CDAO NY schedule

• Who truly owns enterprise data across business domains and technology teams?
• How is the CDAO role evolving across industries?
• Insights on aligning governance, architecture, and operating models to support both control and innovation.
• Practical approaches for positioning the data function as a driver of measurable enterprise value.

• Why do data transformations lose momentum, and where do data ownership and accountability typically break down across the enterprise?
• What most commonly stalls enterprise data programs, and how can leaders identify early warning signals before progress slows?
• Practical approaches for resetting governance and delivery models while strengthening ownership, accountability, and cross-functional alignment.

• 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.

• Key metrics and observability practices leaders use to measure and maintain enterprise data quality.
• How lineage, monitoring, and governance frameworks improve trust in data used for analytics and decision-making.
• Practical approaches for embedding data reliability and accountability across enterprise data ecosystems.

• 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 

• 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.

• What governance frameworks best support advanced analytics while maintaining control and accountability?
• How can organizations strengthen data architecture to support scalable analytics and emerging intelligent systems?
• Establishing responsible experimentation models that enable innovation without compromising oversight.
• Lessons on managing risk and regulatory compliance as data capabilities expand.

• 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 organizations track the real impact of data and analytics initiatives?
• What KPIs best demonstrate operational improvement, decision quality, and business outcomes?
• How can leaders standardize metrics to prioritize and scale enterprise data investments?

• 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 

• 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.

• What in the data stack creates value — and what doesn’t?
• How leading enterprises turn data and AI investment into measurable outcomes.
• The next critical decisions CDAOs must make to sustain impact 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 success quietly reshapes the CDAO mandate.
• Which responsibilities should be expanded, and which should be handed off?
• Managing relevance once AI is embedded across the business.
• Preparing successors and future leadership models.