Analytical software has long been used to evaluate the success of candidates for a variety of metrics. Now this insight is being used more strategically to predict the outcome of corporate learning programs, says Charles Cagle, senior VP of human capital management development, strategy and operations at Infor.
Cagle says that one of the most exciting aspects of using predictive analysis for tracking employee learning is that, “current technology can extract insight on the actual development topics where the candidate falls short of the optimum target.” This is a strategic way to use data to see how closely aligned learning is with actual professional development of each employee – a factor that’s used to improve corporate learning initiatives.
Predictive analysis is best used to evaluate the skills and any gaps that new hires have, and then catering learning that’s directly related to these gaps. Candidates should be assessed for their ability to be self-reliant in learning new things. They should be given the opportunity to learn new skills early in their career experience, as this can improve their performance and ability to ramp up quickly to their new roles.
When organizations rely on real data instead of conjecture, they can design much more effective corporate learning programs. The analytical approach makes a lot better sense than trying to struggle to understand why some employees succeed in learning, while others fail.
It's also important to note that candidates who are well-screened for their ability and willingness to learn during the recruitment phase can predict their future success on the job. The two-pronged approach of having candidates self-evaluate for learning and then driving this process by choosing their own learning style is smart – it empowers employees to learn efficiently so they can become more productive early on.