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

In today's dynamic $100 billion AI landscape, organizations around the globe are refining their competitive edge. But for AI to be successful, programs must start with robust data governance initiatives. Companies who do this well, don't merely have the right tech capabilities, but rather foster a culture that puts data first — prioritizing quality and insights.

Join this session to explore:
•    Finding the right culture balance with data
•    Maximizing AI's impact with a successful data governance framework
•    Implementing quality controls to ensure the efficacy of your AI program

•    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 Wallinger, Director, Data, RLI Insurance
Madhu Mukherjee, Vice President of Transformation & Data Science, Gannett| USA TODAY NETWORK
Christopher Jones, Senior Director, Baseball Analytics, Chicago Cubs

•    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?

We've traditionally seen enterprise data and AI strategies as cost centers to optimize and manage operations. The profit-generating idea of selling - or monetizing - internal data assets externally can feel daunting, even dangerous.

Jessica Li Gebert and Henry Scherman of Neudata aim to tackle the misconceptions and fears that prevent enterprises from capitalising on their data assets. They will discuss successes and missteps in data sales, drawing on use cases that range from AI training data to investor intelligence.

•    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

The choices facing data leaders tasked to transform their data and analytics functions into levers of innovation and change are overwhelming.    Mountains of and technical and data debt, excessive market hype, and increasing pressure to show progress on AI are exacerbating the enormity of the task.  Overcoming these barriers is key to breaking the curse of excessively short CDO tenures.  

Data leaders who are implementing or revitalizing their data teams, and who seek an answer to the question of ‘where should I start?’ must:
1.    Revisit legacy assumptions, challenge the status quo
2.    Embrace an operating model focused on agility
3.    Prioritize progress over perfection; take a practical approach to GenAI
4.    Recognize that ‘all data is not created equally’
5.    Leverage an analytical style of master data management (MDM) to drive short-term value, and establish a strong data foundation

Come hear industry expert, author of the ‘Data Hero Playbook’, and the CDO of Profisee, Malcolm Hawker, as he shares valuable insights the practical steps data leaders must take to drive meaningful and lasting changes in their organizations.  Malcolm also shares his experience on how an analytical style of MDM is the perfect balance between short-term business value, AI-based innovation, and data foundations.

•    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, Vantive
Rajib N. Head, Head of Data Analytics, Harris Associates

•    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 is your organization using AI or analytics to strengthen business resilience?
•    What are the biggest challenges in moving from forecasting to real-time decision-making?
•    How do you balance speed and accuracy when responding to disruptions?
•    What role does data quality play in enabling effective, resilient 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 
Cynthia Pekron, Head of Data Collections Systems, Morningstar
Pedro Tavares, Lead Data Scientist, Glencore Canada
David Brown, Chief Data Officer, RJO'BRIEN 

•    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

•    Data Needs Context – Metrics alone don’t drive decisions; the story behind them does
•    Narrative Drives Impact – Use storytelling to make your insights relatable and memorable
•    From Insight to Action – Connect the numbers to clear next steps and real-world outcomes

•    Unlock AI's True Potential: Learn how to tailor powerful LLMs to your specific business needs, moving beyond generic applications to achieve precise, domain-specific intelligence.
•    Master Complex Data Relationships: Discover how graph databases provide unparalleled insights into interconnected data, revealing patterns that traditional methods miss.
•    Future-Proof Your Analytics Strategy: Understand the crucial synergy between finetuned LLMs, graph databases, and vector databases, equipping you with a holistic approach to next-generation data architecture.
•    Gain Actionable Insights: Walk away with practical knowledge on engineering steps, advantages, and real-world applications that you can immediately apply to your data challenges

•    What profile and skillsets do you truly need and how do you combine them to optimize performance? How are data analytics leaders dealing with the great resignation, or the great re-shuffle?
•    Identify, upskill and retain internal talent; how to create compelling career paths 
•    Tapping into collaboration networks – externally, for recruitment and project-based initiatives, and internally, to leverage mobility opportunities
•    Nurturing diverse internal talent that could be interested in moving to data analytics roles 

•    Bridging the Gap Between Vision and Action, the importance of aligning AI with business priorities and constraints
•    How to identify and prioritize AI use cases with the highest business value
•    Measuring Success and Sustaining Momentum
•    KPIs and metrics to demonstrate value and gain stakeholder buy-in

•    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
 

• 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