CDAO New York schedule

• How is the CDAO role evolving across industries?
• Who owns the data contracts in a world of third-party LLMs?
• Insights on aligning Federated Data Ownership with global privacy standards.
• Positioning the CDAO as the architect of Institutional Memory rather than just a technical lead.

Panellists:  

Andrew Patricio, Chief Data and AI Officer, UNIDOSUS 

Linda Hermer, Chief Data Strategy Officer/ CEO, AMMON LABS/ VANTAGE PRECISION

Patrick Angeles, Senior Director, Customer Engineering, REDPANDA

Joshua Burkhow, Chief Evangelist, ALTERYX  

Moderator: Elena Alikhachkina, 4x Fortune 500 Chief Data Officer & Board Director, NACD 

• Identifying the architecture maturity gaps preventing organizations from scaling analytics and AI.
• Strengthening core capabilities to create reliable and scalable data foundations.
• Key milestones for assessing enterprise data readiness and prioritizing architectural improvements.

This session explores how empowering employees with direct access to data transforms decision-making, operational efficiency, and organizational agility. The abstract highlights how self-service analytics reduces reliance on technical teams, accelerates insights, and enables business users to make faster, data-driven decisions with confidence. It also examines the measurable business outcomes of this shift, including improved productivity, stronger collaboration, and a competitive advantage in rapidly changing markets.

• Self-Service Analytics Empowerment: Organizations gain agility and operational efficiency by giving business users direct access to data, reducing dependency on technical teams, and accelerating time-to-insight.
• Faster, Confident Decision-Making: Equipping employees with self-service tools enables data-driven decisions at speed, fostering a culture where insights are accessible to all, not just data specialists.
• Measurable Business Outcomes: The shift to democratized data delivers tangible results: increased productivity, stronger cross-functional collaboration, and a sharper competitive edge in dynamic markets.

• Governance and accountability frameworks that improve enterprise data trust.
• Practical approaches for measuring and maintaining data quality at scale.
• Embedding trust mechanisms into operations without slowing innovation.

• How to prioritise enterprise data investments between foundational capabilities and high-impact, revenue-generating use cases.
• Moving from centralised data platforms to data products with clear ownership, SLAs, and P&L accountability.
• Embedding governance and portfolio management frameworks that enable scale without slowing delivery.
• Identifying which foundational investments (e.g. MDM, quality, lineage) truly create the “ground truth” required for AI and advanced analytics.

Panellists:

Or Zabludowski, CEO & Co-Founder, FLEXOR AI  

Soumya Ghosh, SVP - Head of Data Engineering, Architecture & Analytics, CITY NATIONAL BANK

Sudheesh Kamath, Global Head of Commercial Data and AI Products, KENVUE 

Moderator: Justin Heller, Chief Data & AI Officer, QUANTIFY DATA ADVISORS  

• How structured refinement & approval processes unlock insights across AI initiatives.
• Turning fragmented AI efforts into a connected system of reference.
• Using program-level intelligence to move faster and allocate capital with confidence.

• How can organizations build enterprise data platforms that support analytics, AI, and decision-making at scale?
• Designing trusted, interoperable data foundations that enable consistency, reuse, and agility across the business.
• Establishing effective ownership and stewardship models to align business and technology teams.
• Modernizing data ecosystems to support AI innovation while maintaining security, governance, and resilience.

Legacy batch ETL pipelines and fragmented data silos aren't just expensive — they're dangerous. By consuming up to 80% of IT budgets on maintenance and creating OLTP-to-OLAP visibility gaps, they expose autonomous AI agents to stale data, the root cause of model hallucination and flawed operational decisions. This session demonstrates how a converged, zero-ETL architecture — enforcing governance directly at the data layer eliminates pipeline latency, neutralizes vendor lock-in, and gives AI agents the real-time, high-fidelity data they need to act safely and decisively. Attendees will leave with a FinOps framework for data and a Return on Data (ROD) model to help CDAOs transform their data stack from a maintenance cost center into a sovereign governance engine for agentic AI at scale.

• Which measurement frameworks help organisations track the real impact of data and analytics initiatives?
• What KPIs best demonstrate operational improvement, decision quality, and business outcomes?
• How can leaders standardise metrics to prioritise and scale enterprise data investments?
  

Krishan Kumar Sharma, Senior Vice President, CITIGROUP

Meetu Malhotra, Data Analytics Principal (Data Scientist lll), S&P GLOBAL

Goutham Budati, VP of Data Strategy and Insights, THE FARMER’S DOG

Aaron Doherty, Head of AI, CHOWLY

Moderator: Victoria Gamerman, PhD., Advisor/ Former Global Head of Data Governance and Insights at Boehringer Engelheim, RWD

• How can leaders translate data and AI outcomes into business metrics that resonate with the board?
• What storytelling frameworks help communicate the strategic value of data investments?
• Which KPIs and reporting models best demonstrate enterprise impact and justify continued investment?
• How can leaders align executive stakeholders around a clear narrative of value and risk?

Panellists: 

Nicholas Aquilino, FVP, Head of Data Analytics and Workflow Automation, APPLE BANK 

Joe Raaen, SVP, Data Strategy, PUBLICIS MEDIA 

Shipali Jangra, Director, Digital Product Management, AMERICAN EXPRESS 

 
Moderator: Khyati Sharma, Associate Director of Technology, CURE AUTO INSURANCE (Citizens United Reciprocal Exchange) 

• What operating model best supports scalable enterprise data and analytics delivery?
• How should organizations define ownership across data products, engineering, analytics, and business teams?
• Approaches for funding and governing enterprise data initiatives.

• Assigning a business owner and P&L targets to every AI initiative with clear KPIs and SLAs.
• Stack-ranking bets by ROI and time-to-value, time-boxing pilots, and doubling down on compounding wins.
• Productizing models into workflows/agents and driving adoption with enablement, incentives, and change management.
• Instrumenting value end-to-end: establishing baselines, unit economics, TCO, and risk-adjusted ROI with continuous optimization.

• How operating models and ownership structures are evolving as AI moves into core business workflows
• Who is accountable for AI — and how responsibilities shift across data, technology, and business teams
• The talent, skills, and cultural changes required to move from experimentation to sustained adoption
• Embedding incentives, governance, and execution discipline to ensure AI delivers consistent business impact

Panellists:  
Nishit Dhilen Mehta, Vice President, Analytics Solutions Manager, JPMORGAN CHASE 
Bin Yu, Ph.D., VP of AI & Data, STARBUCKS 
Ferhat Yilmaz, Director - Enterprise and Pipeline Insights & Analytics, NOVARTIS 

Moderator: John Shaw, Chief Executive Officer, PRAXIS IQ