From Data Chaos to Decision-Ready at Oerlikon
From Data Chaos to Decision-Ready at Oerlikon
Building a scalable people analytics foundation in a federated industrial group
Oerlikon operates across dozens of countries, with local HR autonomy and deeply embedded industrial complexity. Over time, this created fragmented workforce metrics, low trust in numbers, and slow decision-making.
This session explains how HR rebuilt its people analytics foundation by focusing on design principles, operating model, and sequencing. It shows what was built, how it works day to day, what value it delivers today, and what it realistically enables next.
This session explores
- Why fragmented metrics and local KPIs blocked decision-ready analytics.
- Design principles that balanced federation, control, and speed.
- The data foundation built on HR, engagement, and finance inputs.
- The operating model that governs demand, ownership, and prioritisation.
- What the foundation enables today, and what remains unsolved.
Learning outcomes
- Recognise when people analytics problems are decision, not tooling, issues.
- Apply practical design principles for scalable, federated people data.
- Understand how to structure ownership between HR, IT, and Finance.
- Set realistic expectations for value delivered before advanced AI.
- Identify first steps to improve trust and speed in workforce decisions.
25 February 2026
15:55-16:25 CET
Miloš Matović
Head of HR Tech & Analytics
Value
Lens
Learning Pathways
Why this matters
Many organisations are investing in AI and advanced analytics while still struggling with inconsistent workforce data, unclear ownership, and low trust in basic metrics. In complex, federated environments, these gaps directly slow decisions, weaken HR credibility, and limit any realistic path towards scalable, responsible AI-enabled insight.