Serge Gershkovich

Join us for a C-level discussion on how Snowflake unifies product data, enables real-time analytics and drives measurable business impact. Attendees will learn how centralized product intelligence can transform decision-making at the executive level, supporting faster go-to-market strategies, operational efficiency and cross-functional collaboration.

AI initiatives often stall not because of algorithms but because underlying data lacks structure, meaning and governance. Without a semantic foundation, AI agents and analytics tools struggle to interpret business context. In this session, we explore how semantic modeling bridges the gap between business meaning and AI-ready data. Learn how modern data teams use structured semantic layers, governed data models and collaborative design workflows to turn ambiguous business questions into accurate AI outputs, enabling trusted analytics, reliable AI agents and scalable data products on Snowflake.

In this session, a representative from Fortune Brands Innovations Inc. will share their experience accelerating their Snowflake implementation. They will discuss challenges; strategies to improve modeling, version control and governance; and how they streamlined workflows for faster deployment. Joining them, SqlDBM's head of product will provide insights into best practices for modeling and governance. Attendees will gain insights into strategies and lessons from their implementation.

Data engineers value documentation, governance and best practices but often struggle to convey their financial impact. Lacking fluency in business language and metrics, they find it challenging to demonstrate the ROI of data modeling and governance. This talk uses real-world examples to highlight how these practices improve data quality and team efficiency while linking them to business metrics that resonate with stakeholders and the finance committee.