Table of Contents

Introduction. 1

Human Resources (HR) Analytics Defined. 1

Context – What it Is.

VUCA Context

Mindset and Approaches to Thinking.

Using The Cynefin Framework to Determine Context

Introduction

This chapter discusses new and emerging thoughts around HR analytics and their usefulness and applicability to an HR operational environment which worldwide has been shifting presenting an increasingly turbulent context often described as volatile, uncertain, complex, and ambiguous (VUCA).  Against this disruptive context, applicability and implications of HR analytics on business are discussed using systems thinking and the Cynefin framework. To put things into perspective, I start off by defining key terms that are critical as a foundation for better understanding of the discussion around HR Analytics in a VUCA context.

Human Resources (HR) Analytics Defined

HR analytics, sometimes known as people analytics, or workforce analytics as presented by Erik van Vulpen (2024), the founder and Dean of Academy to Innovate HR (AIHR,) is to do with quantifying the impact of HR-related factors on business outcomes to unravel the ‘why’ behind an occurrence and elucidate the consequences of that event (Talent Management Institute (TMI), 2023). According to van Vulpen (2024)

Data is a hot commodity in today’s marketplace. While digital tools generate a vast amount of readily available information, data holds little value in its raw form. That’s where HR analytics comes in – transforming data into insights for resolving workforce and business challenges.

HR leaders use HR analytics to provide insights into recruitment decisions, employee engagement and retention, time tracking, employee productivity and performance, succession planning, diversity, equity, inclusion and belonging (DEI &B), leadership development and employee coaching and development (TMI, 2023). Making use of HR analytics enables HR leaders to use evidence-based data to assess the impact of HR policies on business, thereby improving quality of decisions.  In addition, HR leaders are able to timely establish problem areas and provide corrective action which saves costs and improves efficiencies.

When the context is stable and predictable, HR leaders use HR analytics to inform not only decision making but help present a defendable and sound business case for HR interventions. At the same time, HR leaders are equipped with data to enable proactivity in the navigation of change, handling disruption, and uncertainty. TMI (2024) presents four types of HR analytics that I will discuss based on their applicability to the VUCA context. These are outlined in Table 1:

Table 1: Types of Analytics: TMI (2023)

HR analytics is different from HR metrics and Human Resources Information Systems (HRIS) in that HR analytics involves the collection, analysis and interpretation (TMI, 2024) of HR-related data to provide insights that enable HR leaders to make strategic decisions, while HR metrics is raw data that focuses on specific measurements used to track and evaluate various aspects of the HR function for example staff turnover rate, staff engagement rate, and staff retention rate. HR metrics do not provide the ‘why’ of data whilst HR analytics provides insights into data driven decisions by informing leaders on data trends and patterns that help in decision-making.  As for HRIS, this refers to HR software that collects, stores, and manages HR data for example, Workday or SAP or Oracle enterprise resource systems. This chapter focuses on HR analytics in a VUCA context.

Context – What it Is

The concept of context and its implications on HR analytics can be understood as a fundamental lens. Generation of HR data into useful information suitable for strategic decision-making by HR leaders is influenced by the situation in which it occurs. The whole situation that surrounds and informs a choice or action is its context. From an HR analytics perspective, prevailing examples are analysis of current demand and supply of labor versus trends in a historical timeline, existing market position on salaries and benefits against past trends,  present people performance data and influences on people productivity over some past range of time, prevailing employee retention and turnover rates compared to a historical  time period, current employee engagement rates in comparison with  the past few years, and employee training return on investment over a period of time. HR leaders usually benchmark historical company trends against those of the same industry (in a given time period) to establish, for example, the level of competitiveness of salaries and benefits, their company staff turnover rates vis-à-vis industry rates to gauge whether they are aligned or not, or whether they are strong or present opportunities for improvement. Having staff turnover rates that are higher than industry benchmark ranges is against the quest to the organization’s desire to be perceived as an employer of choice, negatively affects the company’s brand positioning, while undesirably affects employee retention which in-turn increases recruitment costs.

 As alluded to earlier, the prevailing business context is described as VUCA marked by high levels of turbulence.  This context is in direct contrast to the stable, orderly, predictable context that enabled HR leaders ease of dependence on historical data and HR analytics for decision-making, problem-solving and forecasting the future with some degree of accuracy.

VUCA Context  

In the late 1980s, the United States Army War College first used the term VUCA to describe the global changes occurring after the Cold War. Ever since then, business has used VUCA to describe strategic contexts for many other domains beyond the military. Bennett and Lemoine (2014) presented VUCA as descriptive of an array of new concerns and challenges facing the world, characterized by unprecedented changes, both helpful and harmful, that shifted the workings of the world dramatically away from the course they had followed even in the recent past. The business world has changed dramatically over the past few decades, and we now live in a connected society where change can be fast paced, constant, and unpredictable. Elkington, et al., (2017) agree. They contend that operating in a VUCA context does not necessarily mean that the world is bad, unstable, or “out of control” (p.2). Instead, the authors argue that VUCA refers to specific but increasingly “normal” dynamics of the 21st century that impact trade and industry. They elaborate that (p.2):  

These dynamics are being driven by a marriage of six mega-trends: globalization, technology, digitalization, individualization, demographic change, and the environmental crisis. These dynamics are creating disruption while triggering innovation and change at a breakneck pace. In this way, VUCA is becoming the “normal context” for leadership and requires leaders to adopt appropriate perspectives and skill sets. 

Indeed, globally the prevailing environmental context has become VUCA, characterized by increasing and worsening complexity that the website MindTools (2023, p.1) describes as: 

Rapid advances in technology created an environment where the internet, smartphones, and social media are ubiquitous, and global events such as the 2008 financial crisis, the COVID pandemic, and … conflict in Ukraine have increased the sense of turbulence, danger, and unpredictability. 

This VUCA context demands innovative ways around collection, analysis and interpretation of HR data. Reliance on best practices or applying a one-size- fits all approach to HR analytics will not work in a VUCA context. As described by KPMG International (2019)’s Future of HR survey, “the next-generation HR function has an essential role to play, replacing best practices … with bold strategies…tools, processes and metrics (p.13).” Against complexity, HR leaders must innovate new ways of managing HR analytics, while at the same time establishing new metrics that can be used for data analytics. Otherwise, traditional metrics based on historical data, like staff turnover rates, employee compensation levels based on fixed job-descriptions, talent assessment statics based on McKinsey’s 9-box talent mapping metrics are rendered useless against a context characterized by chaos and complexity. Against this context HR leaders have to establish new, never before used metrics, not for analysis but for synthesis, considering data interdependences and interrelatedness.

Within VUCA, a volatile context is closely related to instability, where there is high likelihood of a given factor to change quickly, frequently, and/or significantly while an uncertain context, means unpredictability, leaving no one exactly sure of what will happen next, or the results for a given decision completely unknown, despite any amount of research or predictions.  A complex context refers to a state of being difficult to understand.  Complexity ties up all the other elements of VUCA and the relationships between these factors. The last one, ambiguous context refers to the state of unclearness which implies that there are no clear traits, and business is working under unknown conditions (Bennett and Lemoine, 2014). Disorder, variety, and diversity are typical characteristics of the context that is VUCA.

This turbulent and disruptive context  impacts HR analytics in three ways: 1) the structured and orderly collection, analysis and interpretation of data is rendered undoable, and 2) the form (analysis based on historical data) is irrelevant because the situation and problem is presenting itself for the first time, with new and emerging patterns that have never been experienced before, 3) and nature (data type based on best or good practices, cause-and-effect and one-size-fit-all) is also rendered useless because the events are new, unique, and have never been seen before while also presenting no relationship between cause-and-effect, are diverse and disorderly.

Nevertheless, despite the VUCA context, data still plays a critical role as a strategic source of information to enable successful decision-making and problem-solving.  In a VUCA context, besides the challenge of using HR analytics for problem-solving and decision-making based on irrelevant historical data in an unpredictable context, the other big elephant in the room for HR leaders is the prevailing mindset among HR professionals. While SHRM (2023) presents a common understanding and acknowledgement among HR leaders that the operational environment has become highly VUCA, the challenge is the continued use of methodologies and approaches designed, formulated and implemented for stable, ordered and predictable contexts to solve VUCA context challenges, which includes reliance on historical data analytics in an unpredictable context, where it is impossible to project the future with any degree of certainty. Cabrera et al agree. The authors posit that while the context of the business world is unstable, unpredictable and complex, the solutions applied to problems too often are meant for predictable and stable contexts with simple or complicated problems. The authors present that the prevailing approach to thinking about challenges in the everyday world is linear, anthropocentric, mechanistic, and ordered (LAMO) while the context in which problems occur is volatile, uncertain, complex and ambiguous (VUCA). Figure 1 illustrates the mismatch between VUCA context and LAMO thinking. 

Figure 1. Mismatch between VUCA and LAMO

Against VUCA, HR leaders must move from data analytics to data synthesis and innovate new methodologies of synthesizing data to make sense in a disruptive environment. The difference between data analysis and data synthesis is that, while the former is involved with breaking down information into separate pieces and looking at the pieces individually (reductionist approach), the latter is to do with putting information together, taking into consideration the interdependent, interrelated and interconnected parts of the data metrics and information (expansionist approach) in determining solutions and decision-making.  For example, one of the topical issues facing HR leaders at the workplace is how to keep employee engagement and teamwork levels high in a post COVID-19 era, that saw the birth of remote working taking precedence over in-person work attendance in most administrative jobs. From a data analytics point, HR would go back 3-4 years back to analyze the trends, establish drivers of engagement and teamwork then based on relationship between cause-and-effect, fall back on best-practice to inform them on historical initiatives that helped improve engagement and teamwork in the past. Meanwhile, from a data synthesis perspective, the HR leader would first establish the type of context,  once confirmed as complex (meaning not easily understandable), immediately appreciates that there are a lot of interdependent, interconnected and interrelated parts that must be considered in establishing levels of employee engagement and teamwork, is alert to new and emerging trends,  has to innovate new data points, which includes completely moving away from certain metrics to introducing new ones to dissolve the mess.  This is because a complex context calls for new types and forms of data as historical data points/metrics shift in terms of importance and relevancy due to new, never before heard of forms of interconnected and interrelated data metrics emerging. This requires a new set of competences for an HR leader. These competencies include:

• Ability to synthesize multi-related and multi-layered data forms.
• Competence in understanding that historical employee engagement drivers shift against chaos and complexity.
•  Proficiency in comprehending the importance of context in problem-solving and decision-making,
• Capability in establishing differences in contexts, and applying the correct mindset, ideal for that context, for effective problem solving and decision making.

A disruptive VUCA context calls for new AI data generation technology. Prevailing AI methodologies that are meant to analyze historical data are rendered useless in a complex context. This turbulent context presents opportunity for new AI that can synthesize HR data in relation to the current interconnected and interdependent factors to the issue in question, tracking patterns that HR leaders can monitor and use for problem-solving and decision-making. An example of HR data synthesis could be at the emergence of the COVID-19 pandemic, AI data synthesis would include real time statistics of new cases emerging and interrelated to those that recently travelled, versus not travelled out of the country, by gender, by age, and by country visited. Or in the moment reduction of cases among those vaccinated versus those not vaccinated, or deaths in relation to vaccinated versus non vaccinated, women in relation to men, young people in relation to older people based on current data that can be synthesized for decision-making and problem-solving.

Mindset and Approaches to Thinking 

Worldwide, organizations are increasingly faced with problems and situations that are interconnected, non-linear, volatile, unpredictable uncertain, complex and ambiguous. Using HR analytics for collection, analysis and interpretation of data as a basis for decision making and problem solving is rendered irrelevant in this kind of situation. Against VUCA, HR leaders must move from the prevailing analytical mindset to systems thinking.

Mindset implies mental inclination, that includes attitudes, beliefs and world view that shape how an individual makes sense of the world and themselves. Carol Dweck (2008) defines mindset as a mental attitude that determines how one will interpret and respond to situations. One’s mindset/world view is metaphorically like glasses that one wears through which everything visually experienced, seen, and read is interpreted. But as not all experience is visual, it is also like wearing earphones through which everything heard is also interpreted. This means that two people present at the same event but holding differing mindsets may perceive and understand what is seen and heard differently.  Mindset for an individual, team/group and organization is the fundamental cognitive orientation encompassing the whole of one’s knowledge and point of view. A person’s mindset/world view can include not only current reality, but also anticipation and expectations of future and ideal states, normative values, emotions, and ethics. The prevailing mindset among most HR leaders is based on conventional ways and approaches, of which HR analytics is part. My argument is that for challenges within a stable context, HR leaders can use the prevailing mindset for decision-making and problem solving. However, once the context changes to VUCA, the mindset should shift from HR analytics to HR data synthesis, using methodologies and tools aligned to systems thinking.

Approaches to Thinking. The mode of thinking needed to address HR analytics in different kinds of contexts also differs. Problems in ordered and well-structured contexts are best addressed and may be solved by using analytic thinking paired with analytic, research-based methods and tools. The is where HR analytics falls. However, decision-making and problem-solving that occur in unordered and poorly structured contexts are best addressed and may be solved or dissolved by using systems thinking paired with systems-informed, design-based methods and tools. This is where HR data synthesis should take place.

Analytic thinking is a mode of thinking with an underlying mechanical or biological mental model. Analysis – originally from a Greek word that means to break down into small parts – is a reasoning process in which higher-level (difficult to understand) phenomena are derivable from lower-level elementary parts. Analysis posits linear causality, the notion that the occurrence of any event or situation always has a preceding and discoverable cause. Through this lens of thinking, scientists, and non-scientists including HRM professionals approach a problem by seeking to clearly define it. The analytic process continues by trying to understand the defined problem which is often complicated by reducing and simplifying it into smaller problems while seeking the “root cause.” Once discovered, the problem component/parts are studied individually in order to understand the behavior of each part. The deconstructed parts are then added together to form a linear sequence of causes and effects that demonstrate what happened. From this understanding of parts, solutions can be identified and selected.

Systems thinking is another mode of thinking. Similar to analytic thinking, it applies a mental model to how one perceives, understands, and makes decisions about situations encountered. Rather than reducing or simplifying a problem into smaller problems or looking for a root cause, systems thinking or synthesis – also originally aGreek word meaning to put together; combining elements to make a whole that is new and different than the parts considered separately – holds that situations are integrated wholes that if deconstructed cannot be understood. Systems thinking applied to problems holds that a problem may not be easily defined because of the complexities that characterize it. Furthermore, a problem’s influences may not be inside it but rather may be outside it. To understand these, expansionism rather than reductionism is needed, that is, a process of seeing the patterns of interactions and interdependencies within and outside the problem. Systems thinking is a distinctive model that acknowledges that interactions of elements in a system including people, data, technologies, and other forces produce new and emergent outcomes that may be unforeseen and unpredicted. Jackson (2019) describes systems thinking as “the study of wholes, and their emergent properties (that) … insists that a wide variety of stakeholder perspectives is considered; (and that) systems thinking is the only appropriate response to complexity.”

Systems thinking is not part of the defined discipline of conventional HRM thinking or practice despite the demonstration more than 50 years ago of the “foolishness of applying reductionism for complex systems which confuses the parts for the whole of a problem.” Systems thinking and HR data synthesis approaches are recommended for situations that are unordered, poorly structured and have problems that are complex and chaotic that is, VUCA context challenges.

Using The Cynefin Framework to Determine Context

The Cynefin Framework refers to a sense-making approach that helps in understanding how context variations require corresponding changes in the manner of decision-making and problem-solving. Conceived and developed by David Snowdon in 1999, while working at IBM Global Services, Snowdon extended the framework in 2002 when he founded and directed the IBM Cynefin Centre for Organizational Complexity. He has continued to promote it through his consulting company, Cognitive Edge. He called his sense-making conception the Cynefin framework, where Cynefin, is a Welsh word pronounced Kun-Ev-In, and refers to the contextual habitat, place, or domain where one is located when confronted with a challenge. According to Snowdon (2002), contexts exist along a continuum from ordered and well-structured (also described as predictable) to unordered and poorly structured (also described as unpredictable). Within ordered contexts, are problems that are simple, clear or obvious and also problems that are difficult and complicated. Within unordered, poorly structured contexts are problems that are complex or chaotic. At the center of these contexts is disorder. Figure 2 below shows an illustration of the Cynefin Framework.

Figure 2. 2017 Cynefin Framework (Kempermann, 2017)

Figure 3. The Cynefin Framework: HR Analytics

Ordered Predictable Contexts

In an ordered, stable and well-structured context, some problems are simple and easily understandable. This means that the cause-effect relationship within a defined problem is seen as clear and linear such that an outcome or behavior is directly linked to certain causes. The leader senses and defines the problem, categorizes the nature of the cause-effect relationship, then responds using benchmarking and best practices. The expected outcome would be a solved problem.  

Within the same ordered context, some problems may be difficult and complicated which means they have many parts and subparts making the problem difficult to understand. Causes and effects are presumed to be related but may be difficult to see because they are indirectly linked. In this context a leader will sense, analyze, and respond. This means the problem is first defined as clearly as possible. Once completed, the problem is analyzed (the word analysis from the ancient Greek ἀνάλυσις means to break into small parts; to deconstruct) to its most basic or simple form often with accompanying essential and desirable objectives which are to be met. The response would be to seek a solution that meets those objectives and solves the problem. Decision makers rely on expert advice and those with more experience in the organizational hierarchy – for help with solving the complicated problem. Complicated problems benefit from an analytic approach that reduces the problem to its root cause(s) then applies good practices derived from research-and evidence-based solutions that are expected to solve the problem.  It is within the simple and complicated contexts that HR analytics falls into.

Unordered Unpredictable Contexts 

Some problems and opportunities exist in contexts that are unordered, dynamically active, poorly structured, and unpredictable. Problems in this kind of context are described as complex, or chaotic, but also wicked (Churchman, 1967; Rittel & Webber, 1973) and a mess (Ackoff, 1974; 1981). Such challenges have seven characteristics associated with their context which include: (1) incomplete, contradictory, and changing requirements that are often difficult to recognize which make them difficult or impossible to solve; (2) There is no definitive formulation of the problem due to interdependencies, the problem is not understood until after the formulation of a solution; (3) Solutions do not present right or wrong or true-or-false answers, but are marked by better or worse circumstances; (4) Solutions are emergent; there are no experts who can solve this type of problem; (5) Every complex, wicked or messy problem is essentially novel and unique and requires new innovative ways to address it; (6) Every solution is a ‘one shot operation’: and (7) This type of problem has no given alternative solutions (Rittel and Webber and later Conklin, 2006). Because of all these points, reliance on HR analytics is rendered useless. Ackoff agrees.

What we need are the right answers to the right problems, and not wasted effort on getting the right answers to the wrong problems⁴⁹ (because) the righter one does the wrong things, the wronger one becomes.  If one makes a mistake doing the wrong thing and corrects it, then he/she becomes wronger.  However, correcting a mistake while doing the right thing makes things righter. As Ackoff put it, “it is better to do the right things wrong than the wrong things right!  In other words, why solve the wrong problems precisely?   

For example, if an HR leader fails to recognize that a problem’s context is complex or chaotic and mistakenly applies only simple or complicated improvement methods, approaches, frameworks and tools based on known good or best practices, their efforts will likely fail and can worsen the problem. This is because a problem in a complex context is qualitatively different from one that is in a simple or complicated context. As explained by Goldstein, Hazy and Lichtenstein (2010): 

Until recently the differences between complicated and complex were not well understood; as a result, they have often been treated in the same way, as if the same process should be used to “deal with” situations (or concepts) that are complicated or complex. Business schools justified this by treating organizations as if they were machines that could be analyzed, dissected, and broken down into parts. According to that myth, if you fix the parts, then reassemble and lubricate, you’ll get the whole system up and running.  But this is exactly the wrong way to approach a complex problem (p. 371). 

Problems in an unordered or poorly structured context may be complex, which according to the Cynefin framework, the decision-maker must probe, sense and respond. An increasing number of prevailing HR problems are within this kind of context influenced by interconnected challenges within and outside the organizational context. From an HR data synthesis purview, examples are new and emergent drivers of staff engagement, level of and/or determinants of teamwork among employees in a remote work setting, new messes driving increase in employee mental health situations post COVID -19, perceived employee and leadership buy-in levels in issues to do with diversity, equity, inclusion and belonging. These complex HR situations present new and emergent practices, with no known relationship between cause and effect. For example, data synthesis around increase in mental health cases should take into consideration holistic factors around emergent data patterns on mental health cases, in relation to those working remotely or in-person, employee’s work shift, effect of employee assistance programs, State regulatory and compliance requirements on employee health information confidentiality, employee’s willingness to open up and  discuss their situation, employee’s job type, leader’s ability to direct employee to qualified therapists, emergency contact availability to assist, and employee’s living conditions etc.

Another example of a complex context problem was the emergence of COVID-19 pandemic, a once-in-a-century event that impacted most personal, social, educational, and economic/work activities across the globe. In the United States, the federal government instituted measures to contain the spread including lockdowns and shifting from face-to-face to virtual work where feasible. At its peak, the Centers for Disease Control (CDC Statistics, 2023) estimated that more than 104 million (75%) Americans over the age of 16 were infected, approximately 1.2 million died, and approximately 7% of the population continue to be affected by the effects of “long COVID” The turbulent context characterized by threats and fears about safety and health, critical changes in how work could be done, and the loss of income for millions created for HR professionals many complex and sometimes chaotic problems.  Constrained hospital and work facilities, and burned-out medical and other professionals required new ways of working remotely, heavy investment in technology, and other unpredicted and unanticipated means of maintaining organizational and personnel performance. This forced many to navigate and adapt by a sequence of act-probe-sense-respond as they dealt with elevated levels of variability, unpredictability, complexity, and ambiguity. For these kinds of challenges, there were no experts, and no good or best practices, no historical data, because no one had seen this before and no one is able to predict future trends. Hence HR analytics cannot be applied. At the helm of the COVID-19 pandemic, solutions had to emerge from the interaction of many people and ideas, often spontaneously novel and creative, rather than because they were determined in advance or because an expert could use analytic research methods to solve it. 

Next is the chaotic domain. Against chaos, the Cynefin decisional guidance is act, sense, respond: first act to establish or reestablish order; second, sense where stability lies; then lastly, respond to turn the chaos into complex by first changing one’s mindset from analysis to synthesis, and then by applying methodologies and tools that enable this shift. The challenge is that when operating in unstructured contexts, one must be competent to shift between systemic and analytic thinking. Snowden and Boone (2007) noted: 

In the chaotic domain, a leader’s immediate job is not to discover patterns but to staunch the bleeding. There’s simply no time to ask for input. A leader must first act to establish order, then sense where stability is present and from where it is absent, and then respond by working to transform the situation from chaos to complexity, where the identification of emerging patterns can both help prevent future crises and discern new opportunities.

Within the chaos domain, leaders must shift to HR data synthesis. This happens once the situation has been calmed down to a complex level on the way to stability. At the midst of chaos, there is no one to complement the digital data collection process, that is if the digital system is spared from destruction against the chaotic circumstances.

For example, the attacks on September 11 were an example of operating in a context of chaos.  Regarding thinking and deciding in this domain, Stewart and Reinhart (2002) noted, “People were afraid. … Decision-making was paralyzed. … You’ve got to be quick and decisive—make little steps you know will succeed, so you can begin to tell a story that makes sense.”  At the helm of chaos, the relationships between cause and effect are impossible to determine because they shift constantly, and no manageable patterns exist. The best strategy for handling problems in a chaotic context is to act fast hoping to change the situation by stabilizing the events and relations that generate them. Historical data based on HR analytics is rendered useless.

Reference to historical data using descriptive analytics or diagnostic analysis or projecting future patterns using predictive analysis or prescriptive analysis does not work in a chaotic context. The events are presenting themselves for the first time, and have never been experienced before, making it impossible to depend on HR analytics due to lack of history and/or inability to predict the future. This context requires immediate action, and the consequences are hardly predictable. Those engaged in this kind of situation can be seriously compromised if a negative outcome is produced. Fragouli (2016) argues that chaos and crisis should not be thought of occurring only within war-like situations or environmental catastrophes such as floods. Rather, this context for organizations is increasing: 

Chaos is an inescapable part of current reality in business organizations across the world. In the midst of globalization, business leaders are constantly confronted with chaos due to various political, economic, and social issues. Chaos introduces uncertainty, unpredictability, irregularity, and randomness in organizations; and it challenges the conventional leadership theories, models and philosophies … (and too often) … complexities and uncertainties associated with chaos are often ignored when business models, practices, strategies and policies are formulated in most organizations, and as a result it becomes challenging for business leaders to deal with chaos when it arises (p. 73).   

At the backdrop of chaos, HR analytics can only be done after the disaster has stabilized and reduced to complicated or simple.

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