CDAO Dallas 2024 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?

• 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
• Scaling AI despite unprecedented operational complexity
• How to embrace the CDO/CDAO responsibility as the enterprise AI leader?

• Establishing levels of governance around data
• Exploiting automation to improve data quality, accuracy & operational efficiency
• What’s next? Dissecting where data governance is heading in the future & what this means for businesses

• 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?

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

• Initiatives for training existing employees in data and analytics skills
• Best practices for recruiting top talent in data and analytics and creating an environment that encourages retention
• Implementing training programs to help employees recognize and mitigate unconscious biases

• Integrating data from various sources to create a unified customer view
• Leveraging data to deliver personalized experiences to customers across different channels
• Applying predictive models to anticipate customer needs

• 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

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

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

• Empowering decision-makers with AI-driven insights for more informed and timely decisions
• Enhancing marketing campaigns with AI-powered tools for better targeting and engagement
• Leveraging AI to deliver personalized recommendations and experiences based on customer data

• How to evaluate the risks of your AI models & ensure data is used ethically
• Best practices for creating a responsible AI governance framework

• 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?

• How can AI 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? 
• What are some common challenges organizations face when implementing AI solutions, and how can they overcome them? 

• Explore the key skills needed to become a successful and influential data leader
• State-of-the-market analysis on why data leadership is a unique challenge
• How data leaders should understand the alignment of data with organizational goals translating data insights into actionable strategies