Will Talent Analytics Become Customized?

Talent analytics — based on advanced statistical analysis of employee biographics, demographics, psychometrics, sociometrics, work history, performance trends, career goals, documented strengths and learning objectives — are being promoted by Ian O’Keefe of Sears Holding Co. He believes that algorithms will soon lead to highly customized and individualized:

>>  Performance improvement recommendations based on real-time analysis of unstructured crowd-sourced feedback via natural language processing and cognitive computing (think Yelp)

>>  Career development plans that automatically evolve with links to open-source learning content, MOOCs, corporate social media follower suggestions, and internal job alerts (think Netflix)

>>  Skill-building “time investment” opportunities that are gamified and pushed to your mobile device via alerts for short-term projects sponsored by other departments (think Kickstarter)

>>  Mentor-mentee assignments created from pairing algorithms, deployed through system-generated calendar invites, and improved with machine learning feedback loops (think Match.com)

>>  Leadership pipelines and critical positions that are slated with evolving successor lists based on attrition probabilities of incumbents and predicted gaps in team performance (think Moneyball)

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