Data Entity Mapping with AI - The Challenges and the Solutions

Enhancing company data, even when structured and cleaned, proves challenging without mapping it to industry-standard identifiers like S&P Global’s CapIQ IDs or various BECRS IDs. But entity mapping is a complex process for data engineering and analyst teams across industries.

Creating accurate mappings from company entities in one database to identifiers in an external one presents deep technical challenges due to the limited amount of contextual information and frequent name similarities, including among entities within the same corporate hierarchy. While fuzzy matching and rules-based mapping are common solutions, their reliance on semantic similarities limits their effectiveness.

Introducing AI-powered entity linking—a revolutionary approach that considers not only the submitted entity but also its associated data and the content of the target database. This innovative technology offers a more effective and powerful mapping capability.

Join us in this session as we explore AI entity mapping, including the Kensho Link solution, and its seamless integration with other S&P Global datasets. Discover how it addresses user initiatives such as:

• CRM clean-up
• Platform integrations 
• Security master file enhancement