Perhaps you first became aware of talent analytics when Google’s People Innovation Lab began a 100-year study of its workforce with the goal of “understanding work” as Laszlo Bock, the former SVP of People Operations at Google, wrote in the Harvard Business Review a few years ago.  “By analyzing behaviors, attitudes, personality traits and perception over time, we aim to identify the biggest influencers of a satisfying and productive work experience,” he wrote. Or perhaps you are becoming more familiar with this breed of analytics now, as the labor market becomes tighter and tighter still. Either way, this discipline is gaining traction as more people in the industry become aware of what it can do.

What is Talent Analytics

Talent analytics is an analytics platform that produces insights into the workforce—into the potential hiring pool and into your existing team members. These insights are used to create a better understanding of the strengths of employees and potential employees, their weaknesses and how these can be improved. 

Ultimately, says Dave Weisbeck, Chief Strategy Officer with Visier, “What this really is about is making better decisions—that has always been the true pursuit of anything related to analytics, which people tend to forget.” These decisions—which could range from where to place a certain applicant to how to properly incentivize the workforce—are guided by predictions the software offers about, for example, whether a certain applicant or current employee could successfully transition to a higher position.

Talent Analytics By Any Other Name...

Talent analytics has been around for a bit and goes by a few different names. Here are a few of the more common.

  • Human resource analytics
  • Workforce analytics
  • Human capital analytics 
  • People analytics. 

“It’s an idea by many names,” says Patrick Downes, Assistant Professor of Human Resource Management at Rutgers’ School of Management and Labor Relations.  It’s a toss up as to which term is most popular but the contest probably comes down to talent analytics versus people analytics—with the latter gaining an edge, according to Weisbeck. “We actually did some research on what is the most popular term and it appears that people analytics is winning,” he says.

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Learning Opportunities

What Are The Basic Components of Talent Analytics

Each suite, of course, has its individual stamp in terms of organization and what its features are called. That said, the functionality in a talent analytics application can be expected to be divided roughly into three categories, says Xavier Parkhouse-Parker, co-founder and CEO of PLATO Intelligence

  • Hiring Analytics - Hiring analytics provide insights into prospective hires by analyzing their skills. It also guides the company into making an impartial decision based on the data. One hot topic in the industry right now is bias, Parkhouse-Parker says, and it is argued that hiring analytics can help stem or even eliminate this from the hiring process.
  • Ongoing Feedback Analytics - Ongoing feedback analytics focuses on the existing workforce, determining whether the teams in the company are performing well, whether they have the right skill set and the right talent in the right places.
  • Optimization Analytics - Optimization analytics marries the data and predictions from hiring analytics and ongoing feedback analytics to ensure the company has what it needs to make its internal processes as robust as possible. 

It is working out where companies can be better, where people can put in changes, such as whether a different skill set is needed, whether or not people need coaching,” Parkhouse-Parker says.

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Examples of How It Works

One typical feature in these applications is called automated resume screening, Downes says. It works like this, the candidate uploads his/her resume and the people analytics app uses a machine learning algorithm that reads the words on the resume and scores the candidate on how well she will do in her job. The score is based on all kinds of seemingly disparate data and insights the system has gleaned. Downes gives the example of a talent analytics app that has determined that a person who has held a specific job—for our purposes here let’s say that it was retail experience at Macy’s—was likely to be successful in a similar job at the hiring company. But if this person had—again to use a made-up example—teller experience at a local bank, the system might conclude that this person would be less successful. “The problem, of course is that the company has no idea what is it about having worked at Macy’s that makes people more successful at their company, but they do have a very precise computer-generated prediction about it,” Downes says.

There is, in fact, a wealth of examples of what this application can do. According to a post by Deloitte Insights, these data-driven tools can predict patterns of fraud, show trust networks, show real-time correlations between coaching and engagement, and analyze employee patterns for time management based on email and calendar data. They can also analyze video interviews and help assess candidate honesty and personality through software. Deloitte Insights writes, “Tools can now analyze hourly labor and immediately identify patterns of overtime and other forms of payroll leakage, enabling improvements of millions of dollars through improved practices in workforce management.”