“The beginning of wisdom is to call things by their proper name.”
—Confucius
There are almost as many names for the workforce-focused analytics function as there are functions that perform workforce analytics. This itself is indicative of a discipline in its formative years. Getting the name right is important; without a common language, practitioners risk confusion about exactly what the function does.
This chapter explains why we recommend using the term workforce analytics to accurately describe the function. The focus of analysis is the workforce, and the activities of the function involve applying an analytical approach.
Numerous articles cover the topic of analytics in the “people space.” These articles use the terms talent, human capital, human resources, people, and workforce interchangeably. In 2015 alone, notable authorities in this field wrote articles in key publications using different terms—for example, people analytics (Josh Bersin in Forbes), HR analytics (Patrick Coolen on LinkedIn), workforce analytics (Rebecca Atamian and Travis Klavohn in Workforce), and talent analytics (Ed Lawler in Forbes). Clearly, no standard name for the function has yet taken hold.
The definition of talent varies widely across organizations. According to the Chartered Institute for Personnel and Development 2016 fact sheet, “Talent consists of those individuals who can make a difference to organisational performance either through their immediate contribution or, in the longer-term, by demonstrating the highest levels of potential.” This definition does not represent the entire spectrum of workers in an organization and, used in the title of an analytics function, does not describe analytics relating to the whole organization. In support of this, many analytics experts believe the word talent is too narrow here, so it is no longer commonly used.
The term human capital seems to be more fashionable among consulting firms and other institutions that see people as financial assets. However, few organizations refer to the analytics function as human capital analytics; perhaps not surprisingly, the ones that do are usually financial institutions.
The descriptor human resources (HR) is commonly used in the analytics business, although different schools of thought have arisen. After all, the term HR analytics is often accurate because it describes the analytics department in the HR function. Furthermore, HR might be broader than people because it covers the management of human resources and the interfaces with all other business functions (finance, marketing, sales, and so on). However, other experts argue that HR is a limiting term because managers and executives often link HR only to employees and the policies and processes for their management. This perspective excludes other categories of the workforce, including temporary, nonemployed contract staff; freelancers; and managed services. In short, the term HR analytics implies that HR focuses only on analytics for the HR function (that is, using analytics to affect and inform the policies, practices, and processes that HR as a function manages—or “HR for HR,” as it is sometimes described).
Since early 2015, the term people analytics has been gaining traction. However, this term can be misleading because it can imply analyzing factors about people beyond the workforce—for example, citizen or consumer behaviors. In addition, the word people does not cover gaps in the workforce (for example, numbers acting as placeholders for people yet to be recruited) and the growing presence of robots in the workplace. Therefore, although the term people analytics is fashionable, it lacks clarity as a functional descriptor in an organization and does not represent the entire workforce.
Considering all the limitations of these terms, it is our view that the word workforce is more descriptive of all workers (not just employees) and includes contract staff, managed services, freelancers, and other people. The term also allows for the future inclusion of machines that will replace current jobs performed by humans, a topic discussed later in this chapter. Therefore, because this book focuses on analytics that relate to the entire group of workers for an organization, we recommend the functional descriptor workforce.
Analytics, reporting and analytics, reporting and insights, metrics and analytics, planning and insights, and planning and analytics are all names that have been used to describe the activities of the function. Most teams use the word analytics in the name of their function. Some, however, explicitly call out reporting and analytics, to make a clear distinction between the two disciplines. Some business professionals (including HR) might think that reporting is analytics, so using both terms enables us to distinguish between them.
In other cases, the function is called planning and analytics, to articulate that planning is separate and distinct from analytics. Functions labeled as planning and analytics tend to have an analytics leader with additional, specific responsibility for elements of strategic workforce planning.
Occasionally, the function is called insights and analytics, to expressly state that analytics is about insights, not data or reporting. However, because insights are part of the entire analytics methodology, as Chapter 4, “Purposeful Analytics,” demonstrates, there seems little need to highlight this one element.
Therefore, we can conclude that analytics is the most accurate word to describe the work of the function. This includes all aspects of analytics within the end-to-end methodology, as Chapter 4 describes.
Finding the right name for the function is key, but that name needs to continue to have relevance in the future. Three key changes will impact workforce analytics in the future:
• Artificial intelligence, robotics, and other technologies will transform work that humans currently perform.
• The gig economy, an environment in which temporary positions are common and organizations contract with independent workers for short-term engagements, will expand.
• The amount of workforce-related data will exponentially increase with the “Internet of Things” as workforce-applicable sensors, wearables, and other devices become ubiquitous.
These changes will create a workplace that is more extensive, more democratized, and more fluid, as the Global Consortium to Reimagine HR, Employment Alternatives, Talent, and the Enterprise (CHREATE) describes (www.CHREATE.net; see Chapter 1, “Why Workforce Analytics?”). As a result, the workforce will continue to evolve, expand, and change; some employees will morph into freelancers, and machines will do jobs that people once handled.
Taking into account all of these points, the most descriptive and accurate name for this function is workforce analytics. This term best describes the broadest set of workers that contribute to organizational success and the fullest responsibilities of the function both now and in the future.
Other experts concur with the use of the term workforce analytics as the best description of the function. Most notably, the SHRM Foundation, the research arm of the Society for Human Resource Management (the professional body for the HR profession in the United States), uses the term workforce analytics in its report “Use of Workforce Analytics for Competitive Advantage,” undertaken in partnership with the Economist Intelligence Unit.
With the name of the function in mind, we can define the work of the function as follows: Workforce analytics is the discovery, interpretation, and communication of meaningful patterns in workforce-related data to inform decision making and improve performance.