Human Resources and the growing use of Metrics and Analytics

Abstract

HR professionals have increasingly become involved in organizational strategy (Dessler & Chhinzer, 2016), relying on human resource information systems (HRIS) to provide metrics and analytics in order to make strategic decisions (Hussain, Wallace, & Cornelius, 2007).  Using metrics and analytics in HR presents a unique and sometimes controversial scenario, due to the complex type of asset being measured and quantified: human beings.  This article aims to show that the use of human resource information systems for strategic decision making remains highly flawed, lacking sufficient evidence to justify its enthusiasm in mainstream business.  It argues this point by contrasting articles which claim favourable, unfavourable, and neutral assessments of strategic HRIS solutions, drawing a conclusion based on the most credible evidence.   

Introduction

Since the 1990s, human resource information systems have grown to dominate many facets of the HR profession.  A survey by Overman (1992) predicted that such systems would provide faster information processing, improved planning and program development, and enhanced employee communications.  Looking back, it is plain to see that most of these predictions have come true.  In fact, using a computer system to replace most of these filing cabinet practices hardly seems impressive in modern terms.  Behind the scenes, research was already being done to quantify HRM for strategic purposes.  Tootel, Blackler, and Toulson (2009) artfully summarized this research:

The use of costing and utility models provided some basic tools for demonstrating how HRM can add value to the organization in terms that are familiar to senior business managers and shareholders. The work also included attempts to measure people as assets in accounting terms. However, the limitations of asset accounting definitions created difficulties in real life experience given the human element of labour.  

Despite these early setbacks at quantifying human labour, momentum of such possibility continued.  Ten years later, one study surmised that using the right HRIS could even improve shareholder value (Brown, 2002).  Generally speaking, it was clear that the HR profession was no longer satisfied with mere administrative efficiency (Beadles, Lowery, & Johns, 2005).  There was an increasing pressure to support the strategic objectives of management, with a focus on shareholder value (Hussain, Wallace, & Cornelius, 2007).

Defining Strategic HRIS Solutions

HRIS solutions come in a variety of scope and scale, not all performing strategic functions.  This article concerns itself with enterprise-level HRIS solutions, which store and process everything from standard employee data (basic identity, pay-scale, position, etc.) to more intimate details (performance appraisals, company email, company social networks, web searches, and more).  HRIS solutions are even evolving to track employee mobile devices outside regular work settings. The end game is to store as much employee data as possible (within legal limits), then use analytics to create quantifiable formulas of, for example, a “good leader.”  Sometimes the desired calculation is more subtle, such as when McDonalds used HRIS analytics to study the attitudes of employees toward its managers, separating by age demographics (Angrave, Charlwood, Kirkpatrick, Lawrence, & Stuart, 2016).   

Success Stories

Surely, no business would adopt HRIS analytics, often costly and complex, without proof of success? There are many such success stories.  Google has built an analytics suite designed to measure employee underutilization, and Microsoft has developed a method of correlating the graduate school origin with workplace performance.  Alliant Techsystems uses analytics to determine the probability that an employee would leave, while IBM’s workforce analytics is used to identify “idea leaders” within a company (Dessler & Chhinzer, 2016).   Throughout the beginning of the century, a steady stream of favorable literature began to appear, such as the Oracle (2011) whitepaper illustrating the benefits of its analytics capabilities and how it can help bring a return on human capital investments.  A practical example included an offshore drilling company who was able to establish an analytical link between specific leadership qualities and lower employee turn-over, which resulted in fewer accidents and decreased maintenance time (Rasmussen & Ulrich, 2015).  Exceptions notwithstanding, the problem with literature in favour of HR analytics is that it is often written by IBM, Oracle, Microsoft, or other HRIS manufacturers (Angrave et al., 2016).

Criticism

Some of the strongest criticism of HRIS analytics comes from within the HR industry itself.  HR practitioners are often hesitant to use metrics or analytics to summarize the value of people, noting that it reduces people to widgets (Grossman, 2000).  As HRIS solutions became prevalent in the early 2000s, several studies emerged depicting unfavourable results.  One study revealed that less than half of HR directors believed HRIS solutions had improved strategic decision making, with only 20 percent believing that HRIS represented a competitive advantage (Kovach, Hughes, Allen, Pagan, & Maggitti, 2002).  A survey of 210 HR executives in prominent Canadian corporations showed that most systems are “still being used more for administrative ends than for analytical or decision support ends” (Haines & Lafleur, 2008).  Many HR practitioners believe that such metrics and analytics are a step in the right direction, but that HR should move toward expressing value not just in numerical terms, but human terms as well (Tootel, Blackler, & Toulson, 2009).   There is a real danger of HR becoming too technology focused, opposed to employee focused, being viewed as a management “tool” rather than a strategic partner (Stone, Deadrick, Lukaszewski, & Johnson, 2015).

Other Findings

Research on HRIS sometimes presents a unique point of view of how the technology is used, rather than a judgement on whether it fails or succeeds at improving company strategy. For example, some studies revealed that HRIS metrics and analytics were not always valued for the data they provide, but rather by the user-friendliness of the system itself.  The easier a system was to navigate, the greater its perceived usefulness was to management (Winkler, Konig, & Kleinmann, 2013).  This yet again illustrates the reality of complex human analysis running against management pressure to generate simple, quantifiable data.  Another study in New Zealand observed the same conclusions, that “HR practitioners encounter difficulty expressing HR in terms of metrics…the issue we suspect is due to the difficulties in having HR measures that are both meaningful and credible to key business stakeholders” (Tootel, Blackler, & Toulson, 2009).  

While it is true that HR professionals play a large role in lowering labour costs (Dessler & Chhinzer, 2016), it should not always rely on HRIS solutions to deliver on such objectives.  One particular study witnessed such a problem in action: A clothing retailer used workforce planning software and routinely attempted to keep staff levels at optimum numbers, minimizing labour costs.  However, the HRIS had not calculated for the quality of the labour involved, for the fact that human beings are more careless and prone to lower morale when stretched to capacity.  The net result was a reduction in store cleanliness and avoidance of some tasks such as changing appropriate store signage (Haque, 2015).  In contrast, a different study concluded that adding labour in order to reduce employee strain eliminated these problems (Ton, 2009).  Of course, neither study is the conclusive answer to such a problem.  Rather, from a strategic standpoint the larger problem is the fact that HRIS solutions discouraged experimentation of this type of business activity, often relying on the machine to do the thinking (Angrave et al., 2016). 

Conclusion

Despite mixed results, HR analytics is certain to remain a key ingredient in HR and company strategy. We know that HR professionals, in tandem with HRIS solutions, can indeed generate useful data that can enhance company strategy.  The problem, and unfortunate trend, remains that management expects this data to seamlessly blend with other analytical systems, such as from accounting or engineering departments.  It suggests that many managers and executives, despite wanting HR data for strategic purposes, still exhibit a “limited patience for or understanding of HR” (Angrave et al., 2016).  Until it is accepted that HR data requires a unique set of criteria, a numerical and human factor, the same risk of misinterpretation or misuse will continue.  As the HR profession continues its trend as a management partner, it needs to be afforded more autonomy and authority over the data it is responsible for.  Interpolating any human data, manipulating it to fit into accounting terms, appears to be a lofty and misguided strategy in and of itself.  As the sociologist Cameron (1963) stated:

It would be nice if all of the data… could be enumerated because then we could run them through IBM machines and draw charts as the economists do. However, not everything that can be counted counts, and not everything that counts can be counted.

Works Cited

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