Forget the Winter Olympics. Nowadays, it seems the fiercest competition is playing out in the talent management sphere.
Deloitte’s 2017 Global Human Capital Trends report identified talent acquisition as one of the most important challenges currently faced by companies – in fact, 81% of the business and HR leaders surveyed reported it as “important” or “very important.” In their report, Deloitte specifies the need for HR and business leaders to “rewrite the rules” in order to keep pace with the increasing speed and complexity that organizations are facing.
Moreover, as the proverbial War for Talent rages on, there has been increased pressure to cultivate and utilize advanced talent metrics to not only acquire top talent, but also to develop, train, and retain them over time. This very topic was raised at an HR roundtable hosted at our office in 2017. We invited a group of HR leaders together to discuss, broadly, the topic of leadership potential and development; a key point of discussion that emerged centered around the appropriateness (or lack thereof) of our current talent metrics. For example, one leader expressed frustration about her organization’s heavy focus on talent retention – specifically, once individuals were brought on board, many HR functions were designed around retaining them. Leaders in the C-suite often measured the organization’s health based on percentage of talent retained, and sometimes neglected other important factors such as: employee engagement, job satisfaction, identification of successors for critical roles, or number of internal promotions.
Our group agreed that this was a flawed, yet quite common, approach to talent management.
This got us thinking: what are the talent metrics of the future?
A perusal of the popular press reveals a consistent buzz about talent metrics your company should be using and insights on the next “hottest” indicators of performance. It seems only a matter of time before advancements in computer science gain a foothold in HR and start making huge strides towards improving talent and leadership metrics using artificial intelligence. For example, machine learning is being used to analyze human capital to predict outcomes such as job performance and employee turnover, which can help drive workforce planning positions. While we do not question the need for more advanced analytics and certainly support the mission to develop them, this leads to another important question: What will become of the older, tried-and-true metrics? Should we re-invent the wheel, or build upon the foundation we have?
In a post a few weeks ago, Mike Tobin drew on a baseball comparison to drive home the point that just as it is necessary to practice the good, basic fundamentals of the game, it is important to consistently practice the fundamentals of talent management within an organization. To continue with that analogy…
Since baseball’s inception, simple statistics (e.g., ERA, RBI) have been used to describe player performance. However, in recent decades, more advanced sabermetrics have been employed to enhance the understanding of performance and inform baseball decisions. Before sabermetrics gained widespread popularity, teams which used these statistics ultimately gained a competitive advantage over those that did not (e.g., the 2002 Oakland Athletics Moneyball team). Essentially, they were ahead of the times – blazing a new trail. However, because the game has now reached a point where these advanced statistics are nearly ubiquitous, teams are yet again looking for new technology to obtain that elusive competitive advantage. For example, some teams are investing in technology that analyzes players’ biomechanical information, which has implications for injury prevention and training. With the advent of these new technologies, what will become of the older, simpler statistics? Perhaps more importantly, how can they be integrated so as not to start from scratch?
In theory, as technology progresses and becomes more integrated with talent acquisition and management, there is a lot to be gained—perhaps we’ll have better quality hires, less turnover, better employee engagement, etc. It makes sense to apply predictive analytics and other advanced techniques to the data-rich HR space. But as the rules are rewritten and new, more advanced methodologies become increasingly adopted, we’ll leave you with some food for thought:
What might be lost as we incorporate these advanced models into the field?
Will Human Resources lose the “human” aspect?
While innovation is critical and necessary, what are the most important existing metrics to hold onto that might stand the test of time through further technological advances?
Share your thoughts with us in the comments below!
This post was co-authored by Stefanie Mockler.