CDAO Melbourne schedule

For the first time, AI can interact with systems in a coordinated, task-driven and auditable manner. With MCP AI, the Model Context Protocol, organisations gain a universal gateway that enables secure robot-to-robot and agent-to-agent communication across the high-trust environments they rely on.

In this keynote, InfoTrack will demonstrate how you can eliminate friction from critical data sources and empower developers to build at velocity.

Learn how to access authoritative government data through an MCP gateway that unlocks entirely new possibilities for agentic AI workflows within commercial organisations, enabling processes that were previously too fragmented, too labour-intensive, or too constrained by legacy systems to automate effectively. Propel your AI agent to take on structured work with consistency, transparency, and scale.

The opportunity for CDAOs is clear, when intelligent agents can safely use trusted external data as part of a programmable workflow, entire categories of operational bottlenecks disappear, and new forms of competitive advantage emerge.

• How do you align your data strategy with overall business priorities?
• With so many technology options available, how do you decide which initiatives to prioritise first?
• What role does proof-of-concept or pilot experimentation play before full-scale implementation?
• How do you shift the organisation’s mindset from “chasing the latest tech” to making thoughtful, strategic data decisions?

Speakers
Craig Rowlands General Manager Enterprise Data & AI Australian Unity
Patrick McQuaid Group Head of Data, Analytics & AI NGM Group
Olga Vasilyeva Chief Data Officer Australian Skills Quality Authority (ASQA) 
John Antony Chief Data and Insights Officer Strategy EPA Victoria  

• What does “agentic AI” imply for organisations looking to move from automation to true autonomy?
• What foundational capabilities (data, governance, infrastructure) are required to support an autonomous workforce at scale?
• How should organisations balance autonomy with control, accountability, and risk in AI-driven decision-making?

• Why do so many data initiatives fail to deliver value, and where do teams get stuck?
• How do you turn individual talent into a cohesive team capable of delivering under pressure?
• What frameworks, practices, or mindsets help teams move beyond pilots into operational impact?
  
  Speakers
  Jane McCarthy Head of Data & Insights Flybuys
  Ben Pattison CDAO Flight Centre Travel Group
  Behrooz Hassani-Mahmooei Chief Analytics Officer Monash University 

• What does it truly mean to treat context as a data asset, not just raw information?
• How can organisations unify and enrich data to create a real-time, contextual view of customers and operations?
• What role does trusted, well-governed context play in powering effective AI and personalisation at scale?

• How can CDAOs scale AI responsibly while ensuring governance and compliance keep pace?
• What strategies build trust across the business, so AI adoption is embraced, not resisted?
• How do leaders balance the urgency for AI-driven growth with the risks of misalignment or misuse? 

• How can organisations ensure data integrity across complex, interconnected systems whilst maintaining speed and agility in their data processes?
• What role does automated data validation and testing play in strengthening data governance frameworks and reducing risk?
• How can business and IT teams collaborate effectively to embed data governance practices without slowing down innovation or delivery?

• How should organisations design data architectures that remain flexible while supporting long-term scale and innovation?
• What governance frameworks ensure trust, quality, and compliance without slowing down data and AI initiatives?
• How can data leaders balance platform scalability with risk management, security, and regulatory obligations?

• How can data and AI leaders clearly demonstrate business value in terms executives care about?
• What frameworks or metrics best connect data and AI initiatives to measurable outcomes?
• How do you balance long-term capability building with the pressure to deliver immediate results? 

• Explore the stages of data governance maturity and how to assess your organisation’s current state
• Learn practical strategies and frameworks for advancing governance without slowing innovation
• Discover best practices from leading organisations to embed governance into culture, processes, and technology 

• How can organisations architect AI workflows that balance innovation with robust data governance and compliance?
• What are the key building blocks for turning trusted enterprise data into actionable, AI-driven insights at scale?
• How can platforms like Snowflake enable seamless collaboration between data, analytics and AI teams whilst maintaining control and visibility?

• What strategies work best for modernising legacy systems without causing disruption to critical business operations?
• How do you prioritise which parts of the data stack to replace, refactor, or integrate with modern platforms?
• What role do cloud, APIs, and data virtualisation play in extending the value of legacy investments?

• How can APIs act as the control layer to standardise and scale AI across data ecosystems?
• What governance frameworks are needed to secure and manage AI-driven data usage effectively?
• How do we move from isolated AI models to orchestrated, production-grade AI systems using APIs?

• What role do advanced analytics capabilities play in building true organisational readiness for AI?
• How can organisations translate data maturity into practical, scalable AI outcomes?
• What gaps exist between traditional analytics and AI-driven decision-making, and how can they be effectively bridged?

• How can AI be applied to proactively monitor data quality across modern data pipelines in real time?
• What are the most effective approaches to detecting and responding to data drift before it impacts analytics or AI models?
• How can organisations operationalise data quality insights to build trust and reliability in their AI and analytics outputs?

• Why assembling top technical talent individually doesn’t automatically create a high-performing team
• How to foster collaboration, accountability, and proactive problem-solving in newly formed data teams
• Strategies for hiring, developing, and retaining talent to build cohesive teams that consistently deliver value

• What changes are required in your current data architecture to support scalable and reliable AI workloads?
• How can metadata management and lineage tracking help build trust and transparency in AI-driven decisions?
• What governance and pipeline strategies ensure AI doesn’t amplify existing data quality or compliance issues?

As data becomes the backbone of modern governance, leaders are rethinking how decisions are made. This session explores how AI and advanced analytics are enabling more strategic, transparent, and agile decision-making. Discover how data leaders are transforming operations, driving innovation, and building organizations equipped to thrive in a rapidly evolving digital landscape.

• How can organisations move from static, rule-based data quality approaches to intelligent, automated frameworks that scale with business needs?
• What role does AI and machine learning play in proactively identifying and resolving data quality issues before they impact operations?
• How can enterprises embed continuous data quality monitoring into their workflows without adding complexity or slowing down delivery?

• Which core principles of data storytelling remain essential for engaging and influencing stakeholders, and how are they evolving with new data technologies?
• How can data leaders translate complex data and analytics into clear, actionable narratives for diverse business audiences?
• How do you communicate uncertainty, nuances, and limitations in your data without oversimplifying or losing stakeholder trust?
  
  Speakers
  Darshil Mehta Head of Member Data Management AustralianSuper 
  
  
  

• How can organisations move beyond policy on paper to embed governance into the tools, processes, and platforms teams already use?

• What are effective strategies for federating governance responsibilities without losing oversight or consistency?
• How can automation, metadata, and cataloguing tools support real-time, scalable governance practices across the enterprise?

• How to operationalise responsible AI frameworks across the model lifecycle
• Strategies for managing bias, explainability, and accountability in production systems
• How to balance innovation speed with regulatory and ethical obligations
• Building organisational trust: aligning legal, risk, and data teams 

• How are organisations moving AI from proof-of-concept to operational deployment at scale?
• What practical frameworks or governance structures help AI initiatives deliver measurable business value?
• How can cross-functional teams collaborate effectively to embed AI into day-to-day operations?
• How do leaders measure and communicate the impact of AI once it’s integrated into processes and decision-making?

Moderator
Dave Vinluan Head of Release and Service Delivery ANZ
Speakers
Sparkal Sharma Head of Data and AI Carlisle Homes
Kamran Channa Chief AI & Innovation Officer Latrobe Health Services
Hemil Deshmukh Group Head of Data and AI Wilson
Manohar Esarapu Chief Information and Innovation Officer City of Port Phillip 

• What strategies help reposition data functions from support roles to strategic growth enablers?
• How can data leaders better communicate value to senior stakeholders who see data as a cost line?
• What real-world examples best illustrate how data investments have directly driven business outcomes? 

• What is the strategic role of data stewardship in managing risk and driving value?
• How can data stewardship be embedded effectively across the data management lifecycle?
• What qualities make a data steward truly effective?
• Which tools help data stewards succeed, and how should success be measured?

Speakers
Maruf Hossain Executive Advisor – Data & AI Boldyn Networks 

• How can data leaders reposition their teams from reactive service providers to strategic collaborators with the business?
• What mindset and structural shifts are needed to enable proactive value creation rather than ticket-based delivery?
• How do you measure and communicate the strategic contribution of your data team beyond dashboards and reports?

• What are the most meaningful metrics for demonstrating the business value of data, and how do they vary by stakeholder?
• How can you tie data KPIs to broader organisational goals like revenue growth, risk reduction, or customer experience?
• What common pitfalls do data teams face when trying to quantify impact, and how can they be avoided? 

• How can leaders foster a data-driven mindset across all levels of the organisation and embed data into everyday decision-making?
• What strategies work best to ensure adoption of new data practices and tools without creating resistance or siloed behaviours?
• How can cultural transformation be measured and linked to tangible business outcomes, beyond technology implementation?