Today’s enterprises face immense pressure: scaling fast, staying compliant, and unlocking AI-driven insights—all while fighting siloed data and growing cloud complexity. There is a better way forward, and it starts with Cloudera’s vision for the cloud experience anywhere. Cloudera is the only data and AI platform that delivers the cloud experience anywhere—public clouds, data centers, and the edge—bringing unified security, governance, and control to data wherever it resides. Access 100% of your data for AI-driven insights and future-proof—not just modernize–your enterprise data strategy.

Now that AI coding tools have put development capabilities into more hands, prototypes are becoming business-critical applications almost overnight. Shanea Leven sees an opportunity for a new generation of builders, provided the infrastructure around their applications keeps pace. Shanea explains how organizations can give developers and new technical employees room to build while maintaining the standards required for enterprise software..

Use Qlik Answers to manage and create your Data Products!

The challenge for most data teams isn’t a lack of ideas—it’s the time it takes to turn those ideas into something usable. In this session, Steffen Bischoff, Chief Architect Data at Qlik, follows a single dataset from a core system through its entire journey to becoming a governed data product. You’ll see pipelines created by describing intent instead of writing code, versioned in Git, then curated, quality-checked, and documented with the help of specialized agents. From there, the data product is made available to the AI tool of your choice through the Qlik MCP Server.

Semantic layers and ontologies have moved from nice-to-have data modeling tools to the foundational engine required for enterprise AI. In this episode, Raman Tallamraju, Senior Director and Head of Enterprise Data Architecture and Engineering at Vanguard, breaks down how Vanguard is architecting its AI semantic layer to turn scattered institutional knowledge into reliable, agent-ready context. He shares why autonomous agents expose decades of hidden data debt, how to bridge domain-specific definitions like clients versus prospects, and how to balance building a unified semantic layer with a pragmatic, federated data operating model.