CDAO Dallas  
Presentations

When done right, governance is a growth engine. In this talk, Jean-Georges “jgp” Perrin will show how data contracts bring precision, trust, and accountability into your data and AI pipelines—without creating bottlenecks. Using the Open Data Contract Standard (ODCS) from the Linux Foundation’s Bitol project, you’ll see how organizations can cut downstream defects, accelerate AI model onboarding, lower compliance risk, and reduce firefighting—often in just days. 

Beyond the pilot phase, real-world GenAI implementation brings new challenges. This session shares tested strategies for navigating that shift. 

- Balancing innovation with oversight in regulated environments 

- Embedding GenAI into workflows without disruption 

- Mitigating hallucinations and measuring what matters 

- Governance frameworks that support scalable GenAI use 

Agentic AI promises to transform how organizations automate business processes - but without a solid data foundation, these initiatives often falter. When AI agents act on behalf of users, they must be able to inherit security permissions, access the right data in real time, and understand business context. Without that, they risk delivering inconsistent answers, exposing sensitive information, or driving the wrong outcomes. 

This session explores why a unified data and business context layer is essential for making Agentic AI both powerful and trustworthy. We’ll discuss the pitfalls organizations face today - from fragmented data silos that starve agents of critical context, to ad-hoc integrations that bypass governance and erode trust. 

We’ll then show how leading enterprises are addressing these challenges with a single, governed data access foundation that gives agents and users consistent, explainable, and secure insights at scale. 

If you want your AI initiatives to move beyond pilots and reliably drive business value, this session will provide the roadmap to get there. 

Enterprises are increasingly using AI to interpret text from customers, partners, and regulators.  

Entity extraction and sentiment analysis are core capabilities for transforming unstructured text into structured, actionable data. 

The newest large language models (LLMs) enhance these capabilities but require more than accuracy — they demand orchestration, governance, and activation. 

When properly implemented, AI-native text processing enables: 

- Faster issue resolution 

- Prioritized customer retention strategies 

- Proactive compliance alerts 

- More relevant product recommendations 

- Treating unstructured text as a governed, real-time data source helps enterprises turn conversations into competitive advantage. 

Analytics is broken at the last mile, where insights stop short of action, costing organizations millions in delay, waste, and inaction.  

Learn how a three-tier Agentic analytics architecture fuses diagnostics, LLM reasoning, and autonomous action to close this gap.  

See how enterprises can shift from dashboards to speed of light, closed-loop decision systems, unlocking new levels of resilience and ROI.