EY’s Data Fusion is a cloud-native, AI-driven data management solution built on Databricks to meet the complex data, AI, and analytics demands for financial institutions.
Discover how EY Data Fusion is transforming data management for today’s financial services organizations. This video provides a deep dive into EY Data Fusion—a cloud-native, AI-powered data platform purpose-built to address the complex data and analytics needs of financial institutions. It delivers trusted, AI-ready data by streamlining data processing through an intuitive user experience—enabling financial services organizations to unlock actionable insights faster and with trust. Designed to support modern federated architectures, the platform scales seamlessly to handle large volumes of data and AI workloads while maintaining performance, reliability, compliance, and governance standards essential to financial services. Its embedded AI and GenAI features such as sensitive data detection, AI-driven data quality assurance, and natural language-based data exploration and transformation—significantly improves efficiency, strengthen data trust, and reduce manual effort. With a governance-first design and a low-code/no-code user experience, EY Data Fusion empowers business and technical teams alike to unlock the full potential of their data, accelerate innovation, and achieve sustainable, insight-driven growth.
With over 150 clients in production, 9,000+ data sources, and 6,000+ business rules, this platform is relied upon by leading financial institutions worldwide, making it a proven solution for the most demanding data challenges.
Key Benefits:
• Unified Data & AI with Embedded Governance
Establish compliance and trust through a unified governance framework that seamlessly integrates data and AI—with built-in lineage, validation, observability, and policy enforcement for regulatory alignment and enterprise control.
• Low-Code/No-Code Data Management Across the Organization
Empower teams across business lines to manage and utilize data with no coding skills necessary —accelerating adoption, agility, and pipeline development by up to 50% while supporting scalable AI workloads.
• Accelerated Modernization with Low Cost of Ownership
Modernize legacy systems rapidly with less overhead of standing up, operating and maintaining modern data platform —delivering innovation, operational efficiency, and measurable ROI at scale.
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