It is now commonplace to keep detailed statistics on one’s customers. Customer analytics help organizations make smarter decisions and lead to a higher return on investment. In theory, talent analytics do the same thing—they help guide upper management’s decision-making processes by arming managers with increased knowledge on everything from their workforce to the micro impacts of specific policy decisions. For this reason, talent analytics are also generally associated with higher levels of productivity and a higher return on investment (e.g., for recruitment and training efforts). However, talent analytics—when taken to their extreme—may also raise ethical concerns that can’t be overlooked.
What are Talent Analytics?
Talent analytics increase managers’ knowledge of their organizations. In short, talent analytics help managers understand their organizations from the outside in. Like an MRI, they provide insight into things that may not be visible on the surface. For example, on the surface, it may be impossible for even the best doctor to detect a tumor. MRIs penetrate the body’s surface, providing doctors with the diagnostic tools needed to detect what is happening on an interior level. This knowledge evidently helps doctors make smarter decisions about how to treat their patients . In many respects, this is precisely what talent analytics do too—they help managers understand the inner workings of their organizations, diagnose problems and inform their responses. Specifically, talent analytics can help answer critical questions, such as:
- Are an organization’s investments in employees (e.g., training programs) paying off? If so, in what ways?
- What is the relationship between training and employee productivity?
- Which employees are most likely to be retained over time?
- Does an organization’s employee health and wellness program positively impact employees and/or the organization? How so?
- Do employees with Ivy League degrees necessarily make better employees?
- Are permanent full-time workers a better investment than temporary and/or part-time workers?
Key Talent Analytics and How to Harness Them
A 2010 article in the Harvard Business Review by Thomas H. Davenport, Jeanne Harris and Jeremy Shapiro outlined six key talent analytics, which are summarized below:
Human-Capital Facts: These facts offer insight into individual performance, contingent labor pools and retention. In short, these are analytics that provide a snapshot of who is working for your organization, for how long and at what level of productivity.
Analytical Human Resources: These facts offer insight into how certain departments operate (e.g., Is the turnover too high in a certain department? Why is turn over higher in one department than another?) Analytical HR facts can also help an organization determine whether or not employee performance or department performance is supporting an organization’s broader goals
Human-capital Investment Analytics: These facts help organizations understand what actions impact their organization and more importantly, they help organizations understand how certain actions impact their organization. Do employees with four-day work weeks during the summer months work less or more throughout the year? Does a casual Friday policy increase, decrease or not impact productivity and retention rates at all? Does free food on site help increase productivity or simply drain an organization’s resources?
Workforce Forecasts: These analytics, often mined from an organization’s archive over time, help respond to “what if” and even “worse case” scenarios. What if we trim staff in payroll? What if we start outsourcing certain key services? What if we automate tasks once carried out by human workers?
The Talent Value Model: These analytics grapple with a wide range of concerns, including retention concerns. For example, what types of employees are most likely to stay? Why do they stay? Should low performing employees be let go or assisted? If so, what assistance might help them become more productive?
The Talent Supply Chain: These analytics enable managers to make real-time decisions about staffing (e.g., how many people should be on the floor at any given time) and to implement the increasingly controversial practice of “on-call scheduling” more effectively.
Benefits of Using Talent Analytics
The broad benefits of using talent analytics can’t be discounted. Among other benefits, talent analytics can be used to offer a “big picture” view of an organization—a picture that can be looked at from multiple angles and through multiple lenses simultaneously. In addition, talent analytics are frequently seen to offer human resource departments a twenty-first century “crystal ball”—a way to see into the future.
Talent analytics can also be used to both support and enhance training efforts. On the front end, talent analytics can help inform an organization’s decisions about training priorities. In addition, by adopting a learning management platform with automated advanced reporting capabilities, an organization can track training progress is real time. In short, talent analytics can help organizations refine their onboarding and training efforts.
Of course, talent analytics can also be harnessed to create algorithms that take the recruitment, evaluation and promotion of employees out of the hands of people and put it into a “big data” context. While it may sound shocking, hiring has historically often taken place using highly subjective factors. Talk to enough HR professionals, especially those who have been working in the sector for decades, and you will discover that hiring has often relied on highly subjective predictors. From superstition (e.g., replacing a much loved retired employee with a new employee who shares the same birth date) to decisions made on “gut reactions” (e.g., a “sense” that a candidate is a good institutional fit) to subjective factors (e.g., hiring a candidate because they claim to be a fan of a specific sports team), there is no shortage of stories about “unscientific” approaches to hiring being privileged, even in the case of high-level hires. Talent analytics can and increasingly will automate at least some of the decisions that have historically been made by managers and as a result make superstitions, astrology, intuition and shared “likes” a thing of the past in hiring.
There is no doubt that talent analytics are important, but they also raise ethical considerations. For example, relying too heavily on analytics can dehumanize employees (e.g., see the recent controversy regarding Amazon’s use of talent analytics). Moreover, if used only selectively, organizations risk focusing on less rather than more information (e.g., relying merely on talent analytics may in fact place other sources of knowledge, including the insights of in house experts, on the sidelines). Another problem is the disproportionate use of talent analytics in hiring, evaluating and firing lower-level rather than higher-level employees. Like or not, most employees, including lower-level employees, will know that only certain employees are subject to talent analytics and in the process, an organization’s talent analytics will become stigmatized.
Other ethical considerations arise when talent analytics are used in relation to supply chain issues. For example, in recent years, several major retailers have been sharply criticized for using talent supply chain data to staff their retail outlets. From a retail standpoint, the data is strategic. If you rely on real-time data to determine customer flows at specific retail outlets, it’s possible to staff stores (often at the last minute) with a flexible, part-time on-call workforce. The approach not only reduces the likelihood that a retailer will have a store full of idle employees on slow days but also increases their ability to deal with sudden customer surges at specific locations. While this may sound ideal, it also raises labor concerns (e.g., the practice leaves retail employees without a reliable schedule and often requires them to work short (two to three hour) shifts and/or to be on call). In short, talent supply chain data, while good for business, is bad for employees. In some states, such as New York State, these “on-call scheduling systems” have even come under investigation by the State’s Labor Bureau, putting retail giants from the Gap to Abercrombie & Fitch on the defensive. Indeed, some retailers, like Abercrombie & Fitch, have even opted to end “on-call scheduling” as a result of the bureau’s investigation.
Like other forms of “big data,” talent analytics are here to stay. Whether we choose to use them to guide hiring and promotion decisions and support well-informed and innovative decision-making or use talent analytics to turn human capital into mere data will depend on whether or not we choose to bring ethics into the mix.
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