CDAO Melbourne schedule

An AI agent can be confident, detailed, and completely wrong, not because the model is poor, but because it never had access to the authoritative source it needed. In this keynote, InfoTrack's Ajay Kumar makes the case that the biggest unlock for trustworthy agentic AI is not a better model. It is authoritative data, made accessible. Drawing on InfoTrack's 25 years connecting organisations to trusted government and regulatory data, Ajay will show how the Model Context Protocol (MCP) turns that data into a governed, auditable service any agent can call on, and what this means for sectors well beyond InfoTrack's legal industry roots, including insurance, healthcare, aged care, and financial services.

• 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  
Suresh Bhaskaran Head of Data & Analytics Asahi Beverages

97% of organisations say they're delivering data products, yet 74% are doing so through unstructured, one-off efforts quietly rebuilding the same datasets over and over, often without realising it.

The result: slower forecasting, ballooning maintenance costs, and AI initiatives stuck permanently in pilot.

The real barrier isn't building more data products, it's confidence. Teams don't reuse what they don't trust, no matter how available it is. Drawing on new research by Quest and Corinium, we'll unpack what actually makes a data product reusable, why most reuse initiatives stall, and how leading organisations are turning trusted, governed data into a genuine competitive advantage.

• 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?
  
  

  Moderator
  

  Durjoy Patranabish VP and Head of Global Business Tiger Analytics

  Speakers
  Jane McCarthy Head of Data & Insights Flybuys
  Behrooz Hassani-Mahmooei Chief Analytics Officer Monash University
  Irina Chinenova GM, Data & Insights REST
  Annie Yu Senior Manager, Data & Analytics (APAC) Mars 
  

Most organisations don't discover their data isn't AI-ready until it's too late. Join Joseph Sullivan as he cuts through the hype to share what it actually takes to build a foundation that works - exploring why trusted, governed data isn't just a technical requirement, but the difference between AI ambition and AI outcomes.

• Why governance without continuous verification is faith, not control — and where the gap opens
• How ANZ regulation (CPS 230, BCBS 239) and agentic AI have turned data quality into a board-level, evidence-based obligation
• A practical, technology-agnostic framework for continuous data integrity across the data lifecycle

• 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?

• Enterprise Automation Frameworks vs Yet Another Automation Tool – why is it pertinent for your success
• Productionising AI/Data Innovations at Scale – Bridging the Operational gap with Enterprise Automation
  - Collaborating with Ops teams
  - Democratising Technology Innovations
• Addressing the time-to-market conundrum with an Enterprise Automation Framework:
  - Bridging the Data SILO gaps
  - Innovate or Integrate – Addressing the integration development effort at scale

• Explore practical examples of how your organisation can turn data governance strategy into business value, delivering measurable impact and accelerating AI readiness.
• Case Study 1: Shifting from data teams being responsible for data management to leaders accountable for business outcomes
• Case Study 2: Scaling Data Governance for Impact

How Pepper Money Uses Observability to Make Data Governance Personalised, Active, and Effective

Speakers
Vincent Low Founder & CEO Synogize
Dean Nguyen CEO & Director, KADA AI
Zoran Karanfilovski Data Governance Lead Pepper Money

• What does a practical AI acceleration playbook look like?
• How do you close the gap between the data you have and the decisions your business needs?
• What does AI-native data function look like in action — and what does it unlock?

Speakers
Dheeraj Garg Global Head of Data, AI & Analytics Thryv
Vivek Kumar Tech Manager, Data Engineering Thryv 

• 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?

• 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?

• Multiply Engineering Leverage: Double team output without adding headcount by embedding context-aware AI teammates directly into existing data workflows.
• Proven Enterprise ROI: Achieve up to 98% faster pipeline builds and slash operational costs – backed by real-world outcomes at Merck, EDF, and Sophos (including $900K saved on legacy software).
• Governance-First Blueprint: Gain a practical 90-day framework to safely scale adoption, ensuring AI teammates strictly adhere to your org's governance, audit, and coding standards.

• 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

• As technology becomes central to every business initiative, how do organisations move beyond the traditional "business versus IT" mindset and create true partnership?
• Where should roles, responsibilities and accountability sit between business and technology teams to ensure projects deliver measurable business outcomes rather than simply successful implementations?
• With emerging technologies such as AI accelerating change, what operating models, governance structures and leadership approaches are needed to align business priorities with technology execution?

• 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?
  

  Moderator
  Subhrajit Banik Branch Head DAMA Melbourne

  Speakers
  Darshil Mehta Head of Member Data Management AustralianSuper
  Kaushik Lakshman Head of Data Science Xero
  Nea Dhillon Director, AI Technology & Delivery Victoria University
  Ian Evans Director: Data, Insights & AI Keypath Education 

• Why trusted, secure and governed data is essential for scaling AI across enterprise
• How unified data management and automated data lineage provide the transparency, governance and control needed for responsible AI
• A real-world enterprise case study demonstrating how trusted data foundations enabled secure, scalable AI and better business outcomes

• 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 Former Head of Release and Service Delivery ANZ
Speakers
Brad Ryan Chief AI Officer i-screen
Carly Bergmann Principal Data Scientist Suncorp
Kathryn Gulifa GM Data and Analytics HBF Health 

The semantic enterprise accelerates high-quality decision making by harnessing the entire data estate, which can be used along with industry ontologies to drive standardisation and federation. Ab Initio will demonstrate how the Finance Industry Business Ontology (FIBO) can be materialised into metadata and data using the Ab Initio semantic data platform, orchestrating business outcomes at scale and providing the trusted context for knowledge workers and AI agents to meet regulatory obligations and improve business outcomes.​

How IM Systems designed the future-state data architecture to address some of MARS Australia’s most complex packaging and product content challenges, and create a pathway to AI-enabled packaging artwork production.

The transition from traditional barcodes to GS1 Digital Link QR codes is transforming packaging from a physical asset into a digital customer engagement platform. Recognising this shift, MARS Australia partnered with IM Systems (IMS) to assess whether the re-implementation of its core packaging artwork and product content management platform would provide the strategic foundation required for connected packaging, AI-enabled content, and increasingly digital retailer and consumer experiences.

Rather than simply validating the technology being implemented, the engagement sought to answer a broader strategic question: would the new platform provide a sufficient foundation for the future, or would additional architectural change be required? To answer this, the project combined enterprise architecture, semantic modelling, data governance, and business capability analysis to develop a future-state enterprise architecture, packaging-centric conceptual data model, enhanced operating model, and strategic roadmap.

Presented jointly by MARS Australia and IMS, this session shares the practical lessons from designing a future-ready packaging and product content ecosystem, demonstrating how enterprise architecture and semantic modelling can bridge today's technology investments with tomorrow's digital business capabilities.

• 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?

Moderator
George Ibrahim Senior Manager Contract Quality and Performance Department of Transport & Planning

Speakers
Hamish Morgan Head of Data and AI Governance Treasury Wine Estates
Maruf Hossain Executive Advisor – Data & AI Boldyn Networks
Nikita Jauhari Divisional Data Management Steward NAB
Sandeep Shankar Head of Data and Analytics iSelect & Compare the Market 

• 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?

Moderator
Melroy Vanlangenberg Former Head of Governance Australian Unity
Speakers
Darshil Mehta Head of Member Data Management AustralianSuper
Irfan Khan Head of Data and Analytics Melbourne Airport
Asel Mendis Data & Analytics Lead - Network and Retail Analytics Kmart
Bhavisha Sharma Senior Data Delivery Lead GPC Asia Pacific