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

Practitioner Keynote Fireside Chat: Beyond the hype – what does it mean to be AI-First?

• What defines an AI-first data strategy and how do you measure readiness across the enterprise?
• What are the key components for delivering AI value at scale across the organization?
• Governance at the core – What are the critical capabilities and operating models needed to scale AI governance across the organization?
• What is the potential for Agentic AI to transform front-to-back workflows and what data management challenges and opportunities do agents present?

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?

Moderator: Peggy Tsai, Executive Director, JP Morgan
JC Lionti, MD, Chief Data Officer, Mizuho Americas
Vanessa Jones-Nyoni, Chief Data Officer North America, Commerzbank
CJ Jaskoll, Chief Technology Officer, Russell Investments
Andrew Foster, Chief Data Officer, M&T Bank

Keynote: AI-ready data: Driving compliance, risk reduction and innovation with trusted data

As financial institutions look to scale AI across the enterprise, many are hitting a familiar roadblock: the data isn’t ready. Years of accumulated tech debt, fragmented infrastructure, and inconsistent data governance are now front and center, undermining trust in AI outputs and exposing firms to compliance risk.

This keynote will explore:

• Why AI adoption in financial services stalls and how to overcome foundational data challenges
• How to deliver high-quality, trusted, and governed data for compliant, explainable AI
• How AI-ready data accelerates impact in KYC/AML, fraud detection, risk modeling, and customer intelligence

Amy Horowitz, GVP of Solution Sales and Business Development, Informatica

Keynote: Building a hybrid data foundation to power the next generation of AI

Financial institutions are facing an explosion of structured and unstructured data across diverse environments—while juggling a fragmented tech stack, technology shifts, and a widening skills gap. Yet this challenge also presents an opportunity: with the right hybrid data foundation, organizations can unlock insights, reduce complexity, and prepare their business for the era of AI. In this session, you’ll gain practical strategies for building a resilient data foundation that spans on-premises and cloud environments—while setting the stage for the next era of AI.

The keynote will explore:

• The importance of hybrid data integration for financial institutions—balancing cost, performance, and security in a regulated industry
• Integrating and governing unstructured data to boost the accuracy of AI applications
• How agentic AI is transforming data management

Scott Buckles, Business Unit Executive, Data Fabric, IBM

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: Andrew Delaney, President & Chief Content Officer, A-Team Group
Ellen Gentile, Enterprise Data Quality Team Leader, Edward Jones
Manal Alimari, Executive Director, CDAO Business Partnerships and Product Management, SMBC
Eugene Coakley, Data Engineer, Datactics
Amy Horowitz, GVP of Solution Sales and Business Development, Informatica
Joe Gits, CEO, Context Analytics

Keynote: The CDO Playbook: Data as a business multiplier

In capital markets, bad data is your biggest risk—not a market crash. Fragmented, incomplete, and untrusted data causes every tech investment, from AI to T+1 settlement readiness, to stumble. CDOs must shift the conversation from data as a cost center to being the strategic linchpin for IT modernization. Infrastructure manages it. Applications use it. Security runs on it. People rely on it. AI learns from it.

Attend this keynote and learn how to:

• Quantify the ROI of Trust: Prove that a strong data foundation directly improves business outcomes like speed, cost, security, and compliance to secure executive buy-in.
• Enable AI Initiatives from Day One: Deliver the high-quality, well-governed data AI initiatives need to be reliable, ethical, and effective out of the gate.
• Balance Control and Access: Implement a modern operating model that enables data to flow across teams and platforms without compromising security.
• Deliver Tangible Wins: Identify the most effective, high-impact projects that build momentum and secure funding for your data strategy

Ray Sullivan, Vice President, Product Management, Data Modernization, Rocket Software

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

• 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?
• How can firms effectively manage and ensure the quality of data underpinning their critical data products?
• 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?

Moderator: Kamayini Kaul, Former SVP Group Chief Data Officer, Standard Chartered
Andrew Foster, Chief Data Officer, M&T Bank
Kristi Baishya, AVP, Data & AI Product Management, Nomura Holding America, Inc.
Gurprit Singh, Global Head of Data & Analytics, Partners Capital
Ray Sullivan, Vice President, Product Management, Data Modernization, Rocket Software
Eliud Polanco, President, Fluree

Keynote: From risk to opportunity – minimizing hallucinations in financial data

• How can data managers redefine data quality for the AI era to prevent hallucinations?
• How can firms architect for factual integrity by building a Retrieval-Augmented Generation (RAG) architecture that grounds AI outputs in verified structured and unstructured data, ensuring every answer is traceable?
• What is the playbook for prompt engineering, and how can data managers use it to craft precise queries that guide models toward factual answers and away from fabrication?
• How can data managers use fine-tuning to specialize AI models on proprietary data, with human oversight to build a feedback loop for continuous improvement?

Joe Gits, CEO, Context Analytics 

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
Vinod Surasani, Sr Software Engineer – MDM, RBC Wealth Management
Steven O’Bott, Chief Data Architect, Vanguard
Patrick Mahoney, Enterprise Sales Engineer, Rimes
Patrick Palomo, Principal Solutions Architect, Smarsh
Gille Halle, Data Integration Leader – Financial Services Market, IBM

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?
• What are the core components of a modern, automated reporting architecture?
• What role does data lineage play in satisfying regulators and simplifying root cause analysis
• How can firms implement robust data quality and governance controls to ensure report accuracy and auditability?
• How are cloud platforms transforming the scalability and cost-effectiveness of regulatory data management?
• 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)
Akhil Khunger, VP Quantitative Analytics, Barclays
Akshata Nayak, Director, UBS
Peter Gargone, Founder & CEO, n-Tier
Subash Chandar Elango, Chief Product Officer, ChainSys

Keynote: Building value through trusted EDM — Real world lessons

Operational success in financial services depends on trusted data. Downtime, fines, and technology failures often stem from fragmented information. Learn from our EDM deployment with a leading global financial risk and advisory firm, showing how strategic data management drives transformation and growth rather than just costs.

You’ll learn how to:

• Build a growth-ready data foundation: Deploy unified EDM that accelerates AI, analytics, and compliance
• Deliver business value: Turn data quality and governance into measurable speed, efficiency, and decision-making improvements
• Balance access with control: Enable innovation while maintaining security and regulatory compliance
• Generate quick wins: Identify high-impact projects that secure stakeholder buy-in and funding
• Apply proven strategies: Gain practical insights from a complete client transformation

Jay Balan, Global CIO, ChainSys 

Case study – Lessons learned: Shifting to Agentic AI for investment research

• What are the key architectural and design decisions for building agentic AI systems?
• What are the challenges and strategies for achieving data readiness at scale?
• How can strong partnerships across business and tech drive adoption?
• Real-world impact: How to measure KPIs and quantify business value?

Sri Bhupatiraju, VP, Senior AI Engineer, BlackRock AI Labs

Panel: Redefining data management with agentic AI

• 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
Tyler Frieling, Director of Enabling & Emerging Technology, Blackrock
Marina Kaganovich, AMERS Financial Services Executive Trust Lead, Office of the CISO, Google
Gorkem Sevinc, Founder & CEO, Qualytics

Data Management Insight Awards USA – Winner Announcement: Editor’s Recognition Award for USA Data Management Industry Professional of the Year

Networking drinks