Data Entity Mapping with AI - The Challenges and the Solutions

Entity resolution isn’t just a data management chore—it’s a core technical challenge for data engineering and analytics teams. Even “clean” company data becomes unreliable without consistent mapping to industry-standard identifiers. But traditional approaches like fuzzy matching and rules-based mapping often break down in the face of ambiguous names, multilingual variants, and complex corporate hierarchies. In this webinar, S&P Global explores how AI-powered entity linking goes beyond string similarity by leveraging contextual signals from both the source dataset and the target reference database. You’ll see how this approach enables more accurate and scalable entity mapping across massive datasets. Key takeaways: • Why fuzzy matching and deterministic rules fall short at scale • How AI models incorporate context and metadata to improve precision • Practical applications: CRM cleanup, security master file enhancement, and platform integrations