CDAO Chicago schedule

•    Examine the current maturity of your data analytics capabilities
•    Articulate the end goal of a data-driven organization
•    How to implement the transformation roadmap with actionable phases

Come join me as I share how we have succeeded in evolving our financial data consumer’s relationship with data!
•    Cutting through the noise when setting a strategy
•    Staying on the critical path during execution
•    Evaluating impact: how to know whether you’re moving the needle
•    …and other corporate catchphrases

•    How to support the transition from decision support (charts and graphs) to decision automation (models)
•    Managing accountability for the Amplified Value and Amplified Risk of AI (AI Governance)
•    Scaling AI despite unprecedented operational complexity (AI Change Management)
•    How to embrace the CDO/CDAO responsibility as the Enterprise AI leader (AI Portfolio Management)

 

•   Develop strategies for assessing what ‘AI’ means to you and building a definition to meet your organizations needs 
•    How to gain executive buy-in for AI solutions without overpromising or overcomplicating 
•    Eliminate workforce misconception by communicating ‘optimization’ rather than ‘replacement’ 

Panelists:
Zack Jakaitis, Associate Director, Data Governance Lead, RTX
Randy Callender, Senior Data Scientist, AbbVie
Anil Saldanha, Chief Innovation Officer, Rush University Medical Center
Patrick Chew, Global Head Data Enablement, AIT Worldwide Logistics

Moderator: Colin Kennedy, Founder & COO, Shelf

 

•    How do you evaluate your organization's Gen AI maturity in terms of strategy readiness and adoption?
•     What specific factors did you consider when assessing the impact of Gen AI on your business productivity?
•    What are the primary challenges you face in advancing the use of Gen AI across different business functions?
•    Do you have a dedicated function or team responsible for driving Gen AI innovations and Proof of Concepts (PoCs) within your organization?
•    Have you identified any quick wins or low-hanging fruit for AI applications that can demonstrate immediate value?
•    What criteria do you use to select AI use cases for various business functions?
•    How do you address data privacy and security concerns in your Gen AI implementations
•    What strategies have you found effective in managing the change associated with Gen AI adoption?
•    How do you measure and track the success of your Gen AI initiatives?
•    What future trends in Gen AI do you believe organizations should prepare for?

•    Building a cutting-edge data organization that attracts, excites, develops and retains exceptional and diverse talent 
•    Generating sustainable value with industry-leading data products, insights and expertise 
•    Increasing efficiency by giving data product teams with end-to-end responsibility in a data mesh architecture 
•    What’s next? data-enabled innovations, and a world-class data stack to win customers’ hearts, minds and loyalty

•    How AI can Improve Decision-Making Processes Within Organizations, From Predictive Analytics to Real-Time Insights. 
•    How can organizations effectively quantify the business impact of their AI and analytics initiatives? 
•    This question encourages panelists to share methods and metrics for measuring success, such as ROI, cost savings, or increased customer satisfaction.
•    What are some common challenges organizations face when implementing AI solutions, and how can they overcome them? 
•    This invites discussion on practical obstacles, such as data quality, integration issues, or resistance to change, and strategies for addressing them.
•    What emerging AI trends should Chief Data and Analytics Officers be aware of, and how can they prepare their organizations for these advancements? 
•    This encourages panelists to share insights on the latest trends, such as generative AI, AI-powered automation, or AI in decision-making, and how organizations can stay ahead of the curve.

•    What problems within my org can I address NOW with AI that can show value to stakeholders?
•    My data isn’t perfect, but I don’t want to be left behind with AI adoption, what are my options?
•    Build or buy? What resources, tools, and funding do I have available to begin an AI project?
•    What governance frameworks do I need in place to make sure we are compliant and that we can deploy with confidence
 

•    CDAOs are under increased pressure to deliver business value and show an ROI from data and analytics investments. This CDAO panel discussion will explore: 
•    The importance of building strong and trusted business sponsorship. 
•    Why creating a data culture is a foundational requirement. 
•    Communicating and selling your ideas to business leaders. 
•    Starting small to provide business value. 

Panelists:
Patrick Chew, Global Head Data Enablement, AIT Worldwide Logistics 
Robin Czajka, VP of Data Solutions, HealthTrust 
Marta Magnuszewska, VP of Claims Analytics, Markel 
Alan Schuchner, Chief Analytics Officer, Chicago Atlantic Group
Rohit Sachdev, VP, Data Management, QBE North America

Moderator:
Ravindra Salavi, NA Practice Lead – Insurance, WNS Analytics

Navigating the complexities of AI integration requires a deep understanding of both its potential and its limitations. This session offers a candid exploration of AI's role in modern data strategies, emphasizing the importance of discerning genuine value from mere hype. Through real-world examples and insights from the frontlines, we'll explore how organizations can strategically harness AI to enhance business outcomes without falling victim to the allure of cutting-edge technology for its own sake. Attendees will learn to cultivate a pragmatic approach, ensuring that AI initiatives align with their organization's goals and data capabilities, leading to sustainable, impactful advancements.

    - Recognize the limitations of current AI technologies and avoid overestimating their capabilities in solving complex business problems.
    - Develop a critical approach to AI outputs, emphasizing the importance of domain knowledge and fact-checking to prevent misguided decisions.
    - Foster a culture of data literacy and collaboration to maximize the effectiveness of AI and automation in driving business value.
    - Build a balanced AI strategy that combines foundational preparation with agile implementation, avoiding the pitfalls of perpetual planning or hasty execution.

•    Start with the business problems and then identify a technology solution
•    Employ machine learning to drive strategic business value and augment business operations 
•    Actively engage stakeholders to maximize impact and warrant successful products

•    How can data governance create a collaborative environment where data professionals with diverse skill sets (technical, analytical, communication) can work together effectively to address challenges and opportunities?
•    What approaches or best practices have you found effective in balancing regulatory requirements while promoting innovation within your data team?
•    What strategies can the panel recommend for fostering a culture of data sharing and breaking down silos that may exist between different teams within the organization?
•    How can organizations measure the effectiveness of their data governance program?
•    How can data governance facilitate "data democratization," enabling broader use of data for insights without compromising security or data integrity?

Panelists:
Kalpana Yendluri,  Managing Director, Data & Analytics Environments, BMO Financial Group
Teja Ginjupalli, Chief Data Officer, Kelly
Arindam Mukherjee, Head Data Strategy & Enterprise BI, Lundbeck
Shawn Tumanov, Head of Data, Model and AI Governance, GEICO 
Priya Velamoor, Director, Data Stratgy and Governance, UScellular

Moderator:  Sashi Venkatesan, Head of Pharmacy and Healthcare Data, Walgreens 

•    How can governance enable and support AI/ML?
•    Using existing governance frameworks
•    Building sufficient data AI ethics
•    Becoming an advocate for AI/ML usage

•    Building AI infrastructure and training resources
•    Crafting multi-modal AI/ML tools and tutorials to enhance capabilities in analyzing complex data
•    Cultivating environments of collaboration and innovation

•    The Data Bottleneck: Challenges of Real-World Data for AI/GenAI
•    The Rise of Synthetic Data: Crafting Artificial Reality for AI
•    Unlocking the Potential: Applications of Synthetic Data in AI/GenAI Growth

•    Initial challenges and motivations for adopting Data Mesh
•    Steps taken to transition from traditional data architecture to Data Mesh
•    How Data Mesh has accelerated insight creation at Morningstar
•    Key lessons learned and best practices

•    What are the best methods for ensuring data is understood up and down your enterprise, and how best to articulate the value of relevant projects to senior leadership?
•    Aligning Data Analytics & AI Projects with Business Goals
•    Customizing Communication for Different Leadership Levels
•    Case Studies and Success Stories

Panelists:
Daniel Chertok, Sr. Data Scientist, Endeavor Health
Joel Li, Director, Data Science and Business Intelligence, Allstate Insurance
Marta Magnuszewska, VP of Claims Analytics, Markel
Timothy Riddle, Senior Director, Enterprise Analytics, Premier Inc 

Moderator: Mary Purk, Managing Director, AI and Analytics Research Center, The Wharton School 

 

•    Explore strategies to align CDOs' roles and responsibilities with AI initiatives, enabling them to drive data-driven decision-making across the organization
•    Create a robust data governance framework, establish data-driven culture and leverage emerging technologies to support CDOs in their transformational roles
•    Discover how to measure and communicate the value of CDOs' contributions, ensuring corporate support and investment in their roles to deliver successful data-driven transformation in the age of artificial intelligence

•    How to build trust in the data and deliver value
•    Avoiding paralysis by analysis
•    Becoming more hypothesis minded and understanding the "why"

• What tells you that something is going to actually get value in the end?
• How do disruptions happen?
• What are the keys to using AI successfully and getting value?
• The importance of considering factors such as scalability, integration, and ease of use
• Overcoming challenges and mitigating risks