CDAO Perth schedule

During this 10-minute networking session, the aim of the game is to go and meet three people you don't already know. Have fun!

• Leveraging drone imagery, satellite data, and 200+ live weather stations to create unified data ecosystems.
• Challenges in aggregating fragmented environmental and agricultural datasets.
• The role of open data in enabling collaboration and accessibility (government-led initiatives.

• Lessons learned from implementing domain-oriented data ownership, product thinking, and federated governance across complex, distributed environments.
• How to empower individual teams while maintaining interoperability, quality, and security across the enterprise.
• What comes next—how emerging architectures (e.g. data fabric, AI-native platforms) and business needs are reshaping the future of distributed data innovation 

Despite growing investment in AI and analytics, many organisations still struggle with fragmented data ownership, inconsistent quality, and siloed systems that limit business impact. This session explores how data leaders can build trusted, scalable foundations that enable smarter decisions, stronger governance, and enterprise-wide AI readiness.

• Establish clear data ownership and enterprise-wide governance to create trusted, accountable data foundations.
• Improve data quality and interoperability across agencies, systems, and business functions.
• Build organisation-wide data literacy and stewardship to drive adoption and lasting cultural change.
• Scale self-service analytics and AI readiness with accessible, governed, and trusted data platforms.

Speakers:

Ivy Isaac
Manager - Governance, Analysis and Improvement
Fortescue

Aqif Mukhtar
Manager - Data Analytics
Road Safety Commission

Vaibhav Agrawal

AI Technology Advancement Committee

Australian Computer Society (ACS)
 

• 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.

Most AI initiatives don’t fail because of weak models — they fail because organisations can’t trust or activate the data those models rely on. As AI moves from experimentation to autonomy, fragmented data, static governance, and disconnected systems quietly become the real blockers to scale.

In this session, David Irecki (CTO, APJ, Boomi) explores the shift required to move beyond pilots and unlock measurable AI value. Drawing on real-world examples and emerging best practices, he will show how data readiness, integration, and governance must evolve to support agentic AI operating safely inside core business processes.

• Strengthen data maturity and trust by improving data quality, integrity, and cross-business transparency.
• Transform data teams into strategic business partners through better communication, stakeholder alignment, and operating models.
• Embed data into business decision-making by shifting from static reporting to actionable, insight-driven conversations.
• Expand platform value by integrating new data sources while aligning outputs to evolving business needs.

• Problem statement: Despite best efforts, true self-service remains elusive, hindered by governance bottlenecks, complex tooling, and lack of data literacy.
• What we’ll explore: The key considerations, challenges, and complexities that organizations face today.
• Key outcome: Forward looking strategies for democratizing data, empowering business teams to make confident, data driven decisions while reducing reliance on technical teams. 

Data leaders face constant pressure to move faster while avoiding costly missteps. Decisions to buy, build or partner shape not just delivery speed, but long‑term flexibility, risk and credibility. How do you make these calls in practice, separate substance from vendor noise, and structure partnerships that support evolving business needs?

Speakers:
Andy Maher
Manager – Digital & Data
IGO Ltd.

• Design access models that support agility.
• Balance privacy obligations with business demand.
• Automate controls through platforms and metadata.
• Reduce bottlenecks  caused by manual approvals. 
  

• Securing replication environments to prevent leakage, duplication risk, and shadow data.
• Building integration layers that preserve lineage, metadata, and access control across platforms.
• Safeguarding sensitive datasets in AI/ML workflows without limiting performance or agility.
• Aligning with governance frameworks to ensure ethical, compliant AI adoption from the start. 

• 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?
• Make What role do cloud, APIs, and data virtualisation play in extending the value of legacy investments?

• Align data mesh architectures with robust governance to enable trusted, scalable data.
• Deliver enterprise agentic AI frameworks with embedded guardrails and controls.
• Balance innovation with risk through practical governance and ethical AI practices.
• Drive adoption by managing cost, change, and measurable business outcomes. 

• Rebuild legacy data estates into a scalable, business-wide platform that enables analytics and AI at scale.
• Unify customer and operational data into a single view to drive actionable insights across teams.
• Apply sentiment analytics to uncover customer pain points and identify service improvement opportunities.
• Empower teams with AI-driven insights and coaching to enhance performance and customer outcomes.

• Identify where governance creates unnecessary friction.
• Redesign controls for modern data and AI use cases.
• Clarify ownership for risk and assurance decisions.
• Maintain trust while increasing pace.

• How can AI models be trained and tuned to identify subtle data quality issues and drift before they impact downstream use?
• What’s the right balance between automated detection and human oversight in managing data reliability?
• How do you integrate real-time monitoring into existing data pipelines without introducing latency or complexity?

• 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?

• Reframe organisational mindsets to embrace adaptive, AI‑first ways of working.
• Design scalable AI architectures that support rapid experimentation without chaos.
• Establish practical guardrails that balance innovation with responsible governance.
• Accelerate delivery timelines to keep pace with fast‑moving AI capabilities and market expectations to executive leadership.

Many Perth organisations have reached a practical hiring ceiling due to budget pressure and talent scarcity. The focus has shifted from adding people to changing how data teams operate and prioritise.

• Why does hiring top technical talent individually not guarantee
  a high-performing team?
• Evaluate where AI tools genuinely augment teams versus add complexity
• How do you foster collaboration and proactivity in newly formed data teams?
• What hiring and retention strategies help turn strong individuals into a cohesive
  unit that delivers value?

Speakers:
Douglas Robertson Director & Principal Consultant DR Analytics Recruitment 

Get ready to challenge assumptions, defend your stance, and shift perspectives.
In this high-energy, interactive debate panel, senior data leaders won’t just share insights. With live audience polling and daring prompts, this session invites attendees to vote, question, and engage in real-time. Expect lively discussions, surprising consensus, and a candid look at the questions shaping the future of data leadership in WA.

• 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?

Speakers:

Matthew Curnow
Head of Digital Strategy, Data Management & AI
Wood

Rod Pedero
Data & Analytics Manager – Strategy Enterprise Architecture & Data
Western Power