It would be difficult to overstate the influence of Big Data on a wide range of business and societal outcomes. In particular, the business community's interest in Big Data is substantial, as the amount of available data is growing exponentially, cloud-enabled computing power has increased rapidly, storage and connectivity costs have dropped significantly, and an increasing number of sophisticated machine-learning techniques are available to help to translate Big Data's potential into value-adding knowledge (McKinsey, 2017). As a consequence, firms are spending billions of dollars on data and infrastructure, and hundreds of blogs and thousands of LinkedIn posts have been written on this topic.
Among the many definitions of Big Data (for a detailed review see Gandomi and Haider, 2015), the definition found in the Gartner IT Glossary is the most prevalent in the literature: ‘Big Data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.’ In addition to the three ‘Vs’ included in this definition (volume, velocity, and variety), other dimensions of Big Data have been highlighted in the literature. For example, IBM introduced veracity, which refers to the unreliability, impreciseness, and uncertainty inherent in some sources of data.1 SAS introduced variability and complexity as two additional dimensions of Big Data.2 Variability refers to data flow rates that are inconsistent, and have periodic peaks and troughs. Complexity refers to the fact that Big Data is generated from a myriad of sources. This implies another critical challenge: the need to connect, clean, and merge data from different sources. Finally, Oracle introduced value, especially low value, as a defining attribute of Big Data.3 In its original form, Big Data has little value relative to its volume. This implies that Big Data per se is not a strategic resource. Instead, the value lies in analyses of that data.
With the right analytics, Big Data can deliver rich insights, as it draws on multiple sources and transactions to uncover hidden patterns and relationships. Big Data analytics have been utilized in numerous applications, including real-time fraud detection, complex competitive analyses, call-center optimization, consumer-sentiment analyses, intelligent traffic management, and the management of smart-power grids.
Strategic human resource management (HRM) and strategic human capital scholars have also expressed significant interest in the potential inherent in Big Data and its analysis. Recent special issues of the Journal of Organizational Effectiveness: People and Performance (Minbaeva, 2017) and Human Resource Management (Huselid, 2018), books in the popular press (Bock, 2015; Guenole, Ferrar, and Feinzig, 2017), and workshops held by professional organizations, such as the Academy of Management (AOM) and the Society for Industrial and Organizational Psychology (SIOP), all point to the growing importance of Big Data and analytics, especially in the domain of HRM. Nevertheless, Marler and Boudreau's (2017) review of the literature concludes that while the promise may be real, much work must be done before Big Data can fulfill its promise for the science and practice of HRM.
We believe that the advent of Big Data in HRM represents both a major opportunity and a significant challenge for our field. For example, most organizations routinely spend 50% to 70% of their revenue on their workforces and related expenses (e.g., wages, benefits, investments in training and development). However, the quality of analytics processes and infrastructure in most organizations is poor (Huselid, 2015; 2018). This form of ‘information failure’ can be very costly. Talent (especially top talent) is more mobile than ever, and disruptions and global labor arbitrage have left firms with no choice but to enhance their understanding of the quality of their workforce. Markets are changing faster than most firms can adapt, making workforce analytics one of many potential tools that might help firms survive and, perhaps, prosper in the current economic environment.
To address the challenges and opportunities of Big Data for HRM and to move the field forward, we believe that both academics and practitioners should address several key questions:
In this chapter, we address these questions and provide a brief overview of Big Data in the context of HRM.
We believe that the advent of Big Data provides an important opportunity, but one that is fraught with peril if managed incorrectly. Ironically, more data is not necessarily always a good thing. One could argue at length about whether the Big Data construct is best described by such subfactors such as volume, velocity, variety, veracity, variability, complexity, and value. However, with regard to HRM, we propose that the discourse around Big Data should be concerned with the concept and definition of smart data.
In their recent editorial in the Academy of Management Journal, the editors point out that ‘big’ is no longer the defining parameter for data in management research. Instead, the defining parameter is how ‘smart’ the data is – that is, the insights that the data can reasonably provide.
‘For us,’ they add, ‘the defining parameter of Big Data is the fine-grained nature of the data itself, thereby shifting the focus away from the number of participants to the granular information about the individual. (George, Haas, and Pentland, 2014: 321)
What is smart data for HRM? ‘I know that we have a lot of HR data, but I do not know what kind of data we have': this is the most common response from managers when asked about their existing HR data. What data do we have? Where do we store our data? How was the data collected? What rules have been applied? How can two (or more) datasets be merged into one? What are the advantages and disadvantages of each dataset? Although these are basic questions, most firms do not have the answers.
Such poor organization of firm data can be very costly. When formal, centralized coordination of data collection is lacking, we often see such problems as data duplication and incorrect entries. Moreover, such a situation makes it impossible to combine different datasets; creates unexplained breaks in time-series/longitudinal data; and leads to data inconsistencies due to the proliferation of various metrics, coding system, or time frames. Accordingly, analyses based on such data are rarely comparable or combinable. Answers to complex business problems that rely on analyses of different variables observed over several periods of time and at different organizational levels (e.g., individuals, teams, departments, business units) are difficult to derive. Moreover, firms usually do not collect data documenting changes in the organization (e.g., business-unit reorganizations), even though organizational change can modify the relationships under study. The failure to model such processes biases the analytics-based decision-making process.
Furthermore, many firms do not have full ownership of their data. That is, most firms do not have access to the individual-level data gathered through surveys carried out by external vendors, often due to contractual arrangements. Accordingly, they cannot connect their existing HR data to the collected survey data at the individual level. A major contributing factor to this situation is the fact that firms make unclear agreements with their external vendors regarding whether they will have access to the raw data (i.e., original responses at the individual level). In this regard, external vendors often refer to the need to ensure respondent anonymity. However, in their argumentation, external providers often use the terms ‘anonymity’ and ‘confidentiality’ interchangeably, even though they have very different meanings. When data is collected and held ‘anonymously,’ it does not include identifying information that can link survey responses to certain respondents. In fact, not even the researcher can identify a specific participant. In contrast, when data is collected and held ‘confidentially,’ the researcher can identify the participants but that information is kept in a secure environment.
The problem with anonymous survey data is that matching it with other available data can only take place at the group level. As such, explanatory and causal models accounting for individual variance cannot be developed. Why is this problematic? When we average individual responses at the group level, we lose a great deal of explanatory power. This means that we are unlikely to be able to derive conclusions about the true individual-level antecedents and consequences of employee engagement. In research terms, this is called an ecological fallacy. It occurs when we make conclusions about individuals based only on analyses of group-level data. Even if we are working with a collective concept that is, by definition, supra-individual (such as Barrick, Thurgood, Smith, and Courtright's (2015) discussion of collective organizational engagement), individual-level data is needed to ensure discriminant validity between aggregated individual-level findings and collective organizational engagement.
How can this issue be addressed? A firm can, for example, promise its employees confidentiality rather than anonymity. Patrick Coolen, HR Analytics Manager at ABN-AMRO Bank, explains:
We partnered with an external partner … in some cases, to protect the anonymity, we are not allowed to handle data at an individual level within our organization. This simply means our external partner can perform richer models and therefore can create better insights than we can internally. (Ignostix, 2016)
Employees may trust third parties to avoid inappropriately sharing information with their employer. The aspects of third-party relationships that support trust in confidentiality include a reputation for independence, explicit rules for research ethics, academic integrity, and traditions.
Another solution is to encrypt the individual-level data. Encryption entails the conversion of data into a form that cannot be easily understood by unauthorized users. In practice, one file is created in which individual identifiers are connected with a code. In all other files, the code is used instead of individual identifiers. One person in the company (e.g., the data protection officer) may have access to this file or it can be held by an external party (e.g., a survey provider or academic partner).
Therefore, the key issue is not having more data, but doing more with the data you have. Furthermore, to move from data to actionable information, we must understand behavioral science theory and ask the right questions about how the workforce contributes to success. In this regard, it is useful to remember that Big Data requires bigger theories. The typical statistical approach of data mining, searching for significant p-values, and moving towards increasingly sophisticated econometrics will probably result in a decent, perhaps slightly over-fitted statistical model – the immense volume of data generally makes everything appear significant. However, it is unlikely to result in a model that would be useful for practitioners and also acceptable to top journals. In the Academy of Management Journal editorial mentioned above, the editors stress: ‘Given the unstructured nature of most Big Data, causality is not built into their design and the patterns observed are often open to a wide range of possible causal explanations’ (George et al., 2014: 323). The idea is to move away from reporting on what is happening and toward using rigorous analysis based on solid conceptual models to help the firms understand and address current challenges and to plan for the future (Davenport, Harris, and Morison, 2010).
We believe that the advent of Big Data can have substantial positive implications for the field of HRM provided that leaders and analysts stay focused on data for decision-making and strategy execution through the workforce. In our view, a focus on the workforce (rather than the HR function) is central to the effective use of Big Data in organizations. This reflects the shift in the focus of academics and professionals from the HR function (a relatively low value-added activity) to the workforce's output (an activity with much greater value-added potential). Huselid (2018) defines workforce analytics as follows:
Workforce Analytics refers to the processes involved with understanding, quantifying, managing, and improving the role of talent in the execution of strategy and the creation of value. It includes not only a focus on metrics (e.g., what do we need to measure about our workforce?), but also analytics (e.g., how do we manage and improve the metrics we deem to be critical for business success?).
Workforce analytics is both a very new and a very old discipline (Becker, Huselid, and Ulrich, 2001; Huselid and Becker, 2005; Huselid, Beatty, and Becker, 2005; Huselid, Becker, and Beatty, 2009; Huselid, 2018). Managers have been making decisions about who to hire, how to appraise performance, and who to promote for many years. What is new about Big Data is that it presents an opportunity to substantially improve the quality of these decisions. In short, the main potential of Big Data lies not in the data per se but rather in the insights and intelligence that it can generate.
From a historical perspective, the field of workforce analytics is rooted in the conventional disciplines of economics, statistics, social psychology, law, and, of course, HRM. So, how can we understand the impact of Big Data on the field of HRM? Will the impact be one of evolution or of revolution? Like any other business function, HRM is exposed to various disruptive forces that push business functions to transform themselves.
Consider a case involving the introduction of strategic workforce planning (SWP) and the shift of workforce-planning decisions to line managers. SWP is a technological tool that systematically forecasts risks; finds the right balance among quantity, quality, and location of critical talent; and pinpoints the internal supply of and demand for critical skills and roles in multiple business scenarios. It can be developed in-house (e.g., Novo Nordisk's analytics team developed a tool for the whole organization using only Excel) or in-sourced from external providers. When introduced properly, SWP is a unique case showing how HR technological advancements and easy access to actionable analytics push people-related decisions out of the hands of HR professionals and into the hands of line managers. Minbaeva (2017) noted that:
With the introduction of strategic workforce planning and actionable analytics, do line managers need HR business partners to discuss the changes in their workforces driven by market growth and talent supply? Would line managers prefer to obtain their figures by playing with scenario planning in the strategic workforce planning application? Given the expansion of digitalization and the rise of e-HR, what should be outsourced to robots or automated, and what should be kept for HR? How will the rise of analytics shape the employable HR profile over the next three to five years?
In summary, we believe that changes in the HRM mindset will be necessary to capitalize on the opportunity afforded by Big Data. We argue that these tremendous advancements in information technology, the disruption of the main business processes, and stakeholder expectations for continual economic gains not only pose significant challenges to HR but also offer tremendous opportunities for reinventing HR to allow for organizational value creation. ‘Technology and analytics are needed to translate data, because deciding on human capital value is no different from deciding on capital investments in the business with an expected return on investment’ (EY, 2016a: 2). To rise to this occasion and meet these expectations, ‘many HR legacy mind-sets that may have been true in the past need to evolve to modern realities’ (Ulrich, Schiemann, and Sartain, 2015: 2).
Although firms are improving their abilities to act on the results of their analytics, too few collect data focused on the consequences of their analytics-based decisions and actions. What actions have been taken and where? How have they been operationalized? What changes are evident in the variables? The formal analysis of follow-up data reveals the effectiveness of the decisions and actions, helps identify how actions can be modified or changed to better achieve the expected output, and highlights those actions that are actually harmful and should therefore be stopped.
In HRM, the situation is very different – an atheoretical (or unmonitored) search for results with ‘statistical significance’ can be ill-informed or even illegal. For example, one workforce-analytics specialist recalled that certain analyses showed that single, white males in a focal firm had the highest performance-evaluation ratings and the highest salaries, and that they also received the highest raises (in both percentage and absolute monetary terms). This analyst suggested that the organization should therefore consider devoting more resources to the members of this group because of their ‘obvious’ higher performance and potential. It had not occurred to the analyst that correlation does not necessarily equal causation, and that there were a range of alternative explanations for these findings, beginning with the firm's own biases in its recruiting, selection, development, and promotion processes.
The great irony of the advent of Big Data is that the increasing amount of data has the potential to distract rather than inform. The danger is that that we can easily become sidetracked and overwhelmed by the availability of data, and consequently pursue research avenues that are either not focused on strategy execution or not supported by previous research. As with any form of scientific inquiry, some questions are more important than others, and not all questions warrant significant investments of time and resources to generate a high-quality answer. It is important to understand that all data is only valuable to the extent to which it can create new insights and knowledge relevant for business.
Sanders’ (2016) review of the implications of Big Data for supply chain management includes some important points of particular relevance for HRM. Based on interview and survey data covering executives in more than 300 firms, Sanders concludes that Big Data has created three new areas of opportunity for leaders:
For HRM, the most value added by Big Data and analytics relates to the key unanswered question in HRM: Does HRM pay off? A few years ago, a cover story in the Harvard Business Review claimed ‘It's time to blow up HR and build something new.’ As Capelli (2015: 56) explains, ‘HR managers focus too much on “administrivia” and lack vision and strategic insight.’ Another article in the same issue highlights the fact that HR tends to fall in love with the problem rather than the solution. As such, it focuses too little on the actual value of HR initiatives and their contributions to the fulfilment of organizational goals (Boudreau and Rice, 2015). Big Data and analytics offer a possibility to demonstrate HR's actual value and contributions, thereby making HR a more credible partner for business. As Green (2017: 137) argues, ‘successful people analytics teams focus on projects that actually matter for business.’ To be viewed as a valuable business partner, HR must speak a language that stakeholders understand – the language of value creation. As Ed James, Wawa Inc.'s Senior Director of HR, says, ‘We've found that the more data we [HR] produce and send to our business partners, the more questions we get and the more they want. They become very engaged with what we are doing, very engaged with the solutions.'4
The advent of Big Data and analytics should also help HRM move away from treating all employees equally toward starting to treat them equitably (Becker, Huselid, and Beatty, 2009). For example, analytics can provide input for core talent-management decisions in terms of: (a) identifying pivotal or strategic positions within the organization that have the potential to affect organizational performance, (b) identifying a talent pool (both external and internal) to fill those positions, and (c) monitoring talent performance and actively managing talent retention (Minbaeva and Vardi, 2018). Similarly, well-designed and executed analytics projects can help HR to create ‘a clear sense of the HR management practices (selection, development, performance management, and so on) that you [the organization] wish to improve vs. those you would like to do differently’ (Becker et al., 2009: 129). This will ultimately lead organizations to build differentiated HR architectures and enable them to effectively execute their strategies.
Where will the potential impact of workforce analytics be the greatest? What should be measured and how? While the answers to these questions will almost certainly differ by firm, our key point is that business logics drive measurement. In our view, this means that the metrics and analytics that a firm develops should focus on executing the firm's strategy. More specifically, we believe that workforce analytics will have the greatest impact when it is focused on strategic work embedded in strategic jobs (Becker, Huselid, and Beatty, 2009; Huselid, 2018). These jobs may appear at any point in the firm's value chain and they exhibit two key attributes. First, they are almost always located within one of the firm's most essential strategic capabilities (e.g., supply chain analyst in a logistics firm). Second, there is substantial variability in the performance of the individuals holding those roles. This unique combination of importance and opportunity makes strategic jobs a priority for both the development of analytics and improvements by managers.
The effective implementation of analytics programs requires a wide range of skills and abilities, some of which most likely already reside in most well-developed HR functions. Some may need to be ‘borrowed’ from other functional areas (e.g., marketing, accounting, finance, supply chain), while still others will need to be developed internally or brought in from the outside.
Our point is that world-class analytics do not just occur on their own – they are created by competent, capable leaders who know and understand workforce analytics. Becker et al. (2001) argue that effective workforce-analytics design and implementation require HR leaders with the following skills (in addition to general HR manager competencies):
Taking this idea further, Kryscynski, Reeves, Stice-Lusvardi, Ulrich, and Russell (2018) tested a sample of 1,117 HR professionals from 449 organizations. They found that HR professionals with better analytical competencies outperformed their peers.
Clearly, analytical competencies matter and the field of analytics is growing rapidly (Davenport and Patil, 2012). While this is a positive development for HRM, it is also important to take great care when forming an analytics team. Managers trained in analytics may not have much experience with the science and practice of HR, which points to the need for a wide range of skills on the analytics team and a focus on the organizational level of analysis when considering investments in analytics capabilities.
In addition to changes in the mindset, new organizational-level capabilities are also required. These capabilities must be built on a foundation of individual competencies. Minbaeva (2018: 701) defines human capital analytics (HCA) as an ‘organizational capability that is rooted in three micro-level categories (individuals, processes, and structure) and comprises three dimensions (data quality, analytical competencies, and strategic ability to act).’ She argues that at the individual, process, and structural levels, the development of HCA as an organizational capability requires different components, as well as interactions within and across those components:
A related issue is the discussion regarding where analytics should be located within the organization. Does it belong with HR, line managers, or the business-intelligence unit? Andersen (2017) weighs the pros and cons of moving analytics outside the HR function. Van den Heuvel and Bondarouk (2017) argue that moving analytics to the HR department or to a general business-intelligence department is the most desirable solution. In general, the analytics function should be based in an area where it fulfills boundary-spanning roles and acts as a bridge among HRBPs, line managers, and the executive team. The interdependency between analysts and HRBPs is crucial, as articulating a business problem in analytical terms requires a joint effort between HRBPs and analysts. However, direct links with business and line managers are also needed, as the communication of the problem and the interpretation of the results occur directly between the business leaders and the analysts. Finally, the support of the executive team is crucial. Green (2017: 172) warns that ‘without CHRO and senior executive involvement your people analytics adventure is likely to be doomed from the start.’ Similarly, Boudreau and Cascio (2017: 122) point out that ‘a fundamental requirement is that HCA address key strategic issues that affect the ability of senior leaders to achieve their operational and strategic objectives.’ In her analysis of Shell's analytics journey, Minbaeva (2017: 114) concludes:
one of the decisive factors for the success of Shell's analytics journey is the close cooperation between Jorrit van der Togt, the Executive Vice President of HR Strategy and Learning, and Thomas Rasmussen, the Vice President of HR Data and Analytics, as well as the strong support from the senior business leaders.
Perhaps the most important advice we can provide is that workforce measures and analytics should provide answers to questions, especially questions about the quality and progress of the workforce in relation to the firm's strategy. Therefore, one of the most important things an analytics team can do is to ask the right questions about how the workforce contributes to the firm's success.
This can be quite a challenge, especially in the context of a business (or leadership team) that is pressuring the HR function to ‘do something’ about analytics and to quickly provide results. We have worked with a number of analytics teams that have actually impeded their own long-term progress by moving too quickly to the data-analysis phase. The typical rationale for doing so is that there will be time to go back and collect the ‘right’ data ‘later,’ and that it is important to ‘do something’ now. The answers generated by this approach are often either uncompelling or simply incorrect. Consequently, the analytics team loses credibility and line managers lose interest in the concept.
We can provide two brief examples of how paying attention to the ‘data we have, not the data we need’ can distract an analytics team from focusing on the ultimate goal of helping leaders make better, evidenced-based decisions about the workforce. The first example relates to benchmarking common HR processes, such as time to fill an open position or cost per hire. The measurement of such HR activities is very appealing to leaders because it seems straightforward and relevant. Who can argue against trying to fill open positions quickly and efficiently? Unfortunately, a decrease in the time used to fill an open position is frequently associated with lower candidate quality and, ultimately, higher costs and poorer organizational performance (Becker and Huselid, 2003). How can the firm address this problem? Instead of measuring the time needed to fill a vacant position, some firms measure time to competence or time to first promotion. Others use performance at the one-, two-, and five-year work anniversaries as a measure of recruiting competence. These time-lagged measures are more complex than simple time-to-fill measures, but they are a much better fit for the recruiting construct.
The second example has to do with an overreliance on enterprise resource planning and data warehouses as data sources for workforce analytics. Part of the Big Data implementation process in many firms is the development and installation of system-wide data warehouses that are not only intended to integrate the functional areas within HR (e.g., performance management and compensation systems), but also to link those systems with data in other functional areas (e.g., marketing, sales, supply chain, and finance). This sounds like an ideal situation for the workforce analyst. However, with this type of system, the devil is often in the detail. Given the scope, magnitude, and costs associated with these systems, there is enormous pressure to standardize data feeds and related elements for the workforce. The customization of the software to meet the needs of the workforce analysts is often extremely expensive, especially after it has been installed. To avoid this type of problem, we believe it is important for the workforce-analytics team to be involved in the system's design and implementation from the outset.
The point of these examples is that it is crucial for workforce analysts to focus on collecting relevant data rather than on analyzing available data. First, analysts must determine what to measure and then collect reliable and valid data. As Becker et al. (2009) suggest, the process needs to start with the development of a clear statement of the strategic capabilities (e.g., bundles of information, technology, and people) that are needed to execute the firm's strategy. As we mentioned above, the greatest opportunity to affect the firm's performance is likely to be located in (some very specific) strategic positions.
After these steps have been taken, someone on the team should review the literature to see what is already known about a topic. Relevant questions in this regard include: How do we measure the performance of our project managers? What do we know about the predictors of their performance? How difficult is it to change or influence these predictors? In short, it is important to read the research and to build a theory or model that shows causation in your organization. In the long run this will save a tremendous amount of time and energy. Moreover, these analyses should be focused on the entire work system, not just on individual HR policies or practices (Levenson, 2018).
Most of the focus in the domain of workforce analytics is on quantitative data, including performance-appraisal data, salary data (e.g., base salaries, bonuses, and other incentives), and employee movements (e.g., resignations and promotions). This data is relatively easy to acquire. However, much of the interesting and important data is qualitative in nature, and firms are generally much less skilled in dealing with this type of data (Gandomi and Haider, 2015).
Finally, managers should develop and implement audit functions for workforce analytics. Audit procedures are common in many organizational functions, and we believe that they are particularly important in the workforce data domain because the data-collection processes are not only new but also widely distributed throughout the firm, which increases the likelihood of errors.
In our experience, the most quantitative part of the process (estimating statistical relationships among variables) is actually the easiest and least controversial part. There is an extremely well-developed stream of literature in psychometrics (e.g., ways of measuring aspects of employee attitude, such as satisfaction, job involvement, or engagement) and statistics and econometrics (e.g., ways of assessing relationships among variables).
One key point to keep in mind is that workforce outcomes (e.g., performance, turnover, and satisfaction) are not the result of a single driving factor. Rather, they are determined by a variety of factors. Therefore, the ways in which we think about and model those outcomes need to be multivariate as well (Huselid, 2018). Managers should be wary of simple correlations in organizations. For example, a focus on the relationship between engagement and performance is likely to overstate the importance of engagement in the model. Instead, managers should utilize multivariate models, such as regression or network analyses (Robinson, 2018), and predictors that have been identified in the extensive body of HRM research.
Another defining characteristic of measuring and managing the impact of the workforce on the firm's success is that the effects of the workforce are nested or multilevel in nature. For example, employees work together in teams, which develop (or support the development of) a product or service. This then influences the production, merchandising, and distribution processes, which in turn affect customer sentiment and purchase (and repurchase) behavior. That behavior turns into sales and cash flow, and ultimately into profit and shareholder value. The rich, multilevel nature of this research can also be modeled using existing research techniques (Gibson, 2017). The reliance on a single-level view yields an ‘incomplete understanding of behaviors occurring at [any] level’ (Hitt et al., 2007: 1385). We believe that firms that can understand and work to improve the direct and indirect ways that employees affect firm value can enjoy a source of competitive advantage that is difficult to replicate.
In the absence of managerial influence, workforce analytics represents a substantial missed opportunity. Therefore, it is important to develop an implementation plan that ensures that workforce data and analytics are used to help execute strategy and to improve workforce quality. Managers need help with focusing and prioritizing their workforce decisions and investments, and they require information that will enable them to make better decisions about the firm's most expensive (and valuable) resource.
In this context, Big Data and the analytics team can help managers by collecting and presenting data on the extent of workforce success. Data-visualization software, and HR or workforce scorecards, allow managers to understand complex, often nuanced data. One approach at the HR function level is the HR scorecard (Becker et al., 2001), while metrics for the broader workforce can be presented in a workforce scorecard (Becker et al., 2009). Regardless of the approach, HR leaders and decision-makers need to understand the specific process through which the workforce affects the firm's success, how the firm is doing in relation to those elements, and areas in which improvements can be made.
Our final point is that the scholarly and practitioner communities must work closely together as the field evolves. In our work with the Human Capital Analytics Group at the Copenhagen Business School (Minbaeva) and the Center for Workforce Analytics at Northeastern University (Huselid), we have observed numerous cases in which applied analytics teams made substantial mistakes because they were not aware of prior research on a topic or the appropriate analytical tools. Similarly, we have worked with analytics teams that were exceptionally advanced and were undertaking much more sophisticated analyses than have typically appeared in the literature – so much so that they were hesitant to publicize their work because they felt it could be a source of competitive advantage. Clearly, both the academic and practitioner communities have much to learn from each other (Simon and Ferrerio, 2018).
We also believe that the Big Data trend represents a significant opportunity for HRM scholars to conduct new, innovative research that was simply impossible to undertake even a short time ago. Workforce analytics exists within the broader context of business analytics. HR function analytics are likely to be a subset of workforce analytics, but they do not have to be. For example, prior research on the impact of HRM systems on firm performance can help firms position their work in the context of the broader business and its strategy (Combs, Liu, Hall, and Ketchen, 2006; Huselid, 1995).
For scholars, we believe that it is important to reach out to practitioners who are handling this work in organizations. Scholars can help firms understand what we know about the relationships among HR practices, talent, customer outcomes, and firm-level outcomes, and then translate those findings into a structure easily accessible to practitioners who are developing and implementing workforce analytics. As such, we believe that the field of workforce analytics will face many of the same challenges and obstacles encountered in evidence-based management, especially in the process of translating the extant research into testable internal research designs (Rynes and Giluk, 2007). Excellent examples of this process can be found in case studies of Google (Bock, 2015), Jack in the Box (Schiemann, Seibert, and Blankenship, 2018), and Zara (Simon and Ferrerio, 2018).
We began this chapter with a focus on four broad questions:
Our conclusion is that Big Data in the domain of HRM has the potential to substantially contribute to effective workforce management and, ultimately, to firm success. However, much of this potential remains unrealized. Our analyses show that that the shift toward workforce analytics and the broader construct of evidenced-based management represent a real and enduring transition. Is this transition real or a fad (Rasmussen and Ulrich, 2015)? Only time can tell. Nevertheless, we believe that workforce analytics represents a significant shift in HR management, as it meets a significant managerial need and, at its core, is based on fundamental social science research methods that are well understood and well proven. For managers, we highlight the need to develop a causal understanding of the role of the workforce in the firm's success and to then act on that information. In this regard, there is still much work to be done.
The challenge for both scholars and practitioners is to carefully manage the signal-to-noise ratio, and to avoid becoming distracted by data and questions that are not relevant to the firm's overall success. The HR team cannot handle the analytics challenge alone. The most effective organizations build specific organizational capabilities in analytics by creating interdisciplinary teams. Broad, integrated business problems require equally broad and competent analytics teams to address them.
Analytics can drive the makeover that HR needs (Cappelli, 2015). HR tends to fall in love with the problem rather than the solution, and to focus too little on the actual value of HR initiatives and their contribution to the fulfilment of organizational goals (Boudreau and Rice, 2015). ‘A critical analysis of many HR functions today would reveal between 60 per cent and 80 per cent of activity and associated cost remains focused on what are primarily transactional or compliance-based activities, suggesting the function may not be that different to what it was 30-plus years ago’ (EY, 2016a: 1). We believe that carefully designed workforce analytics can go a considerable distance toward closing this gap.
4 www.nugress.com/resources/images/HR%20Analytics%20%20Gaining%20Insights%20for%20the%20Upturn%20[1].pdf
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