HR Strives to Balance Data-driven and People-driven Approaches to Talent Management

Corporations are being offered a bewildering menu of people analytics tools that promise predictions and recommendations to improve talent strategies. While HR traditionally holds data from employee records, people analytics draws in data generated across the organization — from wearables, AI-enabled everything, candidate screening platforms, the transition to cloud architecture, the digital transformation in production and services, worker surveillance software and, perhaps coming soon, AR/VR and blockchain applications.

People analytics tools potentially offer insights to support work on equitable pay, retention, emerging skills gaps, workforce planning, internal job boards and optimal training investments. However, as Oracle’s State of HR Analytics survey shows, most organizations are struggling to get these tools deployed and maintained; only 29% of respondents said their organizations were able to make positive changes using people analytics.

CompTIA’s survey of HR leaders reinforces how challenging this is. A net 74% of respondents said a priority this year was either modernizing their HR systems or data-driven enhancements to people management. In a question about most desired changes, more investment in HR technology was the top response.

Each new data source available to HR introduces a new set of challenges in collecting, storing and analyzing data and then converting it into something actionable. That produces a surging demand for data literacy. New job roles such as HR technologist, HR data analyst or people analytics data scientist confirm the need for greater expertise and specialization within the HR department.

Last year’s Workforce and Learning Trends report discussed the concept of robotic process automation (RPA) and its path to becoming more widely utilized in organizations. The case for that has become somewhat clearer, particularly in HR where it can streamline employee communication, screening, recruiting, hiring and benefits management. PwC analyzes companies that are succeeding at digital transformation and show they have “invested significantly in process automation, putting tools in the hands of employees in order to accomplish tasks faster, leaving more time to dedicate to value-driven and insights-based work.”

Last year’s report also said AI is becoming a partner in hybrid human-digital teams. But one risk of data-driven talent management is taking the human out of human resources and leaving too much to the algorithm. The New York Times reported last year on the extraordinarily high turnover in Amazon warehouses, with workers sometimes being fired not by a boss but by automated processes with questionable judgment.

An area of HR where emerging technology seems to be yielding results is data-driven internal job marketplaces that reduce attrition and job hopping by identifying realistic career paths. A Bloomberg report makes the case that these platforms, by revealing hidden skills and less-obvious adjacencies, are getting the right people into the right assignments.

We’ve also previously noted the emerging importance of algorithmic bias, and Seth Robinson, Vice President of Industry Research at CompTIA, says the risk it poses to the HR technologist is growing more acute. “Machine learning solves some problems and then introduces new ones,” he explains. “If I’m thinking, ‘I've got a very small talent pool, so let me use technology to get me the best candidates,’ that still needs oversight.”

Even a well-trained model without bias is unlikely to help with what Robinson calls the inevitable corner cases that every organization has. “RPA is picking the low-hanging fruit of routine processes,” he says. “But so much of business is non-routine. You need humans who understand the corner cases. It doesn't seem like we're on the precipice of software taking that over.”

Please visit the CompTIA research hub to access the full Workforce and Learning Trends report.