CDAO Chicago schedule

•    Spearheading an effective data literacy initiative to drive tangible and rapid transformation  
•    Painting a vivid picture and conveying data's worth to non-data personnel, enhancing their involvement  
•    Establishing a comprehensive framework capable of conveying data to diverse audiences with varying levels of data proficiency   
•    Introducing user-friendly data analytics tools that don't require advanced technical skills 

•    How forward-thinking financial institutions are moving from AI strategy to measurable outcomes: what’s not, and what’s next
•    How breaking down data silos and implementing strong governance unlocks the full potential of AI-powered insights
•    Embedding compliance, ethical risk management, and transparency into every step of your AI journey

•    Exploring how to clearly define and align data strategy with overarching business goals to ensure data initiatives directly support organizational priorities and drive measurable outcomes
•    Developing methods for prioritizing data projects by assessing their potential impact on key business objectives
•    Highlighting the importance of collaboration between data teams and business leaders to ensure alignment, foster shared ownership of projects, and maximize the success of data-driven initiatives across the organization


 

•    What does "data productization" involve, and how does it transform traditional data management?
•    What key factors make a data product scalable and efficient in today’s fast-paced business environment?
•    What are the main challenges in productizing data, and what strategies can overcome them?
•    How does productizing data enhance decision-making and drive measurable business results

Panelists:
Jenny Pidej, Sr. Data Product Manager, Sephora 
Patrick Chew, VP AI & Data Science, AIT Worldwide Logistics 
Christine Wallinge, Director, Data, RLI Insurance

•    Developing  practical steps to move from strategic planning to successfully deploying data, analytics, and AI initiatives across the organization
•    Addressing common roadblocks such as data silos, governance, and scalability to ensure a seamless implementation
•    How to leverage AI and analytics to uncover opportunities, enhance decision-making, and achieve measurable business impact

•    What are the key components of a strong data governance framework?
•    How can organizations ensure data quality while maintaining compliance?
•    What role does automation play in improving data governance?
•    How can data governance frameworks evolve to address future compliance challenges?

•    Why now? A look at the upside of selling data: net-new revenue, stronger strategic clout and a sharper competitive edge
•    Get your house ready: Inside a global French manufacturer who broke data silos, cleaned data schemas and sped up approvals so monetization could even start
•    Clearly outline the risks and rewards: How a leading southern hemisphere retailer matched its regional data to real market demand and won over risk & legal teams. 
•    Protect the core: Inside a SaaS company that proved you can sell data while guarding customer trust, intellectual property and existing revenue streams

•    How can organizations integrate advanced technology to enhance customer experience?
•    What role does talent play in driving a data-driven customer journey?
•    How can data be leveraged to personalize customer interactions at scale?
•    What are the biggest challenges in designing a seamless, tech-powered customer experience

 

•    Five characteristics of a Data Driven Organization
•    AI Quotient: Measuring the success of data in the organization
•    Transforming organizational mindset and steps to build Data Driven Culture

•    What does it take to actually become a customer-centric organization? 
•    Shifting from product-driven to customer-driven: best practices to adapt the entire data ecosystem 
•    How to align your business practices, data analytics, and talent development, around delivering personalized relationships 
•    Balancing hyper-personalization with data privacy

•    What are the biggest data management challenges that slow down digital transformation, and how can they be overcome?
•    How can organizations balance data quality, governance, and scalability to accelerate digital initiatives?
•    What role do AI and automation play in enhancing data management for a seamless digital transformation?
•    How can businesses effectively measure the impact of their data management strategies on digital growth?

Panelists:
Shawn Tumanov, Head of Data, Model and AI Governance, GEICO
Daniel Chertok, Sr. Data Scientist, Endeavor Health
Randy Callender, Senior Data Scientist, AbbVie
Mary Basani, Director, Digital Product Delivery, Rotary International

•    How AI-driven observability frameworks can enhance trust, quality, and explainability in enterprise analytics pipelines
Drawing from real-world implementations, we’ll explore how organizations can embed trust into every layer of the analytics stack to power responsible and resilient decision-making.
•    Scaling Responsible AI Through Data Products and Federated Governance
Learn how the shift toward domain-owned data products and federated governance models can unlock more scalable, context-rich, and accountable analytics in large enterprises.
•    Solving for enterprise challenges - legacy systems, skill gaps, and fragmented data
See how layering human oversight, infrastructure signals, and AI logic creates sustainable AI transformation, especially in highly regulated environments.

•    What are the key factors that typically cause Generative AI projects to fail, and how can they be avoided?
•    How can organizations set achievable goals and measure success in Generative AI initiatives?
•    How critical is data quality and preparation in driving the success of Generative AI projects?
•    What steps can businesses take to ensure they have the right talent, resources, and infrastructure for Generative AI success?

•    How to approach digital transformation in order to remain competitive  
•    Discuss how your peers are optimizing investment in areas such as utilizing cloud technology, self-service capabilities, or social media marketing to better serve customers 
•    Discuss how your organization is staying up with the pace of technological advancement, especially in light of the ongoing shift in client expectations 
•    Uncover what other executives are taking into account while stepping up their efforts to transform their organizations digitally 

•    How can universities prepare the next generation of AI professionals and support the adaptation of the existing workforce?
•    What universities and industry can learn from each other to implement AI in alignment with their missions?
•    Building Successful Partnerships – Effective collaboration models between companies and universities to maximize AI’s impact in both sectors

•    How integrating your own data improves the accuracy and relevance of LLM outputs
•    Ensuring compliance and safeguarding sensitive internal data when working with LLMs
•    Tailoring language models to address specific organizational goals and use cases
•    Techniques to fine-tune LLMs for better decision-making and insights based on internal data

•    How to separate valuable data stories from irrelevant noise to focus on what truly matters?
•    Turning complex data into a compelling story that resonates with your audience
•    Using visuals to simplify data and enhance storytelling effectiveness
•    Ensuring your data narrative drives actionable insights and supports strategic decisions

Panel Discussion: Preparing Your Data for AI Success: Strategies and Best Practices
•    What is Enterprise AI and how do you define an enterprise AI asset? 
•    How do you think enterprise AI assets should help organizations D&A team to deliver business value faster?
•    What are your thoughts on standardization of tools and automation when it comes to deliver value faster in AI and Gen-AI domain? 
•    What do you think about Governance at enterprise level end to end. Starting from intake process, ETL, engineering, ML engineering and AI development?

Panelists:
Patrick Chew, VP AI & Data Science, AIT Worldwide Logistics 

•    Identifying High-Impact Opportunities for AI-Driven Value Creation 
•    Building a Robust AI Ecosystem for Sustainable Value Generation 
•    Quantifying and Communicating the ROI of AI-Driven Value

•    Diving into the methodologies behind synthetic data creation 
•    How synthetic data helps mitigate privacy concerns, reduce bias in models, and improve the quality and diversity of training datasets for AI systems?
•    Discussing the potential pitfalls of synthetic data, such as maintaining data authenticity, handling edge cases, and ensuring scalability in real-world applications

Keynote Presentation: Enhancing Large Language Models with Your Internal Data for Greater Insights
•    How integrating your own data improves the accuracy and relevance of LLM outputs
•    Ensuring compliance and safeguarding sensitive internal data when working with LLMs
•    Tailoring language models to address specific organizational goals and use cases
•    Techniques to fine-tune LLMs for better decision-making and insights based on internal data

•    Consumer & Supply Chain Data Intelligence: How companies are capturing, managing, and analyzing data to drive product development and go-to-market strategy
•    Ethics and Privacy: Navigating responsible data use in an increasingly AI-driven world
•    Structuring the Modern Data Stack: Best practices for organizing data intake, storage, and analytics to support scale and agility
•    Data as a Strategic Asset: What makes data defensible and investable in a market where open-source AI is redefining competitive advantage

•    Discovering best practices for ethical AI implementation, including responsible data collection, unbiased algorithm development, and transparent decision-making processes  
•    Understanding the importance of cross-sector collaboration and partnership-building to maximize the impact of AI for good