CDAO Chicago 2025
Presentations
Day 1
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.
Jeannie Furlan
VP, Financial Data Strategy - Mutual of Omaha
• 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
Anusha Dwivedula
Director of Product, Analytics - Morningstar
• 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
Arindam Mukherjee
Director Data Strategy & Management -
Ipsen
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.
Malcolm Hawker
Chief Data Officer - Profisee
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
Emma McGrattan
Chief Technology Officer - Actian
• 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
Jing Liu
Executive Director - Michigan Institute for Data and AI in Society
Day 2
• 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
• Understanding the importance of cross-sector collaboration and partnership-building to maximize the impact of AI for good
• Building a Robust AI Ecosystem for Sustainable Value Generation
• Quantifying and Communicating the ROI of AI-Driven Value
• 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
• 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
• 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
• 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
• 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