Becoming truly data-driven requires more than adopting new tools—it demands clear alignment between business goals and data architecture. This practical, half-day session guides attendees through a proven, four-step methodology for designing modern data platforms that deliver real, scalable business value. Participants learn how to convert business priorities into architectural choices and critically assess emerging technologies—from cloud-native platforms to data lakehouses and data fabrics—to build a focused, actionable road map. Data leaders gain practical frameworks to determine which components of the modern data stack will create the greatest impact for their organization. Attendees leave with a clear road map and the confidence to drive meaningful, scalable transformation across their data ecosystem.

Semantic layers stand out as a key approach to solving business problems for organizations grappling with the complexities of managing and understanding the meaning of their data. A semantic layer, also called a context layer, is a business representation of data that allows organizations to quickly map various data definitions from multiple data sources to familiar business terms, offering a consistent and consolidated view of data. Join our workshop to gain insights into the foundations of semantic/context layers, their implementation, and the business value they provide by enhancing the utility of your data. The workshop promises an interactive experience, offering participants the opportunity to both understand the nuances of semantic/context layers and actively engage in constructing one.

In this interactive workshop, participants move from high-level strategy to concrete data models that power modern analytics platforms. As a continuation of the morning workshop, “How to Accelerate Business Impact When Building Your Data & Analytics Road Map,” participants engage in hands-on exercises to design data structures aligned with business goals and decision-making processes. The session emphasizes how effective data modeling enables value creation, efficiency gains, risk reduction, and strategic alignment in an environment of uncertainty and disruption. Participants explore practical techniques to connect strategy, architecture, and analytics so that data investments consistently deliver business value.

This hands-on workshop provides a practical introduction to retrieval-augmented generation (RAG), showing how to ground LLMs in trusted, up-to-date data for more accurate and explainable results. Participants build an end-to-end RAG pipeline with guided, runnable examples: document ingestion and chunking, embedding generation, vector indexing, retrieval strategies (keyword, dense, and hybrid), prompt construction, and response citation. Attendees explore techniques to improve quality and robustness, including query rewriting, reranking, metadata filtering, evaluation of retrieval and generation, and defenses against hallucinations and prompt injection. The workshop also covers operational considerations such as latency/cost trade-offs, observability, and updating indexes as data changes. By the end, attendees leave with reusable reference code, a checklist for diagnosing common failure modes, and a clear set of patterns for deploying RAG applications in production.