CDAO Chicago schedule

•    Moving beyond pilots and hype to real-world deployment
•    Why the most effective AI agents augment human judgment, not replace it
•    Trust, not technology, as the main barrier: data quality, hallucinations, and compliance
•    Turning agents into measurable business value, not impressive demos

•    Looking back, what is the single decision you would change and why?
•    What early signal or risk did you underestimate at the time?
•    What would you prioritize in the first 90 days if you were starting again today?
•    What advice would you give executives facing the same challenge now?

•    Why fragmented data stacks are collapsing under AI and analytics demands
•    How converged platforms simplify architecture while improving governance and trust
•    The shift from tool-centric to platform-centric data strategies
•    What organizations gain, and risk, when consolidating their data ecosystem

•    How do you decide which data initiatives truly drive business impact?
•    What criteria help you prioritize data projects when resources are limited?
•    How do you measure whether a data initiative is actually moving the needle?
•    What common mistakes prevent data projects from delivering real business value?

Panelists:
Kyongche Kang, Analytics Branch Chief Data Scientist, UNITED STATES ARMY RESERVE
Smaran Mandala, Senior Director, Analytics and Innovation Partner, JLL
Lev Tyomkin, Vice President, Equity Research Data Strategy, JEFFERIES
Christopher Jones, Senior Director, Baseball Analytics, CHICAGO CUBS
Dhruv Baronia, Head of Analytics, NORTHERN TRUST

•    Who truly owns the data product lifecycle in your organization, and what breaks when ownership is unclear?
•    What’s the hardest leadership decision you’ve had to make when a data product wasn’t delivering value?
•    How do you balance speed to market with governance without killing innovation?
•    Should data products be killed like any other product, and who has the authority to pull the plug?

•    What’s the real cost of not unifying data, and who’s actually paying for it?
•    Is data unification a technology problem or a leadership problem in your organization?
•    At what point does data unification start driving growth, not just efficiency?
•    What’s the biggest myth organizations believe about achieving true data unification?

•    How can data & analytics automation tools and frameworks evolve to address future compliance challenges?

•    How is GenAI reshaping data fabric architectures?
•    What new metadata challenges does GenAI introduce?
•    How should governance models evolve for GenAI?
•    How can automation improve trust and data quality in GenAI systems?
•    What are the key lessons learned from real GenAI implementations?

•    If people matter more than platforms, why do most data transformations still start with tools?
•    What leadership behaviors most often undermine a data-driven culture, without realizing it?
•    How do you hold teams accountable for data outcomes, not just data delivery?
•    At what point does investing in technology stop delivering value if people aren’t truly enabled?


 

•    Why do most data transformations fail despite strong investment and executive support?
•    What leadership decisions make the biggest difference between failure and success?
•    What should leaders do differently in the first year to avoid common pitfalls?

•    Data quality and governance as the true scaling limits
•    Shared analytics and data models for agent collaboration
•    Measuring value across distributed intelligence
•    When data complexity erodes expected returns

•    How has GenAI changed the definition of “trusted data,” and are today’s governance models still fit for purpose?
•    Where are organizations most exposed today: data quality, security, privacy, or model misuse—and why?
•    How do you balance speed to innovate with GenAI against the risk of losing control over data and IP?
•    Who should ultimately own data trust in the GenAI era: IT, security, data leaders, or the business, and how is accountability enforced?

Panelists:
Veronica Butler, Director of Data and Program Evaluation, SUNSHINE ENTERPRISES
Vinod Surasani, Sr. Software Engineer, RBC
Shuen Mei, VP, Director of Software and Data Engineering, HARBOR CAPITAL ADVISORS, INC

•    Data enables new revenue streams beyond traditional products
•    High-quality data drives smarter, more profitable decisions
•    Data-driven products scale with minimal incremental cost
•    Strong data capabilities create sustainable competitive advantage

•    Where are AI and GenAI truly improving the customer experience, not just automating it?
•    How do you balance personalization with trust and data privacy in AI-driven customer journeys?
•    What customer insights are only possible today because of GenAI?
•    How do you measure real customer-centric impact from AI initiatives beyond efficiency gains?

•    How agentic AI is moving beyond rule-based systems to make dynamic, context-aware decisions — transforming how organizations trade, manage risk, and operate.
•    Why the future isn’t just about smarter models, but networks of AI agents working together to forecast, adapt, and respond in real time.
•    How quantum breakthroughs are unlocking unprecedented speed and complexity in areas like optimization, portfolio management, and compliance.
•    What leaders must do today to leverage agentic intelligence and quantum technologies to stay ahead in an increasingly autonomous and unpredictable landscape.

 

•    Shift from roles to skills
•    AI as a productivity multiplier
•    Continuous upskilling advantage
•    AI skills as the new talent currency

•    Analytics helps identify weak signals before disruption becomes visible
•    Predictive insights shift organizations from reactive to proactive decisions
•    Scenario modeling improves strategic resilience under uncertainty
•    Data-driven foresight accelerates response and innovation

•    How do you ensure data strategy is tightly aligned with business priorities?
•    What frameworks or criteria help prioritize data initiatives by business impact?
•    How do you balance quick wins with long-term strategic data investments?
•    What role should business leaders play in prioritizing data projects?

Panelists:
Robert Cooper, Director, Analytics, CHICAGOATLANTIC
Latha Ramamoorthy, Vice President - Technical Product/Program Manager, JPMORGAN CHASE
Menaka Nagarajan, Vice President Business Analytics, HEALTH NETWORK ONE
Jenny Pidej, Sr. Data Product Manager, SEPHORA
Ravi Salavi, VP, Data Analytics Strategy & Capabilities, TRUSTAGE

•    Aligning Data Strategy with AI Goals: Ensuring data architecture, governance, and quality are designed to support scalable AI initiatives.
•    From Data Lakes to Intelligent Data Platforms: How modern data infrastructures enable faster insights and more effective AI deployment.
•    Future-Proofing Data for AI Innovation: Preparing organizations to leverage emerging AI capabilities through stronger data management and integration.

 

•    What risks do companies face if they delay AI adoption?
•    Can you share examples of AI improving decision-making or agility?
•    How can organizations overcome resistance to AI initiatives?
•    What’s the best way to address AI skill gaps in teams?
•    How can departments collaborate to make AI a business priority

Panelists:
Madhu Mukherjee, Vice President of Transformation & Data Science, Gannett| USA TODAY NETWORK
Daniel Chertok, Sr. Data Scientist, AI Research and Commercialization, BMO U.S.

•    At what point do AI agents stop being tools and start acting as true teammates in D&A teams?
•    How must data and analytics operating models change to effectively integrate AI agents at scale?
•    What new skills and roles become critical when humans and AI agents work side by side?
•    How do teams maintain trust, accountability, and data governance when AI agents take on more autonomy?
•    What early wins should organizations target to prove the value of AI agents in D&A?

•    Why classical analytics is hitting its limits—and where quantum can break through
•    How AI and quantum together accelerate modeling, optimization, and insight generation
•    What’s real today versus what’s still experimental in quantum-driven analytics
•    Preparing data, skills, and strategy for a quantum-enabled future

•    How future-focused CDAOs are aligning data strategy with long-term business goals, customer-centric innovation, and emerging technologies like GenAI
•    Building agile data organizations that can move fast—balancing governance with experimentation, and enabling real-time, high-impact decisions
•    Translating data investments into tangible business outcomes by embedding analytics into core operations, managing risk, and demonstrating value to the C-suite

•    Shifting from experimentation to measurable business impact
•    Prioritizing use cases that drive revenue, efficiency, or risk reduction
•    Establishing clear KPIs to connect AI initiatives with strategic goals