysts
Data security parties (government, data protection
authorities)
System support
Fragmented and outdated IT landscape
Data warehousing lacks usefulness
Time-consuming data retrieval and preparation
Technology as main driver of HR analytics
System integration
From automation to artificial intelligence
Self-service analytics
From reporting to analyzing
Table I.
HR analytics: 2015
and 2025 situations
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Results
In this section, we will present the results of the analyses for each central topic of the study
(i.e. application, value, structure, and system support) in turn. For each topic, we will first
discuss the situation in 2015, followed by the predicted situation in 2025. An overview of the
findings is presented in Table I.
Application of HR analytics in 2015
The first central topic of this study is the application of HR analytics. Results concerning the
current situation can be grouped into three categories: the goals of HR analytics, its current
analytical focus, and the substantive themes on which the HR analytics is focused. Looking
at the current goals of HR analytics, respondents indicated that the primary current concern
was to actually establish HR analytics. Several underlying elements were mentioned.
First, it involves proving added value to the business by demonstrating that interventions
driven by HR analytics realize measurable business improvements. Second, it implies
exploring how and under what circumstances HR analytics can be applied within the
organization, and be implemented within its daily business routines. In also involves
assessing what capabilities are needed to execute HR analytics and what the HR analytics
department might look like in terms of number of FTEs, the job profiles required, and
responsibilities. Here, an organizations legal department and consultancy companies can
offer advice. The third element in establishing HR analytics is creating awareness.
Respondents indicated that the concept of HR analytics is often rather unknown within
organizations and, when known, often considered as an experimental platform within
HR something that does not warrant much attention from business management and
something they are reluctant to apply. One respondent indicated that most managers would
be happy if HR was just able to provide standard metrics. Given this situation,attempts
are made to create awareness among HR business partners as to the purpose and value of
HR analytics, and to explain that HR analytics is a tool for both HR and for the wider
business. A fourth aspect mentioned by the respondents was the need to build alliances,
both within and beyond HR. Within HR, the aim should be to deepen collaboration with its
disciplines (e.g. training and development and compensation and benefits) while outside of
the HR domain explicit and transparent collaborations are needed with linking departments,
such as control, and compliance. The final element identified concerning the establishment
of HR analytics was to create a foundation for conducting analyses. This might involve
gathering business cases that could be analyzed, obtaining access to data sources, and
acquiring the proper analytical tooling.
The second category that emerged from responses, concerns the current analytical focus
in the application of HR analytics. People involved in HR analytics currently spend the
majority of their time on the basic reporting and the calculation of metrics. Actual analytics,
where variables are compared to each other, only forms a very limited part of their work.
Further, such analytics mostly involve simple statistics such as cross-tabulations.
Furthermore, the primary focus is on gaining historical insights, rather than on prospective
insights obtained from predictive analytics. It was also indicated that the analytical focus is
on data that are already available within the boundaries of the organization, rather than on
data that may need to be gathered or that are available outside of the organization, such as
through social media data or from personal devices.
The third category covers the substantive themes on which HR analytics is focused.
Although there seems to be a consensus that HR analytics aims to contribute to business
outcomes, various respondents indicated that HR analytics is often focused exclusively on
HR themes, meaning that HR data are combined with other HR data, without relating them
to business outcomes. Explanations suggested by the respondents included that HR
primarily focuses on its own HR organization, and thus on solving HR-specific issues, rather
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than on wider business issues, and also that business data are often hard to gather or
impossible to link directly to HR data. Further, although several concrete areas were
mentioned where HR analytics was conducted (e.g. management development, employee
value proposition creation, strategic resource planning, talent management, and
performance management) it was also noted that the primary focus of HR analytics is on
traditional key performance indicators, such are absenteeism and the percentage of women
in management jobs. The focus on such themes may be explained by the continued focus on
reporting and metrics, rather than on true analytics.
Application of HR analytics in 2025
The results addressing the future application of HR analytics showed a very different
picture. First, the central objective in 2025 was predicted to be fostering fact-based
organizational decision making, referring to evidence-based ways of working and decision
making in general. This implies being information driven, rather than relying on gut feeling.
As a second objective, in a similar vein, respondents mentioned the development of an
evidence-based mindset, but specifically within HR. It was commented that HR is often
considered as a somewhat soft profession that relies on hunches, experience, and the course
of history. HR should become able to build stronger argumentations based on models and
numbers so that business disciplines with a more quantitative orientation take them
seriously. The third central objective mentioned referred to determining the HR drivers of
business outcomes. Such HR drivers could be in any HR domain, and should be considered
in the broadest sense possible. One respondent also indicated that the scope of HR analytics,
which is currently limited to an organizations own employees, should be expanded to cover
the flexible workforce. In relation to this, business outcomes were, however, mainly referred
to in general terms, such as improving the performance, efficiency, or effectiveness of the
organization, optimizing operational costs, or increasing the organizations impact on its
clients. The fourth goal concerned proving that interventions driven by HR analytics
realized measurable improvement. More generally, it was mentioned that the aim would be
to make HR analytics sufficiently mature to be able to complete the full HR analytics cycle,
which starts with analyzing a business case, followed by producing insights from these
analytics, implementing an intervention, executing a follow-up measurement, and
determining the costs and benefits of the intervention based on its results. The fifth goal
of HR analytics concerned the transformation of established organizational models.
According to several respondents, HR analytics could ensure lean and agile organizational
structures that could be established based on an optimal combination of people
characteristics and skills on the one hand, and strategic business targets on the other. Work
roles would then be tailored to human capabilities and characteristics, rather than the other
way around. The final goal, which is perhaps the most challenging, is to manage data
privacy as well as the growing data volumes. As one respondent stated, we can be sure that
more data will be available in 2025, new forms of data will be collected, and these will be
accessible to provide new information and new insights. Challenges in this respect will be to
determine which data to use, how to structure the data, how to protect them and, in the end,
how best to utilize them. As the respondent observed, most companies currently struggle to
maintain one separate database storing peoples data, not to mention the ten that may be
present in 2025. Complying with data privacy legislation and keeping the trust of the people
will be additional challenges. This is seen as particularly challenging given the thin line
between privacy intrusion and business progression. Data privacy will therefore be an
influential factor in shaping the future of HR analytics.
In the second category, the analytical focus, the central element mentioned by the
respondents was the focus on predictive analytics, or predictive modeling. Such analytics
could, for example, be focused on predicting peaks in employee turnover or changes in levels
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of engagement. In more general terms, the purpose of predictive analytics will be less
reactive. The second element within this category was the emphasis on data integration.
Data from outside the HR domain (e.g. financial and marketing data), but also data from
beyond the organizational boundaries (e.g. data from personal gadgets), should be
combined. With regard to data analyses, these fields are now operating as silos separate
worlds. The benefits of integration will be that it simplifies the analytical process, and
enables the analysis of more advanced, complex, and overarching strategic issues that cover
multiple businesses and staff functions. The third element related to the future analytical
focus concerned the standardization of measurements. On the one hand, standardization
implies clearly defining and conceptualizing concepts such as performance in order to
clarify what they mean and entail. On the other hand, it implies the development of reliable
and valid measurements for such concepts, with the purpose of facilitating organizations in
conducting cross-country and cross-cultural HR analytics. A fourth aspect that came out of
our analyses was the standardization of the analytical approach and tools. It was perceived
that HR analytics would reach a certain level of maturity by 2025, implying higher levels of
standardization, resulting in automated calculations and dashboards automatically
reporting the effect-sizes of relationships. Further, this maturity would also bring
forward a proven and optimized HR analytics toolkit for facilitating data preparation, data
blending, analyses, and storytelling. One respondent indicated that standardization would
also support further education and knowledge management on HR analytics, which would
be crucial in ramping up future HR analytics capacity.
When it came to the specific themes on which HR analytics would be focused in 2025,
a great variety of HR themes and practices were mentioned, including leadership, recruitment,
succession planning, strategic workforce planning, retention management, flexibilization,
virtual and self-steering teams, e-HRM, talent management, employability, employee health,
compensation and benefits, diversity, and engagement. Several respondents thought that the
themes dominating in 2025 would not be that different from the current situation. However,
the complexity of the cases would increase, the themes will concentrate more on overarching
organizational challenges, and HR themes will increasingly be addressed in conjunction with
business data and data from other disciplines. In addition, the developments in big data,
such as the accessibility of social media data, will influence the themes being addressed in
2025. Furthermore, and understandably, it was stressed that the themes, just as the related
challenges, would differ among organizations. Retail organizations may, for example, be more
focused on performance in terms of profit and business revenue, whereas non-profit
organizations, such as many hospitals, may be more interested in optimizing efficiency or
patient satisfaction. Some of the likely future themes for HR analytics mentioned by the
respondents are: the relationship between strategic personnel planning and sales or
productivity; finding the right balance between different types of contracts, such a permanent
contracts, fixed-term contracts, and contacts with self-employed workers; the impact of new
ways of working, such as flexible rather than fixed workplaces, on employee productivity;
performance in virtual teams vs performance in a more traditional setting; effects of self-
service e-HRM tools compared to shared service centers; and smart health, for example,
adjusting work pressures for people vulnerable to burnout.
Value of HR analytics in 2015
The second central topic of this study was value, focusing on the added value of HR
analytics as perceived by the organization, and the influence of HR analytics on decision
making. First, HR analytics is still a relatively unknown practice in many organizations,
both within HR and in the wider business. In general, analytics projects are often considered
to be something additional, rather than something elementary. A current challenge is to
explain what HR analytics actually is, and what its purpose is. Today, early-adapters of HR
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analytics are trying to convince their organizations of its value. Moreover, the respondents
stressed that HR analytics still has to prove its added value. It was stated that HR analytics
creates large expectations, but has yet to deliver concrete results. Organizations tend to see
HR analytics as a boat they cannot miss, but not as an activity that is already able to add
significant business value. HR analytics has yet to be embraced by the business, is not
prioritized, and therefore generally does not influence business decision making. Several
related causes were mentioned. One being that HR is generally not involved, or taken
seriously, in business decision making. This is likely to restrict the influence of HR analytics
since this usually originates within the HR function. Second, it was mentioned that HR
business partners were not yet ready to apply a more statistical and analytical approach in
their collaboration with the business. Further, one respondent stated that findings coming
from analytics are often hard to grasp by less data-savvy individuals, a description seen as
applying to many within HR. A third explanation was that basing decisions on an extensive
use of data and analytics was something new for many business managers, that there is
currently still plenty of room to rely on gut feeling. Overall, it was considered that business
managers often find it hard to understand, accept, and adopt the application of analytics in
decision making.
Value of HR analytics in 2025
With regard to the value of HR analytics in the future, the general perception among
respondents was that, by 2025, HR analytics will be an established practice within
organizations. It will have proven its added value, and even necessity, in tackling business
problems. Consequently, many comments were made arguing that HR analytics will be a
major influence in future decision making in both the HR and the business domains. Some
illustrative comments were: managers will consider HR analytics an unmistakable link in
underpinning and making strategic choices,”“in ten years, no single decision within the HR
domain will be made without a clear business case supported by statistical data,and
HR analytics will be seen as a viable addition to existing decision-making tools.At the
time, however, nuances were made with regard to the increasing relevance of HR analytics.
The main one was that the development of HR analytics will have benefited from the general
trend toward evidence-based decision making. Analytics will have become an inevitable
part of decision making and organizational improvement. One respondent predicted that,
around 2025, there will be a movement that is not dissimilar to lean six sigma, where
interventions and rewards will be accurately tracked and their contributions to business
results measured. One of the drivers for the expected general development of evidence-
based decision making was seen as the entry of newer generations into management
positions. Inevitably, as noted by another respondent, there will still be a core of decision
makers born around or before 1970 who will continue to rely on gut feeling. In some final
nuances, it was argued that the rise of HR analytics will depend on the extent to which HR is
able to demonstrate a track record in HR analytics, and the extent to which data sources can
be combined. However, HR analytics was widely expected to be of considerable value for
organizations in 2025.
Structure of HR analytics in 2015
The third central topic of this study was structure, focusing on the positioning and
organization of HR analytics, as well as the actors involved. In most cases, HR analytics is
currently organized as a specialized team and positioned within the HR function.
Most teams or departments, often called HR analyticsor HR metrics and analytics,are
still fairly new, and still exploring their ideal composition, role, and responsibilities. Where
organizations do have an HR analytics team in place, its typical size is about five FTE.
As noted earlier, the adoption of HR analytics mostly originates within the HR function, an
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approach which was heavily criticized by one respondent who argued that it is easier to
teach HR to a statistical programmer than statistical programming to an HR professional.
Nevertheless, the primary actor currently involved in HR analytics is HR itself. A few
respondents mentioned the role of the HR business partner being to liaise with
management. Advice based on HR analytics, and desires concerning reporting and
analytics, are discussed between the HR analytics experts and the HR business partner.
Connections linking the HR analytics team to other HR disciplines, and especially to other
disciplines outside the HR domain, are limited. It was mentioned that, in some cases, finance
departments were involved to facilitate joint reporting, internal departmentswere
involved because of data privacy aspects, and that early adopters among management
might play a part. It was stated that only a few progressive organizations are collaborating
internally with legal, finance, and marketing departments, and with works councils, or have
well-established ties with labor unions, specialized consultancy companies, and universities.
However, these are still the exceptions. One respondent explained that, in his organization,
the marketing and sales disciplines were collaborating in a big data team, but that HR was
not yet involved. In a similar vein, several respondents stated that there were several
analytics teams within their organization, but that these were mainly positioned within a
specific function such as marketing or finance. In addition, one respondent indicated that the
business units themselves initiated HR analytics projects.
Structure of HR analytics in 2025
The findings with regard to the future structure of HR analytics can be grouped into three
categories: the positioning of HR analytics in organizations, the internal actors involved, and
the external actors involved. First, concerning the future positioning of HR analytics, many
comments were made on where HR analytics would be positioned in 2025. In essence, there
were three groups of responses. Three respondents were not sure whether HR analytics
would be positioned within a company-wide big data team, or as a separate team within
the central HR function, or that an HR analytics team might reside anywhere within the
organization provided its link to the decision makers was short. A second group of seven
respondents argued that HR analytics would remain within HR, and have become an
integral part of each center of excellence covering aspects such as training, performance
management, and compensations and benefits. In this scenario, an intensive collaboration is
foreseen with the HR business partner, who would also need to have become more
analytical. However, several respondents argued that the HR function as we now know it,
will disappear, or at least change fundamentally. The HR function will have a stronger
quantitative orientation and there will be much less opportunity for HR advisors to rely on
gut feeling. This brings us to the third group of responses. Half of the respondents (ten)
expected HR analytics to have become integrated in an organization-wide analytical team or
function. This team will function independently of disciplines and focus areas, and identify
valuable business cases and opportunities to improve business performance. Such a team
will cover all functional areas of potential relevance, including HRs. Various labels for
such a team were suggested in the responses, including enterprise analytics, big data team,
central analytics center, and business intelligence team. Further, it was argued that
such a team could be positioned in an operations or a strategy department. The bottom line
is that, in this future scenario, HR analytics will cease to exist as a separate discipline within
the HR domain.
The second category of comments concerning the future structure of HR analytics
addressed the internal actors that will be involved. As one might expect, the analysts will
play a central role. Some referred specifically to HR analysts, others to general analysts and
stated that the analytical role could be fulfilled by, for example, statisticians,
econometricians, mathematicians, and data scientist types of people.This group would
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be responsible for not only executing analyses, but also for continuing to propagate the
added value of HR analytics and to speak up when research results should not be slavishly
followed, for example, if results were significant but not sufficiently robust. Further, it was
foreseen that these analysts would not be working in isolation but cooperating closely with
people in HR, finance, IT, marketing, and on the board, in order to acquire the necessary
information and data to develop useful insights and to influence decision making.
A second major internal actor is expected to be the business or, more specifically, board
members, directors, and line managers. Their role in HR analytics will involve formulating
relevant business questions, insuring that the relevant business data are made available,
and supporting interventions that are based on insights from the analytics. Further, they
will be interpreting the results from the analytics, and explaining their limitations and
nuances. A few of the respondents expected the HR business partner to have a supportive
role in this, and foresaw HR management playing a role in establishing HR analytics
position in the organization. These comments were based on a 2025 perspective where HR
analytics was still part of HR. Some of the respondents who foresaw a broader and general
analytics function, considered consultants (either internal or external) as an important actor.
These consultants would translate business challenges into research questions and have
some understanding of statistics in order to properly guide the analysts. They would need
the ability to advise on the outcomes in a way that was appealing to management, implying
that they should also be able to link the outcomes to the business strategy and to the
challenges facing management. A final actor that arose from the analyses, although only
mentioned by one respondent, was the employees themselves on which HR analytics is
focused. The development of HR analytics is, according to the respondent, largely
dependent on where one draws the line in using employee data to base decisions upon. In the
light of the growing importance of data privacy, the employee can be considered a very
relevant actor, as well as a potentially constraining factor.
Several groups of external actors could be identified within the third category of
comments regarding the future structure of HR analytics. The first group involved
educators universities and research centers. Respondents expected that, by 2025,
universities would be offering HR analytics courses and that the first graduates from a fully
focused data analytics curriculum would have entered the labor market. One respondent
felt the time was now right to establish studies that would not only deliver good candidates
for HR analytics jobs, but would also nurture research in this field. This brings us to the
second group, knowledge partners, consisting of universities or consultancies, that will
help in broadening knowledge. The third group mentioned consisted of data providers.
Our respondents predicted that, in the future, there will be more data sources and more
parties offering additional external data that will be being integrated in HR analytics.
Fourthly, external data analysts will be involved in data management, statistical analysis,
and benchmarking. The final group identified concerns parties involved in data security.
As one respondent stressed, there is a thin line between privacy intrusion and business
progression. Therefore, actors such as the government and data protection authorities will
be playing an increasingly active role in preventing HR analytics becoming another
big brother watching you.
System support of HR analytics in 2015
The fourth and last central topic of this study was system support, focusing on the support
of information technology for HR analytics. The current system support for HR analytics is
characterized by fragmented and outdated IT landscapes. Respondents commented that
multiple systems are used to store data, and that various tools and platforms are in use to
execute analyses and visualize results. Overall, the IT support for conducting HR analytics
is considered limiting. One respondent indicated that, in most organizations, legacy systems
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are hindering progress with HR analytics and that this can be considered as the main
obstacle today. Examples of systems currently used to support HR analytics are outdated
versions of business objects and Excel, with e-mail and SharePoint mainly being used to
distribute reports.
It was also mentioned that even if data warehouses are in place for HR analytics, their
usefulness is limited because they primarily contain HR data. Business data is often not
available, or only provided on an ad hoc basis. There is little visionary thinking on how to
develop a system architecture that facilitates the proper execution of HR analytics. This is
perhaps because, as one respondent indicated, there is insufficient contact between HR
analytics people and IT staff. The relationship can be classified as distant, with both
speaking a different language.
The main issue with this lack of system support for conducting HR analytics is
considered to be the time consequently absorbed in data retrieval, data cleaning, and data
restructuring and organizing that is on preparing the data for analysis. Although these
activities may be automated to some extent, a great deal of HR analytics amounts to
manual labor.
System support of HR analytics in 2025
In general, information technology is considered as the main driver of HR analytics in 2025.
It was said that, without good tools, it will be impossible to make solid analyses. Further, the
more an organization internalizes HR analytics, the greater will be the need to develop
supportive information technologies. Nevertheless, as one respondent stressed, there are
other drivers of HR analytics success, and IT is a means to an end and not a goal in itself.
By far, the most comments on future IT support for HR analytics concerned the
integration of systems. Respondents referred to organization-wide systems, data in one
single place, data from all disciplines centralized in one database, and the infrastructure for
analytics being in one spot. All the comments essentially boiled down to information
technology providing an infrastructure in which HR data could be combined with financial
and other business- and performance-related data.
Further, one of the main developments foreseen by the respondents concerned the
automation of HR analytics. They saw this as including, for example, the automation of data
collection by constantly running queries on the databases, and thus automatically reporting
metrics, and then making additional calculations. Nevertheless, they expected the data to
still require cleaning and to some extent interpretation, such that manual actions would still
be an important part of the process. However, at the same time, they recognized that
software is becoming increasingly smart, and that artificial intelligence is advancing.
Consequently, less human capacity may be needed for data management in the future.
Another related element raised by several respondents was the development of analytics
as a self-service for managers. This implies the ability to run HR analytics at any time, in
any place, and on any device or, as one respondent put it, doing HR analytics on the fly.
They saw the device-independent execution of HR analytics being facilitated by the use of a
data warehouse in which HR and business data are combined.
Furthermore, respondents indicated that the focus of supporting information technology
will shift from reporting to analyzing. Whereas current software is often focused on
dashboarding and displaying metrics, with progress mainly in terms of more advanced
reporting solutions, the respondents predicted a shift in focus to analytical solutions with
visualization capabilities and the statistical power to, for example, develop predictive models.
Discussion
The central question addressed in this study was: What will HR analytics look like in 2025
in terms of its application, value, structure, and system support? Based on the views of a
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sample of 20 Dutch HR analytics practitioners, we concluded that the future development of
HR analytics will probably be driven by an emphasis on integration. First, an integration of
data is foreseen. While the current focus of HR analytics is largely on HR challenges, and
thus primarily uses HR data, the future emphasis is seen to be increasingly on overarching
organizational challenges requiring data beyond the boundaries of the HR domain and even
of the organization as a whole. The integration of employee data with data from finance,
sales, marketing, social media, and personal devices is anticipated. Second, an integrated IT
infrastructure is needed to facilitate the use of multi-source data in analyses. Regardless of
whether this integration involves the implementation of organization-wide systems or data
warehouses, the data from all disciplines should be centralized in a single database to
facilitate their combined analyses. Third, the integration of the governance of the various
existing analytics functions is foreseen. Today, analytics teams in various disciplines tend
to operate rather independently of each other and, by 2025, a centralized analytics function
may very well be established. This function will then be focusing on identifying
opportunities for improving business performance while addressing all the relevant
functional areas, including HRs. Consequently, HR analytics as a separate team, function,
discipline, or practice could very well cease to exist. Paradoxically, taking an outside in
approach and transcending its own functional boundaries actually seems a prerequisite for
HR analytics to be of relevance (Rasmussen and Ulrich, 2015).
Technology was seen as the main driver of the development of HR analytics. This does
not surprise us given the historical overview presented earlier in this paper: HR analytics
came out of the HRM and Technologyresearch stream, and it is not difficult to see their
interconnections. Developments are seen in not only integrating the currently fragmented IT
landscape but also in automating data collection and data preparation activities, which are
currently perceived as taking up considerable time by the HR analytics professionals.
Furthermore, offering self-service applications to line management to facilitate analyses has
the potential to speed up the development of HR analytics considerably. Is the technological
skyunlimited? Probably not as powerfully mentioned by one of the respondents,
there is a thin line between business progression and privacy intrusion. Analytics using
employee data can probably only go as far, and develop as fast, as the employees accept.
While organizations have to comply with data privacy legislation, they may be more
dependent on establishing trust with their employees if they are to use theirdata for the
good of the business.
Limitations and future research
One of the strengths of this research may also be its main limitation. The sample used in this
study consisted of practitioners active in HR analytics, as either a manager, an advisor, or a
PhD candidate. One could argue that such a group knows best where HR analytics is
coming from, where it currently stands, and where it is heading, because of their own
experiences within their organization, or because they are involved in professional networks
with others adept in HR analytics, or because they are informed by the literature on the
dataficationof society, organizations, and HR. However, it may also be that such a sample
is biased to some degree. It would be valuable in future research to include business
managers who are supposed to be served by HR analytics, and by IT specialists who may
have better insight into how IT is currently supporting business analytics and therefore
may be better placed to predict how technology will facilitate the development of HR
analytics in the years ahead.
Another limitation concerns the inclusion of organizations from a wide spread of
industries. Given the rather early stage of HR analytics research, adopting such a broad
sample of organizations helps in gaining a general impression of the state of HR analytics
and its plausible and most likely developments over the next decade. However, a sector- or
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industry-focused approach could have provided deeper insights. For example, the extent to
which an organization already has a rather analytical or evidence-basedculture, or houses
analytical capabilities in its operating core, may very well-influence the degree of support
that can be expected from the business when introducing and promoting HR analytics.
It may very well be that within companies that generally have to forecast decades ahead,
for example, those in the petrochemical industry, that there is a more analytical corporate
culture than within utility companies which until recently were state-owned. Another aspect
that may influence the development of HR analytics within companies is the perceived
quality and image of the HR function. Is HR represented at the C-level and involved in
strategic decision making? Is HR perceived as a genuine business partner, or is it considered
to be a group of people who simply organize the recruitment process and reimburse travel
costs? Qualitative research on the development and future state of HR analytics could be
strengthened, deepened, and nuanced by classifying organizations in terms of their HRM
maturity and the extent of their analytical culture.
Finally, in this study, we did not explore the perceived likelihood of the predicted future
developments within HR analytics. Therefore, it remains unclear, for example, how likely it
is that HR analytics will remain within the HRM function rather than become part of a
centralized analytics function. Moreover, such developments may very well differ among
organizations, depending on the organizations governance, the maturity of their HRM
function, and so on. Scenario research or Delphi studies could teach us more about which
developments are likely to occur in specific contexts.
As outlined at the start of this paper, we are advocating a new wave of scholarly
research focusing on the development of the business discipline of HR analytics, including
its impact on the HRM function and on organizations as a whole. The results of the study
indicate that universities are viewed as one of the external actors involved in HR analytics:
as both an educator of future (HR) analysts and as a knowledge partner. The significant
infiltration of HR analytics into the HRM agenda, and increasingly also into the business
agenda, provides scholars with an opportunity to help steer the development of HR
analytics. However, the scholarly contribution should start by asking relevant questions.
Based on insights from our study, we believe that the relevant questions include:
How and to what extent does the decision-making influence of centrally positioned
analytics teams differ from that of analytics teams positioned within individual
disciplines?;What drivers are behind the development of an evidence-based
organizational culture?;What preconditions will allow employees to let organizations
use their datafor HR analytics purposes, and what are the boundaries?;To what extent
do organizations comply with legislation when conducting HR analytics projects?;
To what extent does the availability of self-service technologies for conducting HR
analytics influence decision-making, and to what extent are such technologies already
developed, implemented, and used?;What analytical software requirements would
facilitate self-service HR analytics in going beyond advanced reporting on metrics?;
In what way and to what extent does the involvement of external knowledge partners
and data analysts pay off?;How are external partners selected, and are there downsides
to external involvement?; and, finally, What can HR learn from the earlier
transformation of the marketing and finance functions into a decision science?.
Furthermore, basic descriptive research that provides insight into the current state of HR
analytics would be of value. Such research could provide insights into what HR analytics
teams look like in terms of their size and their roles and responsibilities, the extent to which
they actually focus on HR analytics as opposed to reporting metrics, and where such teams
are positioned within organizations, who supervises them, and how they are connected with
other disciplines. Such insights would enable the identification of trends in the development
of HR analytics, and therefore help in steering its future development.
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Managerial implications
Since the beginning of the global economic crisis around 2008, many investments in IT have
been put on hold. The speed of technological advances may have also discouraged or
prolonged decision making, simply because investments become risky with new
technologies possibly being outdated before they are even implemented. However, many
organizations are working with fragmented and outdated IT landscapes that need to be
replaced. One example of current trends is that on-premise enterprise resource planning
systems are increasingly being replaced by off-premise cloud solutions. Our study points to
the need to build a solid IT infrastructure that can support evidence-based decision-making.
Given that many organizations are in the process of making major IT investment decisions,
they need to be aware that improving their (HR) analytics capabilities will impose additional
specific requirements for new (HR) technologies. These requirements may include the
provision of self-service capabilities for conducting analyses, user-friendly automation of
data cleaning and data collection, the possibility of conducting genuine predictive analytics,
and the ability to report results in a visually attractive manner that can help convince
business leaders.
Furthermore, our study may help HR managers and business leaders to decide where in
their organization to position HR analytics capabilities. Many HR directors and managers,
and a steadily growing number of business managers, consider HR analytics as a boat they
cannot afford to miss. However, establishing an HR analytics team staffed only by HR
people, and positioning the team within the HR function, may not be a recipe for success.
As this study shows, such an approach may make it difficult to get commitment from the
wider business, to obtain data from other disciplines, and, more significantly, the HR
analytics team may even be dismantled before it reaches maturity. Regarding this last
concern, many respondents foresaw a centrally positioned analytics team transcending the
individual disciplines. Given the general trend of increasingly basing decisions on analytics,
other disciplines may very well-establish analytics teams. As such, it may be more effective,
and cost-efficient, to establish a central analytics team right away.
Concluding thoughts
Our research suggests that HR analytics will have a major influence on decision making in
organizations in the coming years. Further, HR analytics is likely to influence the
composition and role of HRM as a function. It can help to ensure lean and agile
organizational structures that are based on an optimum combination of people
characteristics and skills on the one hand, and strategic business targets on the other.
In so doing, HR analytics has the potential to transform organizational models. Overall, this
study has aimed to make a modest contribution to the understanding of HR analytics by
providing a glimpse into its future. As Ulrich stated, no one can predict the future course of
the HR profession,and we would add neither of HR analytics. Nevertheless, as HR scholars,
we should be part of it.
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Corresponding author
Sjoerd van den Heuvel can be contacted at: sjoerd.vandenheuvel@hu.nl
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Otomasi akan menjadi tantangan serius bagi dunia kerja ASN ketika seluruh jenis pekerjaan, khususnya yang bersifat klerikal akan digantikan oleh robot atau sistem. Sehingga demografi ASN pun akan mengalami perubahan signifikan, di mana generasi milenial mulai mendominasi, tidak hanya dari segi kuantitas, tetapi juga kualitas dan kompetensi yang lebih melek teknologi. Ke depan, rekrutmen ASN seharusnya menjadi lebih fleksibel karena menyesuaikan tantangan perubahan lingkungan yang begitu cepat. Demikian pula dalam pola karier yang memberi ruang berkembang yang cukup bagi ASN yang bertalenta tinggi. Demikian pula dengan pembelajaran berbasis e-learning akan mendominasi pemenuhan kewajiban pengembangan ASN dibandingkan metode klasikal. Ulasan mengenai tantangan ke depan ini disusun dalam tulisan yang kelima. Tulisan terakhir masih berhubungan dengan proyeksi dan tantangan manajemen ASN ke depan, namun mengangkat tema yang lebih spesifik yaitu bagaimana mendorong ASN memiliki kemampuan atau kompetensi people analytic. Dalam tulisan ini juga ditekankan pentingnya human capital management dalam manajemen ASN, melalui strategi, struktur, teknologi, SDM, dan kultur/budaya. Dalam hal ini ASN perlu melakukan upskilling dan reskilling agar mampu menghadapi tantangan ke depan. People analytic yang efektif dapat meningkatkan kinerja organisasi yaitu melalui enablers(pendukung), products, stakeholders dan tata kelolanya. Pada bagian akhir disajikan data profil ASN terkini yang bersumber dari Badan Kepegawaian Negara (BKN), sebagai referensi yang akan selalu diupdate dan disajikan dalam setiap penerbitan outlook. Bagian ini menjadi salah satu cara updating data ASN agar terjadi diseminasi dan advokasi ke mitra kerja atau pembuat kebijakan di bidang ASN secara khusus dan publik secara umum terkait profil ASN yang utuh.
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