Opening & Welcome
Andrew Delaney, President & Chief Content Officer, A-Team Group 

Practitioner Keynote Fireside Chat: Delivering AI value at scale

• Delivering AI value at scale – What defines an AI-ready data strategy and how do you measure readiness across the enterprise?
• Governance at the core – What are the critical capabilities and operating models needed to scale AI governance across the organization?
• Agentic AI – Exploring its potential to redefine front-to-back workflows and decision-making

Interview with: Stephanie Zhang, MD, Head of Enterprise AI & Data Governance Products, JP Morgan
Interviewed by: Julia Bardmesser, Adjunct Professor; NYU Stern School of Business; Founder and CEO, Data4Real

C-Level User Panel: Demonstrating business value from data and AI

As enterprises scale AI initiatives, CDOs are under pressure to prove value, avoid costly pitfalls, and align efforts with business outcomes. This panel will explore what it takes to deliver impact from strategy to execution.

• How should you link AI strategy to business benefits with measurable outcomes?
• Taking a holistic approach to AI strategy and focussing on big outcomes: who are the key partners for the CDO to enable scaling and transformation? 
• Managing and governing unstructured data: is this the next frontier?
• Why are data products essential as a foundation for accelerating AI and ML?
• What architectural models best support accessible, AI-ready data across the enterprise?
• How will agentic AI impact data management and data operations?

JC Lionti, MD, Chief Data Officer, Mizuho Americas

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Panel: How to ensure high quality and trusted data for AI

• What methods and frameworks are emerging to improve data quality and observability, and how are firms operationalizing them to support AI initiatives?
• What structured approaches can organizations use to identify, mitigate, and monitor the risks of poor data quality in the context of AI output reliability.
• How can AI, GenAI, and LLMs be embedded into data management and governance workflows to enhance data quality, streamline remediation, and increase trust in AI outputs?
• What ‘shift-left’ practices are proving most effective in addressing data quality issues early in the pipeline? Where should they be built into modern data engineering workflows to benefit AI use cases?
• What best practice frameworks and governance models across the data lifecycle promote high data quality standards ensuring explainable and responsible use of AI?

Moderator: John Bottega, President, EDM Council
Ellen Gentile, Enterprise Data Quality Team Leader, Edward Jones

Panel: Agentic AI and autonomy in the future of data management

• What is the realistic roadmap for Agentic AI adoption in enterprise data management and how will it reshape current models of governance, quality, and control?
• What technical and organizational barriers must be overcome to enable agentic systems especially around trust, explainability, and human oversight?
• What foundational capabilities such as data readiness, metadata, semantic layers, and observability must be in place to support effective and scalable Agentic AI?
• How can firms balance autonomy with accountability in agentic systems? What is the role of the ‘human in the loop’?
• What are the most compelling use cases where Agentic AI can deliver measurable business value? How can you explore these opportunities today?

Moderator: Marla Dans, Senior Data Executive
Brian Greenberg, Senior Director & Data Operating Model Lead, Bank of New York

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Panel: Building and scaling data products and marketplaces to deliver ROI at scale

• What are the most common organizational and technical barriers to data product adoption and how are firms overcoming them?
• How can next-gen data architectures enable scalable, fast deployment of data products and marketplaces?
• What governance and semantic practices are helping firms improve discoverability, ensure trust, and foster collaboration across teams?
• How is AI reshaping the way data products are created, curated, and consumed within internal and external marketplaces?
• What KPIs and value frameworks are emerging to measure ROI and business impact from data product and marketplace investments?

Andrew Foster, Chief Data Officer, M&T Bank

Panel: Building the next generation data architecture and intelligent data ecosystem

• How are firms architecting cloud-native data platforms that enable real-time data sharing, AI enablement, and interoperability across the enterprise?
• What design principles are guiding the handling of structured and unstructured data within a unified storage and access framework? What event-driven architectures support this evolution?
• How are firms implementing semantic data layers that are essential to enabling data reusability, lineage, and cross-functional intelligence?
• What are effective methods for embedding KPI-driven control frameworks into the data operating model to measure and continuously improve data performance and business outcomes?
• How are AI-driven data pipelines being operationalized to accelerate insight delivery, reduce latency, and support the demands of next-gen analytics and intelligent workflows?

Moderator: Brian Buzzelli, Head of Data Practice, Meradia
Jehangir Abdulla, Head of Back Office Development, Schonfeld

Keynote

Panel: Managing the regulatory reporting new normal – the data management response

• Balancing cost vs change: How should firms balance heightened demands from regulators and constant change from re-writes vs. keeping up with BAU?
• Regulators want transparency – how are firms progressing with data lineage and moving to more automated approaches?
• What progress is being made by financial institutions and regulators to implement data standards and what more could be done?
• Data accuracy and data quality: What controls should firms put in place to proactively prevent data errors in reporting?  
• What progress are firms making in modernizing data infrastructure to break down silos and create a single, streamlined reporting platform to improve efficiencies?
• How can firms leverage the foundational work for reporting to generate alpha and business value?
• What are the opportunities for AI to reduce human error and drive greater data quality to achieve data standards set out by Regulators?

Moderator: Dessa Glasser, Independent Board Member, Oppenheimer & Co
Tamara Roust, Senior Policy Advisor for AI Cyber Security & Privacy, Deputy Chief Data Officer, US Treasury Dept, Office of Financial Research (OFR)

Networking drinks and Data Management Insight Awards Ceremony