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, AMMON LABS

Moderator: Elena Alikhachkina, Chief Data and AI Officer, TE CONNECTIVITY

 Senior Representative, REDPANDA  

• Why data transformations stall due to Legacy Friction and lack of accountability.
• How to identify "Data Debt" early and create a roadmap for Foundation Hardening that supports (but is distinct from) AI experimentation.

• 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 ‘Ask Anything’ session is designed for open exchange — bring your challenges, compare approaches, and leave with practical ideas you can apply immediately.

Discussion points:

• What makes data “trustworthy” at scale?: Metrics and observability practices.
• How do lineage, monitoring, and governance translate into real trust?: Decision-making approaches for creating transparent and auditable data pipelines.
• Data Reliability today: What’s working in practice, and what isn’t?

• 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.

• Defining decision ownership across data, AI, and business teams.
• Governance structures that clarify accountability without slowing innovation.
• Aligning leadership roles to support responsible AI deployment and scale.

• How do CDAOs prioritize enterprise data investments to support business objectives?
• The importance of aligning data strategy with broader organizational strategy and operational goals.
• Balancing investment between foundational data infrastructure and analytics innovation.
• Establishing governance and portfolio management frameworks for enterprise data initiatives.

Panellists: 

Senior Representative, FLEXOR AI 

Anu Sundaram, Vice President, Business Analytics, RUE GUILT GROUPE

Mitesh Shah, Chief Data Officer, CARDWORKS

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

• 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.

• Why AI trust begins at the data pipeline level — not at the model.
• How to design data flows that are traceable, auditable, and aligned with emerging global standards.
• The role of metadata, lineage, and data provenance in enabling explainable and compliant AI.
• Lessons from ISO-certified AI practices in a complex, regulated environment.

• Applying FinOps principles to improve visibility and control over data and AI infrastructure spending.
• Establishing governance models that link technology investment to measurable business outcomes.
• Practical approaches for balancing cost discipline with continued innovation and experimentation.

• 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?

Panellists:
Victoria Gamerman, Global Head of Data Governance and Insights, BOEHRINGER INGELHEIM

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

• 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:

Senior Representative, DATACAMP

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

Swathi Varahabhatla, Healthcare Business and Data Analytics Lead, BLUE CROSS BLUE SHIELD OF MASSACHUSETTS   

• 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.

• What executives must change in communication, incentives and enablement for AI success.
• Upskilling strategies tailored to modern hybrid human–AI workflows.
• Building internal evangelists who accelerate adoption across business units.
• Responsible leadership practices to manage fear, resistance and displacement concerns.

• How to structure a data team around business outcomes, not just technical delivery.
• Case studies in activating 1P audience intelligence — from dynamic paywalling to paid media optimization.
• Lessons learned connecting content intelligence to personalization and commercial results at scale.

• How can organizations treat data platforms as core enterprise infrastructure rather than project-based initiatives?
• Frameworks for prioritizing enterprise data investments while balancing foundational infrastructure and analytics innovation.
• Approaches for establishing domain ownership and managing data as products across the organization.
• Key indicators leaders use to measure the impact and maturity of enterprise data strategy.

• 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

Aggie Prakash, AI Integration Specialist, MCKINNEY

Ferhat Yilmaz, Director - Enterprise and Pipeline Insights & Analytics, NOVARTIS