Zurich Insurance

In Property & Casualty insurance, data accuracy and speed are not optional — they underpin underwriting decisions, claims processing, and regulatory compliance. Zurich Insurance's data operations were ill-equipped for this burden. A centralised team of roughly 30 professionals was expected to serve 5,000 employees across seven distinct brands, creating a bottleneck that scaled poorly. Data was treated as a byproduct of IT delivery rather than a strategic asset in its own right, leading to manual processing workflows, fragile point-to-point integrations, and an inability to respond to cross-functional demands in a timely manner. Regulatory queries — a routine pressure in P&C — could take weeks to resolve, exposing the organisation to unnecessary compliance risk.

Rather than pursuing another centralisation effort, Zurich Insurance redesigned its data operating model across three iterative strategy cycles spanning nearly a decade. The organisation built a hybrid architecture: a central centre of excellence providing standards, governance, and tooling, paired with embedded capability squads — data professionals deployed directly within individual business functions. This federated model reduced delivery latency by placing analytical expertise where decisions are made. The transformation required rebuilding the entire data and reporting estate from the ground up, alongside a sustained programme to upskill both data staff and business stakeholders. Strategy evolved progressively — from a risk-first posture, through a technology-led phase, to a balanced 50/50 focus on technology capability and organisational data literacy, ensuring each phase matched the organisation's actual readiness.

The restructured model delivered measurable operational gains alongside broader cultural change. Regulatory queries that previously required weeks of manual data retrieval can now be resolved in minutes, a step-change in compliance responsiveness critical for a regulated P&C insurer. Cross-functional data delivery became faster and better aligned to business priorities, with capability squads reducing the handoff friction that had slowed previous workflows. Key outcomes include:

• Regulatory response time: reduced from weeks to minutes
• Data culture: measurable improvement in data literacy across all organisational levels
• Talent retention: formalised career pathways for data professionals reduced attrition
• Delivery quality: cross-functional alignment improved as embedded squads built contextual business knowledge

• Match the model to regulatory reality: In P&C, full data mesh risks governance gaps — a hybrid centre-of-excellence with embedded squads balances agility with compliance control.
• Respect organisational maturity: Advancing data strategy faster than the business can absorb creates unsustainable outcomes; each phase should consolidate before the next begins.
• Embed, don't centralise delivery: Placing data professionals within business functions accelerates delivery and builds the mutual understanding that sustains long-term data culture.
• Formalise career paths early: Talent retention in data roles requires visible progression; treat this as infrastructure, not an afterthought.