Content uploaded by Indranil Bose
Author content
All content in this area was uploaded by Indranil Bose on Apr 28, 2018
Content may be subject to copyright.
1
AIMA Journal of Management & Research, February 2018, Volume 12 Issue 1/4, ISSN 0974 – 497
Copy right© 2017 AJMR-AIMA
Article No. 1
EVOLUTION OF HUMAN RESOURCE ANALYTICS:
AN EXPLORATORY STUDY
Dr. Indranil Bose
Dean-School of Business, University of Bolton, Ras Al Khaimah Academic Centre, Ras Al
Khaimah, United Arab Emirates
Dani Jose
Senior Lecturer, School of Business, University of Bolton, Ras Al Khaimah Academic
Centre, Ras Al Khaimah, United Arab Emirates
Abstract: Emerged in the old forms of traditional measurement sources of different sub-functions, HR
analytics have come a long way today. It has passed through the phases of measuring the sub-functions of HR,
staffing technique and criteria effectiveness, introducing comparative benchmarking on functional parameters of HR
at the industry level, predictive and prescriptive analysis etc. Different experiments, academic researches such as
‘Moneyball’ experimentation by Oakland A, USA; ‘Project Oxygen’ by Google etc. and emergence of big-data era
have added many twist and turns in the evolution of HR analytics till day. However, the continuum is still not ended
and several growth possibilities are yet to be explored as there is a good deal of confusion remains regarding the
specific focus areas. Still majority of the Fortune 500 companies are restricted to traditional analytics practice and
have not shifted to the advanced HR analytics, though a MIT study has shown specific proof of better impact caused
by HR analytics in progressive firms. Finally, it can be concluded that the factors like choosing the right purpose and
moment of interventions, adopting appropriate methods of analytics, collaborative interventions, scaling up of HR as
a point of interventions are properly required to implemented for optimum impact to be caused by HR analytics in
the organizations.
Key words: Human Resource Analytics, Evolution, Descriptive study
Introduction:
It has been a half century since Human Resource was known as the personnel function, and two
decades since Dave Ulrich challenged the same to get a seat at the table. As part of the evolution
of the function toward being more strategic, the discipline of Human Resource has moved away
from an emphasis on basic measurement to scorecards, engagement surveys and strategic
planning. Soundarrajan and Singh (2017) in their work on HR analytics have identified these
three main areas of measurements, practiced in HR as the foundation of emergence of HR
analytics. Despite, the enormous attention being paid to HR analytics since last more than one
decade, good deal of confusion still exists regarding where people should be focusing their
attention. Since, HR measurements can be traced back to scientific research that occurred at
some point in the past and what is not explicitly based on research usually has been found to
have a strong measurement component. Rao and Sinha (2016) have therefore commented that
data and analysis have been part of HR for as long as the function has existed.
Trends culminating to the growth of HR analytics:
2
AIMA Journal of Management & Research, February 2018, Volume 12 Issue 1/4, ISSN 0974 – 497
Copy right© 2017 AJMR-AIMA
Different pervasive trends can be related to the growth of HR analytics as a significant
instrument as it seems today. The first and foremost trend remains positioning of HR as strategic
identity. It has been observed that, HR has been searching for the longest time for the secret
source that will enable it to be more strategic. However, some studies have shown that over the
past two decades, surprisingly little change happened in the amount of time people in HR spent
on strategic activities (Lawler and Boudreu, 2012). A study conducted by Amstermin (2015) has
also shown that most of the middle and senior level HR executives of America have remained
unchanged more with transactional activities than strategic activities. There is a lot of basic work
that has always been and will always be part of the work, most of which does not seem
particularly strategic at first glance; making sure that people are paid properly, open positions are
filled up appropriately, performance reviews are conducted, development planning takes place
etc. However, under certain circumstances, these activities can be strategic, yet most of the time
these are about enabling the business to do its work by ensuring people process implementation.
Sometimes, though, when there is a critical business need best served by these traditional HR
practices, everyday HR engagements emerge as strategic. So, it can be said that whether
traditional HR is truly strategic or not often depends on the context. Therefore, one of the key
jobs for HR analytics has remained to understand the context of HR implementation. The second
trend can be identified in the rapid development and deployment of technology that makes it
easier to collect and store data in easily accessible formats. This includes both the proliferation of
survey vendors and internet based self-survey tools. It also includes the widespread installation
of enterprise resource planning (ERP) and other business IT systems that link together for joint
analysis of previously disparate data systems that were hard to integrate. The third trend is the
increased awareness of the importance of practicing evidence-based HR. This trend is however
found to be more aspirational than widespread. It has got more attention in the academic and
research communities than within the HR function itself. Yet the growing number of data
scientists and HR professionals with advanced degrees in industrial-organizational psychology
and other fields have provided a good deal of internal momentum toward taking a more
scientifically valid approach to defining and analyzing HR issues.
In the present situation, tons of data available that measure the execution of HR processes such
as headcount, vacancies, time to fill, completion of performance reviews, distribution of ratings
of performance, details on individual development plans ETC. However, HR analytics is still in
limited usage to describe these processes without embedding the initiatives in strategic context.
A recent study by Narsimhan (2016) has indicated that though HR analytics is becoming popular
rapidly in India, yet its strategic contextualization has remain restricted to primary interface
levels only in most of the organizations.
Evolution of HR analytics:
In 1950s, Peter Drucker wrote, ‘Some wit once said maliciously that [personnel management
comprises] all those things that do not deal with the work of people and that are not management
(Drucker, 1954). This reflects the truth that once HR struggled to be accepted as part of
management. J Barney (1991), for the first time clearly articulated on the resources an
organization has and their link to competitive advantage. Based on the resource based view
(RBV) theory, propagated by E.T. Penrose (1959), Barney’s work highlighted the aspects of
linkage between human resource management and business strategy. As per RBV theory, any
organization has tangible and intangible resources. Barney further elaborated that resources,
3
AIMA Journal of Management & Research, February 2018, Volume 12 Issue 1/4, ISSN 0974 – 497
Copy right© 2017 AJMR-AIMA
those can be sources of competitive advantage. He also, highlighted the preconditions for
successful implementation, which later became known as VRIO (Valuable, Rare, Inimitable and
Organized) framework. However, many scholars have criticized VRIO framework on the ground
that the having human capital alone is not sufficient for competitive advantage. According to
Cantrell, Benton, Laudal and Thomas (2006), language, behaviours and actions displayed by
human capital at workplace are critical to capitalize on the valuable resource, that is human
resource management. In the meantime, Baird and Meshoulam (1988) in their studies have
highlighted the need of alignment of HR policies with the organization life cycle stage and
business challenges, but also within. According to them, vertical and horizontal fit ensures that
the HR function become complementary to other business functions in achieving organizational
performance. Vertical fit ensures cross-functional collaboration between HR and other functions
leading to better appreciation of how HR contributes at the business strategy level in solving key
business challenges. Horizontal fit ensures collaboration between various HR sub-functions so
that their synergy helps HR contribute in achieving business objectives. As all of these
arguments make one point that human resource plays significant role in achieving business
results, the challenge lies in demonstrating the link between the HR, strategy and performance
using data. Though, HR’s tryst with data seems to be very old, yet relating the same to
organizational strategy and performance has been exclusively attempted since the concept of HR
analytics came into forefront. HR measurements in real sense and form, started with the
challenge of finding right persons in the organizations. Since world war II, the acute shortage of
skilled manpower was the biggest challenge to the organizations. US army started using skill
tests to find the people with the right attributes and this was later adopted at a large scale by
American Telephone and Telegraph Company (AT&T) and other American multinationals. In
the process, they adopted scientific measurement tools through skill and attribute testing.
However, since, mid 1990s, measurements were more integrated to strategic dimensions of HR
functions as more and more forward looking companies started adopting predictive models,
assessment models. In the meantime, Jac Fitz-enz (1978) published an article titled “The
measurement imperative”, where he proposed a radical idea. In it, it was proposed that human
resources activities and their impact on the bottom line could be measured. The same triggered
debate and interest by other scholars leading to more researches on measuring HR. Jac Fitz-enz’s
work has literally initiated the beginning of data capturing for key HR activities such as staff
retention, staffing, compensation, competency development etc. Subsequently, by 1990s, the
concept of ‘Benchmarking” became popular by incorporating comparison of data in the
measurement of HR functions. Gradually, it reached the pick in early 2000. But soon it was
found that benchmark was not providing any insights for action and the only benefit was a splace
how the company was doing compared to others. Also during the 1990s, emergence of human
resource accounting and utility analysis were witnessed, which also added new dimensions to
quantify HR, though with limited impact (Crestone, 2014). A more advanced and comprehensive
use of metrics was found in 2002 by Oakland A, the legendary baseball team of USA. Billy
Beane, the general manager of the team extensively used the metrics to select the team members,
which was then indeed a dynamic initiative in comparison to others (Coolen and Ijsselstein,
2015). Oakland A’s with a paltry budget of US 41 million were competitive with teams with
much larger budgets. It extensively used sabermetrics (player data based on extensive analysis of
baseball) in selection of players. Billy found that players with strong sabermetrics were
correlated to winning games than those players who were strong in traditional metrics like
batting average etc. Also it was found that sabermetrics also offered an opportunity to put
4
AIMA Journal of Management & Research, February 2018, Volume 12 Issue 1/4, ISSN 0974 – 497
Copy right© 2017 AJMR-AIMA
together the match-winning team, which was far less expensive. The result was phenomenal.
Oakland’s A was not only able to source better but less costly players, which in turn delivered
excellent results on ground. Based on this successful experimentation by Oakland A, Lewis
(2003) developed a path-breaking strategy on metrics based selection model, which is known as
‘Moneyball’ concept. Extension of the ‘Moneyball’ concept to the business world happened in
large scale since 2006. In 2009, global leader Google started ‘Project Ogygen’ to find out the
attributes of effective managers. Devenport, Harris and Shapiro (2010) published an article in
Harvard Business Review on ‘Talent Analytics’ and it was an instant hit across the globe.
Google’s “Project Oxygen’ also became globally known, when 2011, Google shared the results
of it highlighting data based findings on what a perfect manager stands. Soon thereafter, there
were series of research publications, which highlighted the benefits of using analytics in
workforce management. Frequently articles were found to be published in Wall Street Journal,
Forbes, Harvard Business Review, Fortune Magazine etc. As per a study by Patrick and Auke
(2015), a total number of twenty articles on different aspects of workforce analytics, including
the famous ‘project Ogygen’ experimentation. Some of these works however identified few
shortcomings. For example Garvin, Wagonfel and Kind (2013) in their research shown that the
some of the parameters covered in ‘Project Oxygen’, such as academic grades etc. have no
positive co-relation to the employee performance. However, more significant evolution at this
stage, which was related to ‘Project Ogygen’ experimentation of Google and subsequent
researches were a dynamic shift from traditional metrics based HR measurements to ‘predictive
analysis’ of HR analytics, which was a futuristic development.
Contemporary status of HR analytics:
It must be accepted that HR analytics has not reached the final stage yet and it is still at the stage
of ‘continuun’. Researches and experiments are currently being carried out on level of maturity
of HR processes, data quality and capabilities to implement HR analytics on wider aspects and
dynamics of human resource. Since last few years, a trend has been found, where more emphasis
is being given on showcasing metrics focusing on ‘efficiency’, such as lowering HR cost per
employee vis-à-vis increase in productivity per hire. Otganizations in service industries across
the globe including banking, hospitality sectors have gone one step further and they have
expanded ‘efficiency metrics’ into ‘effectiveness metrics’, which are indeed the strategic based
value addition to the discipline of HR metrics. Though Google leads the way the HR analytics
have been used through both predictive and prescriptive analysis, large numbers of world
renowned organizations such as Shell, Procter & Gamble, Morgan Stanley, Xerox, General
Motors etc. have also started using the same in their ways. However, still the total number of
organizations globally using advanced HR analytics is small and distributed unequally. Latest
study by Bersin, Leonard and Wang-Audia (2013) has shown that only 10% of Fortune 500
companies are using advanced analytics and out of this 10%, only 4% are using predictive and
prescriptive analytics and remaining 6% are still restricted to basic statistical techniques for HR
analytics. However a subsequent study by Bersin (2016) has shown that the number of
organizations using predictive analysis and advanced HR analytics has risen to 8%. He however
has held difficulty of scaling up of HR at the highest level responsible for this slow growth
among Fortune 500 companies.
5
AIMA Journal of Management & Research, February 2018, Volume 12 Issue 1/4, ISSN 0974 – 497
Copy right© 2017 AJMR-AIMA
Era of Big-data and its impact on HR analytics:
Big-data is considered as equivalent of telescope and microscope in terms of revolutionary ideas.
As telescope helped to see starts and galaxies, which are never visible to naked eye to the
universe and similarly, microscope made it possible to capture the life at cell level. In the same
manner, big data is extending helps to the business to capture unseen patterns and trends, to find
answers to unanswered questions and to deal with uncertainties. As it is widely accepted that
‘human factor’ is the key driver of business, quantification of anything and everything related to
people and process of the organization, which ultimately will effect the business performance has
become one of the most prominent intervention points of big-data based analytics. Traditionally,
HR has always been collecting volumes of data on various dimensions of human resources, such
as demographic, performance management, compensation/benefits, education history, job
location, training, talent movement through Human Resource Information System (HRIS).
However, with the emergence of advanced analytics, based on predictive orientation, top
managements started demanding to HR to show evidence of HR investments impacting the top-
line and bottom-line of the organization. In the era of big-data, the possibilities of more accurate
predictive and rational justifications from HR side and the complexity of demand from top
management have increased manifold, atleast in those organizations with a specific analytics
culture. Cascio and Boudreau (2011) pointed out that HR faced ‘big-wall’ in moving beyond
benchmarks and scorecards to demonstrates its ‘strategic impact’ in terms of causation and
organization effectiveness. Presently the promise of big-data to HR to help to cross this wall and
to demonstrate its impact on top-line and bottom-line by showing with data how various
elements of employee life cycle can drive revenue and profits and make people as a source of
competitive advantage are bearing fruits.
Conclusion:
In 2011, Massachusetts Institute of Technology (MIT) conducted a study on analytics, which has
found that top performing organizations use analytics five times more than the lower performing
organizations (La Valle et al. 2011). Many other studies since then have finally concluded that
HR analytics can help organizations to effectively deal with fiercely competitive business
environment at both tactical and strategic levels. It has found that at strategic level, typical
competitive challenges faced by any organization include productivity, innovation, global
scaling, lean delivery etc. Through proper alignment both vertically and horizontally using big-
data approach, the HR functions remain at the verge of exponential growth towards contributing
better impact to the overall organization. However, the factors like choosing the right purpose
and moment of interventions, adopting appropriate methods of analytics, collaborative
interventions, scaling up of HR as a point of interventions are properly required to implemented
for optimum impact to be caused by HR analytics.
References:
• Amstermin, S. (2015), Measurement and analysis of functional HR, Journal of
international HRD, Vol. 19(1), pp. 23-37
• Baired, L.&Meshoulam, I. (1998), Managing two fits of strategic Human Resource
Management, Academy of Management Review, Vol.13, pp. 116-128
• Barney, J. (1991), Firm resources and competitive advantage, Journal of Management,
Vol. 17(1), pp. 99-120
6
AIMA Journal of Management & Research, February 2018, Volume 12 Issue 1/4, ISSN 0974 – 497
Copy right© 2017 AJMR-AIMA
• Bersin, J.; O’ Leonard, K. & Wang-Audia, W. (2013), High impact talent analytics:
building a world class analytics and measurement function/ accessed April 09, 2017,
from http://marketing.bersin.com/rs/bersin/images/hita100113sg.pdf
• Bersin, J. (2016), Is software better at managing people than you are? Fortune magazine
• Cantrell, S., Benton, J.M., Laudal, T. & Thomas, R.J. (2006), Measuring the value of
human capital investments: The SAP case: Strategy & Leadership, Vol. 34(2), p50
• Cascio, Wayne F. & Boudreau, John, (2011), Investing in people: Financial impact of
human resource initiatives, Pearson Education
• Crestone, R. (2014), Data based decisions in Human Resource, Journal of Management,
Vol. 19(1), pp. 90-101
• Coolen, Patrick & Ijsselstein, Auke (2015), A practitioners’s view on HR Analytics/
accessed May 03, 2017, from https://linkedin.com/pulse/practitioners-view-hr-analytics-
patrick-coolen.
• Devenport, Thomas H., Jeane & Shapiro, Jeremy, (2010), Competing on talent analytics,
Harvard Business Review
• Drucker, P.F. (1954), The practice of management, Edition 1, Harper & Row
• Fitz-enz, J. (1978), The measurement imperative, Personnel Journal, Vol. 54(4)
• Garvin, D.A., Wagonfel, A.B. & Kind, L. (2013), Google’s project oxygen: Do managers
matter (case 313-110), Boston, MA, Harvard Business School in Soundarrajan, R. &
Singh, K. (2017), Winning on HR Analytics: Leveraging Data for competitive
Advantage, Sage
• La Valle, S.; Lesser, E. ; Shockley, R., Hopkins, M.S. & Kruschwitz, N. (2011), Big
data, analytics and path from insights to value, MIT Sloan Management Review, Vol.
52(2), pp. 34-41
• Lawler, Edward E. & Boudreu, John, D (2012). How HR spends its time: it is time for a
change, Centre for effective organizations report, Cornell University
• Narsimhan, D. (2016), How HR impacts bottom-line, HRM business review, Vol.7(3), pp-
143-151
• Rao, D & Sinha, K.K. (2016), Measuring Human Resource, Journal of Development and
Policy, Vol. 6(2), pp. 56-61
• Suoundararajan, R & Singh, Kuldeep (2017), Winning on HR Analytics-leveraging data
for competitive advantage, Edition 1, Sage publishing