Beyond Talent Management: Increase your Revenue and Performance while Lowering your Employee Turnover with Talent Science
Jerome Moureaux, Infor Talent Science @Infor_HCM

Jerome opened by talking about how top performers can leave for many reasons, especially when you get a few of the following combining:

Lack of cultural fit
Lack of promotional opportunities
Lack of recognition

Jerome defined talent science as being the application of predictive talent analytics, and leveraging performance data to improve business outcomes through people. This approach isn’t an optional add-on that you bolt on somewhere; It needs to be applied at all stages from candidate sourcing, selection, on boarding, coaching & development, performance management, succession planning.

The Infor approach is based on 39 capability themes, and starts with a deep dive into developing profiles for existing employees and then looking for correlation between the capabilities and high-performance. The results of this initial work is to identify what is the most predictive profile for high performance. This profile then shows a range of what is optimal for each organisation, recognising that different companies need different people and that more of everything is not necessarily better (e.g. too much of one capability can hinder performance).

I’ve worked with plenty of capability models before and a lot of what Jerome covered resonated with me but I think their approach stands out for a few reasons:

The level of granularity of the themes
The breadth of the deep-dive to identify the predictors of high performance (along with the quality of the analytics)
The strong links at every stage of the people process, including interviews, on-boarding, performance management, talent management, and succession planning. The profile for each employee is a valuable resource to support all of these phases.

(This was live-blogged during a session at the European HR Directors Business Summit 2015 in Barcelona – I’ve tried to capture a faithful summary of the highlights for me but my own bias, views – and the odd typo – might well creep in.)