Sectors
at the heart of data.

Massive data volumes, strong regulatory requirements and growing pressure on customer experience: data is a strategic asset at the heart of the banking model.

• BCBS 239, GDPR, AI Act compliance and continuous regulatory reporting
• Fighting fraud and financial crime with AI
• Personalising advisor and customer journeys with GenAI
• BI industrialisation and real-time risk management

Pricing, claims, prevention: insurance is a business of models and data. AI and data science find immediate application here.

• Actuarial models, dynamic pricing and risk management
• Fraud detection and automation of claims handling
• 360° policyholder view and contract personalisation
• Solvency II, DORA compliance and ACPR requirements

Energy transition, asset optimisation and an explosion of IoT data: the sector combines innovation with very heavy industrial requirements.

• Predictive maintenance and reliability of industrial assets
• Energy performance and production optimisation
• Data platform at scale for multi-country organisations
• Computer vision and AI for site safety

Distributed sites, legacy ERPs, external growth: industrial data is fragmented. Structuring it is the prerequisite for any transformation.

• Group reference data and post-acquisition harmonisation
• Operational and supply-chain performance management
• Multi-country, multi-language change management
• Data maturity scales for subsidiaries and BUs

Patient data, clinical trials, R&D: a sector where quality, traceability and compliance are non-negotiable, and where AI is redefining the value chain.

• Master Data Management for products, sites and clinical trials
• GxP, GDPR compliance and international standards
• Accelerating R&D with data science and AI
• Real World Evidence and use of real-life data

Complex networks, dense operational flows and a service-continuity imperative: data directly feeds the quality of the passenger experience.

• Capacity forecasting, FinOps and cloud cost optimisation
• Self-service analytics for business units (Text2SQL, GenAI)
• Pipelines as code and hardening of operational flows
• Predictive maintenance of rolling stock and infrastructure

Customer experience, desirability, omnichannel: data must serve the elegance of the brand as much as commercial performance, online and in store.

• 360° customer view and premium omnichannel personalisation
• Demand forecasting, allocation and scarcity management
• Counterfeit detection and product traceability (NFC, blockchain)
• GenAI for creative marketing and customer relations

Multi-tenant, fragmented digital journeys and a constant need for productivity: data must serve both the end user and internal efficiency.

• Data product management and GenAI for business productivity
• Multi-tenant mobile platforms and personalisation
• 360° customer view and use of transactional data
• Industrialisation of BI and steering

Massive audiences, continuous content streams and platform competition: data and AI decide engagement and time spent.

• Recommendation, audience analytics and churn reduction
• Automated moderation and AI-assisted editorial quality
• Advertising performance and cross-channel data activation
• Telecom networks: QoS management, traffic forecasting, NetOps

Sovereignty, transparency and day-to-day public service: public data is a lever for modernisation, within a demanding and increasingly regulated framework.

• Data sovereignty and trusted hosting (SecNumCloud)
• Open data, transparency and quality of public reference data
• Generative AI in the service of public servants and citizens
• GDPR, AI Act compliance and ethics of public algorithms