Sarah Levy

As organisations race to deploy AI across the enterprise, many are discovering that the hardest part is not choosing the right model. It is giving AI enough business context to produce accurate, reliable, and cost-effective results at enterprise scale. This panel explores what "context" actually means for enterprise AI, where semantic layers, ontologies, and governance fit into the picture, and what organisations are learning as they move AI initiatives from pilot to production. Drawing on real-world examples, these industry leaders will share practical lessons, common pitfalls, and the context that makes the difference between AI and enterprise AI.

AlphaSense paired dbt's governance with Euno's AI context platform to rebuild legacy models, redesign billing architecture, and scale conversational analytics across the business.

Self-serve analytics promises speed. But without clear guidance, it often leads to hidden obstacles like cluttered dashboards, runaway costs, and a loss of trust in the data.

Practicing analytics well takes more than just tools and tech. It requires data modeling practices that unify and empower all teams within analytics, from engineers to analysts. This is especially true as AI becomes a part of analytics. Without a governed data model that provides consistent data interpretation, AI tools are left to guess. Join panelists Joe Reis, Sarah Levy, Harry Gollop, Rob Hulme, Shachar Meir, and Guy Fighel, as they share battle-tested advice on overcoming conflicting definitions and accurately mapping business intent to data, reports and dashboards at scale. This panel is for data & analytics engineers seeking a clear framework to capture business logic across layers, and for data leaders focused on building a reliable foundation for Gen AI.

Managing metrics across teams can feel like everyone’s speaking a different language, which often leads to loss of trust in numbers. Based on a real-world use case, we’ll show you how to establish a governed source of truth for metrics that works at scale and builds a solid foundation for AI integration. You’ll explore how Bolt.eu’s data team governs consistent metrics for different data users and leverages Euno’s automations to navigate the overlap between Looker and dbt. We’ll cover best practices for deciding where your metrics belong and how to optimize engineering and maintenance workflows across Databricks, dbt and Looker. For curious analytics engineers, we’ll dive into thinking in dimensions & measures vs. tables & columns and determining when pre-aggregations make sense. The goal is to help you contribute to a self-serve experience with consistent metric definitions, so business teams and AI agents can access the right data at the right time without endless back-and-forth.

Adopting dbt marks a significant leap towards governed data transformations. But with every game-changer, big questions arise: Where do data transformations end?