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DGIQ-EDW26: AI-Ready Data

This is Subscription-Only Content, It is NOT purchasable as a separate product

In today’s AI-first era, customers expect data products to be deeply interconnected, consumable with minimal effort, and widely available. The need for "AI Ready Data," suitable for consumption directly by AI agents, has never been clearer.

Customers have been clear that receiving "just" data is no longer sufficient. They expect data to be immediately accessible, usable, and understandable to both human and AI consumers with “zero ETL” (Extract, Transform, Load). We will discuss and demonstrate the direction that S&P Global is taking, explicitly aimed at serving this need, greatly increasing the insight available to customers. This includes the concept of providing machine-readable metadata at the column level for datasets. This metadata permits AI and ETL tools to automatically ingest and connect delivered data to a customer’s own data, as well as automatically import that data into a customer’s data catalog.

Attendees will gain a firsthand understanding of how S&P Global is using RDF and URIs to simplify data integration and enable AI use cases.
Global Impact: Promotes transparency, interoperability, and innovation across industries by democratizing access to high-quality reference and metadata.
Accelerated Time-to-Value: Rapid, seamless data integration for AI and analytics.
AI Readiness: Initiatives simplify the process of making data actionable for AI, improving efficiency and outcomes for users.

Speaker: Hamish Brookeman, VP - Enterprise Data Architecture, S&P Global Enterprise Data Organization

Hamish is responsible for Enterprise Data Architecture, which is responsible for the overall design of managed data structures, including strategies for data implementation, acquisition, and maintenance, and evaluating data sources for adherence to quality standards and ease of integration. The specific role is to capture data requirements clearly, completely, and correctly, and represent them in a formal and visual way through the data models. In addition, making sure that data integration is based on a common metadata framework and that the integrated data is presented to the business as valid information.

Hamish previously served in a similar role for S&P Global Market Intelligence. Hamish joined S&P Global in 2015 via the SNL Financial acquisition, where he had served as Head of Data Architecture since 2006.

Hamish has 25+ years of experience in technology leadership, large abstract datasets, and highly engineered information systems. He has extensive knowledge of Structured, Semi-Structured, and Unstructured data strategies. Hamish attended Princeton University, where he studied Economics and Politics.