For AI-driven analytics to really shine, access to curated business metadata is critical. But this requirement can lead to architectural complexity as different solutions are integrated to deliver data storage and processing, metadata curation, model management and data access policies.

In this session, we’ll look at why business metadata is so important to conversational analytics and consider what architectural layers are required to deliver it as part of an integrated GenAI Analytics platform. We will look at how these extra requirements deliver capability at the cost of architectural simplicity and consider how Starburst’s integrated analytics platform reduces complexity whilst still providing architectural optionality through open standards.

Takeaways

• Why & how business metadata impacts conversational analytics capabilities
• How the need for metadata integration leads to increasing complexity in logical & physical architectures
• How Starburst can help tame the complexity whilst maintaining architectural optionality