How a large group with dozens of brands and hundreds of thousands of employees across several continents made its talent pools legible and actionable.
In a group of this size, identifying internal or external talent is a considerable challenge: profiles live across different entities, regions, HR systems and company cultures. Consolidating and analyzing skills data becomes particularly complex.
The Innovation Department therefore sought to make better use of internal data to optimize talent management, while ensuring a better fit between profiles and business needs.
"This fragmentation held back our ability to fully leverage our human resources and to anticipate mobility needs at a global scale."
Innovation Director, automotive groupThe main challenge was to effectively reconcile information from heterogeneous HR systems, in order to quickly identify profiles suited to a role or an internal move.
A useful answer had to account for past assignments and performance, availability, employees' aspirations, and above all the internal mobility targets set by the group.
The group had long invested in data-lake type solutions to centralize its HR data. They store large volumes, but only present a two-dimensional view of it: fixed tables, isolated indicators, with no context and no understanding of the underlying dynamics.
No real impact on decision-making could be observed, for lack of a tool able to render information intelligible, interactive and action-oriented. Decision-makers were left facing raw data, with no clear link to operational or human concerns.
Previous roles, skills, performance, aspirations, availability: data is picked up where it lives, with no migration.
An interactive interface where the pool is browsed, rather than a list to be filtered.
Recommendations on the internal candidates best suited to a given role, with the path that justifies them.
Simple, fast access to consolidated data, with no need to go through a data team.
The solution integrates seamlessly with a data-lake type infrastructure. Thanks to native LLM integration, users query and explore the graph in natural language: opening up semantic analysis and knowledge discovery to non-technical teams.
"What's pretty wild is that users literally talk to their graph."
Innovation Director, automotive groupof targeted users use the tool daily: significant adoption for a cross-cutting HR tool.
less time to identify the key players in a change-management process.
interactions per month with the platform for the most active users.
Through guided comparative analysis between an employee's current context and the target context.
On planning and skills management in critical functions like supply chain.
We model a real HR scope, a few entities, a few job families, and you ask your mobility questions in natural language.
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