Building a Trusted Data Marketplace for AI: What You Have to Solve Before the Agents Show Up
About this session
Enterprise AI stalls when the data layer loses business meaning. Ownership blurs. Access drifts from intent. Quality rules live in too many places. Governance fragments across systems, teams, and tribal knowledge. Under that kind of pressure, AI products inherit confusion before they produce value.
This lightning talk draws from the experience of building an AI-powered data marketplace inside large regulated enterprises, where data discovery alone solves very little. In this environment, data has to earn trust. People need to know who owns it, how it is governed, how it should be interpreted, and whether it belongs in high-stakes workflows at all. The session moves past the familiar idea of a data catalog and gets into what it takes to build a marketplace that AI teams, business users, and governance partners can actually rely on.
It gets into the parts that usually stay buried: how to capture business-owned metadata with operational value, how to distinguish certified, draft, deprecated, and restricted assets, how to surface quality issues before someone consumes the data, how to tie access controls to business intent, and how to make ownership visible enough for accountability to hold as data moves into downstream AI use.
It also looks at the shift from passive documentation to active infrastructure. In most enterprise settings, people still piece together data context through old reports, tickets, shared drives, relationships, and institutional memory. AI raises the cost of that fragmentation. When a person cannot tell which metric has approval, which source carries authority, or which dataset is safe to use, an AI system will only move faster in the wrong direction. It will retrieve, summarize, and automate on top of ambiguity.
Using real implementation work, this session shows how a governed data marketplace can serve as the connective layer between data producers, business consumers, policy owners, and AI builders. It focuses on the metadata that matters most: ownership, certification state, lineage, quality thresholds, access context, usage constraints, semantic definitions, policy tags, and consumption patterns. Those elements give downstream search, analytics, agents, and AI workflows the context they need to operate with precision and accountability.
The talk closes on the broader point: when AI-powered analytics runs on top of a trusted marketplace, the marketplace becomes far more than a place to find data. It becomes part of the control plane for enterprise AI readiness.
The infrastructure choices organizations make now will shape whether AI adoption scales with trust and discipline, or whether every new use case reopens the same unresolved questions around meaning, ownership, and control.
Speaker
Key takeaways
- Builders will leave with a practical mental model for data marketplace architecture
- a clear view of the metadata layer required for AI readiness
- a grounded understanding of why this work is core infrastructure for scalable enterprise AI