CDAO Chicago schedule

In this provocative keynote, we’ll unpack what it really takes to turn data into value. Through four bold truths, you’ll get a front-row seat to the uncomfortable – and necessary – shifts D&A leaders must make to be successful:
•    You’re in a toxic relationship…with data.
•    Spoiler Alert: Data isn’t the most important part of your data strategy.
•    Ready or not, AI is coming for you.
•    It’s not the data. It’s you. 

If you’re ready to stop blaming technology and start leading the change, this is where it begins.

•    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

•    How AI-driven observability frameworks can enhance trust, quality, and explainability in enterprise analytics pipelines
•    Scaling Responsible AI Through Data Products and Federated Governance
•    Solving for enterprise challenges - legacy systems, skill gaps, and fragmented data

•    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

•    Who is using self-service analytics automation as a strategy, and is it helping or hurting business decisioning?
•    Has centralizing data and analytics given the illusion of control - or is it a single point of failure?  
•    What role does Agentic AI workflows and automation play in improving data and analytics governance?
•    How can data & analytics automation tools and 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

Moderator: Maggie Hays, Founding Product Manager, DataHub

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

The financial services industry is facing mounting pressure to innovate—while simultaneously managing risk, ensuring regulatory compliance, and meeting rising customer expectations. At the heart of this transformation lies one critical asset: trusted data.
Yet many organizations are held back by outdated systems, fragmented data environments, and manual processes.
Join this interactive roundtable to explore how financial institutions can accelerate their digital transformation by building a foundation of clean, governed, and actionable data. 
We’ll share practical strategies for:
•    Automating data quality and governance
•    Mastering customer and transaction data
•    Meeting regulatory requirements with confidence
•    Enabling innovation through a unified, AI-powered platform

Led by Larry Hunt, Field CDO at Ataccama, this session is designed for data leaders who are ready to modernize their data landscape, reduce risk, and fuel innovation with trusted, enterprise-grade data.

•    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

In an era where data is a strategic asset, organizations must evolve beyond traditional analytics to remain competitive. This session explores how adopting a platform mindset—one that emphasizes scalability, collaboration, and innovation—can unlock the transformative power of AI across the enterprise.
Join Michael Umlauf, Senior Vice President, Data Science & Analytics at TransUnion, as he shares insights from his experience leading large-scale data transformations and building AI-powered platforms that drive real business value.
Key takeaways from this session will include:
•    Solving complex data challenges with a unified, scalable platform that balances governance and agility
•    Enabling AI-powered collaboration across business and technical teams to accelerate innovation
•    Real-world applications of AI and machine learning in modern data management and decision-making
•    Building resilient, future-ready data ecosystems that support continuous learning and adaptability
•    Lessons from TransUnion’s journey in adopting a platform-first approach to AI transformation
Discover how a shift in mindset—paired with the right technology and leadership can be the game-changer your organization needs to lead in the age of AI.

•    Data as a Moat: Unbreachable defenses that keep competitors at bay and investors engaged
•    Novel Data Analytics in the Age of AI: Fuse public, private & behavioral streams into exponential insights (1 + 1 = 3)
•    Data to Power the User/Customer Experience: Supercharge every user journey with real-time signals that delight and retain
•    Ethics & Privacy: Innovate boldly with bias-proof, compliance-by-design safeguards

•    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?
•    How can organizations build a culture that embraces AI-driven insights to improve agility and resilience across all levels of the business?

Panelists:
Michael Umlauf, Senior Vice President, Data Science & Analytics, TransUnion
Kalpana Yendluri, Director, Emerging Technologies, Great Lakes Water Authority
Vipin Kataria, Senior Lead Architect (Data/ML), Picarro Inc 
Muniba Talha, Associate Professor Data and ML │Founder Women in Data Science AI and Machine Learning, KEA
Austin Dreyer, Head Data Scientist, Good Sam 

Moderator: Farid Sheikhi, Senior Manager, Analytics Innovation, RBCx


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•    What are the key challenges organizations face when preparing their data for AI initiatives, and how can they overcome them? 
•    How important is data quality and governance in ensuring successful AI outcomes, and what best practices would you recommend? 
•    Can you share effective strategies for integrating disparate data sources to create a unified and AI-ready dataset? 
•    How do you balance the need for large volumes of data with privacy and compliance concerns during AI data preparation? 

Panelists:
Patrick Chew, VP AI & Data Science, AIT Worldwide Logistics 
Cynthia Pekron, Head of Data Collections Systems, Morningstar
Sumeet Bhatia, Senior Director, Data Strategy and Enablement, Sun Life Canada
Dimple Thakkar, Former Head of Data for AI and ML, JPMorgan Chase

Moderator: Anne Homsy, Director of Analysis, Morningstar

•    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 

•    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