This year, supply chain constraints, delays, logistics, and failures have been featured news headlines. Their impact has affected consumers and enterprise alike. Talent management is not immune to these changes—it’s facing a supply chain crisis of its own, and data-driven learning technology is essential to solving it.

Talent Pipelines Remain Under Stress in 2022

Much has been written about the talent pipeline since the 1960s, often focusing on the transactional nature of the employee relationship. For decades, there have been warnings that our talent supply chain is not capable of meeting employer demand, but the breaking point arrived in 2020.

The Great Resignation has become the Great Reshuffling, and in 2022, the seismic changes in talent management have boiled down to daily challenges to recruit, reskill, and upskill talent. Employers need to approach the talent pipeline like a production pipeline, offering clear specifications to colleges, training organizations, and professional boards. But there must also be an investment in expanding learning technology that can manage these complex talent logistics.

Leverage Training Data to Solve Talent Supply Issues

Many training teams are not using learning technology that can connect with larger business systems to create repeatable, accurate data-driven decisions. These decisions are often focused around optimizing course completion, boosting learner engagement, and showing training performance, but training data can be used to solve complex talent logistics. Here’s how it can work:

Map current talent capacity. With a training data model that accesses skill and competency data from other parts of the organization, it is easy to model a snapshot of your current talent capacity. How many employees have required training in a specific skill? How are those employees organized and managed? What other requirements, tasks, and KPIs are they connected to? This type of report can reveal weaknesses and opportunities in your talent.

Know how to invest in upskilling and reskilling. Using the above mapping, it is a simple matter to identify exact areas that could use upskilling or reskilling. But with a robust data model, you can also know how best to invest resources. Need to quickly pivot into a new technology or market? Identify a subset of employees that have adjacent skills and use that to develop a new course. Knowing how to invest intelligently allows your organization to “grow their own” without unneeded waste.

Create data-based talent requirements. Recruitment often struggles to find the best outside talent because requirements for roles and responsibilities are not as precise as they could be. You would expect engineers to provide precise requirements for a new part, with robust training data it is possible to provide the same type of requirements for talent. By knowing where your current and projected skills gaps are, you can hire to fill those gaps.

These ideas aren’t new, but they can be automated, optimized, measured, and reported with modern learning technology. There is no sign that the labor market will change in 2023, so every tool available must be used to find, retain, and train talent.