How HR Analytics Can Transform the Workplace

The accumulation of worker-related information has evolved into a key tool for making predictions about individuals and larger patterns of employee behavior. Many organizations are ready to adopt advanced analytics into their everyday decisions, and they’re on the brink of taking the first step.

As data storage becomes progressively cheaper, many businesses are beginning to recognize that they’re sitting on a hoard of insightful information that can exponentially drive their business. Meanwhile, companies that have staked out leading positions, technologically speaking, are hungry for as much additional data as they can possibly get.

TechRepublic highlights Google as a leader in this area. Since before 2013, Google has been using HR Analytics to “determine the characteristics of good leaders, design the most productive work environments, predict which employees are most likely to leave the company (and) which candidates have the highest probability of succeeding,” as well as forecast future hiring needs and increase diversity in hiring.

When we explore how workforce analytics can increase productivity, the answers are abundant and impressive. Below we’ll detail some of the benefits.

The international law firm Allens used contextual recruiting, derived from data about candidates’ demographics and educational backgrounds, to yield deeper diversity in candidates. Miriam Stiel, partner and head of the intellectual property group at Allens, commented that “Candidates from disadvantaged backgrounds are 50 percent more likely to be employed at firms that use the Rare content recruitment system.”

Data from industry hiring patterns contains valuable insight on how to improve job postings and attract exactly the kind of candidates that your organization seeks. It facilitates an analytical process that takes into account an assortment of variables, including location, job duties, and industry trends, as well as historical data on cost per hire and time-to-fill figures.

Employee churn is an expensive fact of life for every company, and reducing it is an ongoing challenge. Predictive modeling helps employers define the characteristics of a successful candidate and reduces turnover by matching candidates with jobs that are well-suited to their unique skill sets. The software uses profiles to identify traits characteristic of high performers and eliminates any unconscious biases that the hiring manager may have. Drawing on current employee data for predictors of productivity, recruitment analytics also enable new candidates to be scored against the traits of current high-performing employees.

One key to employee productivity lies in the engagement levels of employees. Gallup reports that only one-third of American employees are engaged in their jobs, while over 50 percent are “just there,” not really interested in what they’re doing. More disturbingly, the Gallup poll finds that 16 percent of workers are actively disengaged, possibly hunting for a new job or even preventing their co-workers from being productive.

These figures show how much room for improvement currently exists, and MIT professor of management Emilio J. Castilla points out that workforce analytics are perfectly suited to contribute to that improvement. He writes, “Leaders need to be extremely proactive in trying to understand what is happening with employee engagement and how the company can take advantage of data collection and analytics to continue to be successful in engaging employees and creating quality jobs.”

As every HR professional knows, the art of managing human capital is not limited to hiring the right people. Forecasting how many people will be needed within a given window of time is equally important, and workforce analytics provide value here as well. Based on historical patterns of demand, the analytical software can run simulations of possible economic scenarios to predict short-term and long-term staffing needs.