I help boards trust their AI — by governing the data that feeds it.

Problem

Enterprise AI initiatives were outpacing the controls around them. Internal teams needed a repeatable way to assess inherent AI risk, define mitigations, and produce assurance evidence without slowing delivery.

Approach

• Designed an enterprise AI assurance framework aligned to DAMA, ISO 27001, and APRA CPS 234.
• Mapped regulatory obligations to AI lifecycle stages, controls, and accountability models in Collibra.
• Built repeatable AI assessment, AI inventory, and approved-use-case workflows.
• Coordinated third-party AI due diligence — technical docs, control environments, testing evidence, contract obligations.

Outcomes

• Improved regulator and internal-risk confidence through repeatable assurance artefacts.
• Enabled secure, auditable migrations for Australian Retirement Trust and Challenger.
• Reduced data and AI risk during legacy decommissioning and cloud migration.

Problem

Ziko was layering AI features onto cloud data without governance scaffolding. Data ownership, sensitivity, lineage, and access patterns were largely tribal knowledge — a regulator and customer trust risk as AI usage grew.

Approach

• Conducted AI and data governance assessment for the Azure migration.
• Established Data Owners, Stewards, governance forums, decision rights, and escalation paths.
• Implemented MS Purview classification, sensitivity labels, and data estate map.
• Aligned Purview policies with Azure security, access management, and audit requirements.

Outcomes

• Delivered a governed, AI-ready cloud platform with scalable compliance and security.
• Strengthened trust in AI-driven outputs through structured governance.
• Improved traceability of data inputs feeding AI models inside Ziko.

Problem

Long-running ETL was blocking same-day reporting; on-prem footprint was expensive; planned migration timelines were aggressive and audit-sensitive.

Approach

• Re-architected ETL with quality gates, lineage capture, and reusable patterns.
• Embedded governance into the Snowflake migration — access, classification, masking, and review cadence.
• Stood up risk-aware delivery rituals across onshore/offshore teams up to 23 developers.

Outcomes

• ETL processing time reduced from 8 hours to 28 minutes — same-day data availability.
• Snowflake migration completed in <25% of planned time, saving approximately $1.5M.
• Improved client confidence in AI-driven outputs through governance traceability.

Problem

Core banking migrations are unforgiving: data quality, secure handling, and audit traceability all need to land cleanly the first time, with members and regulators watching.

Approach

• Implemented data management policies aligned to regulatory and audit expectations.
• Built evidence packs and traceability supporting audit readiness.
• Introduced operating disciplines — punctuality, cadence, escalation paths.

Outcomes

• Digitisation and storage savings exceeded $300k.
• Operational discipline and punctuality lifted from 48.3% to 92.5% in three months.
• Built a collaborative, risk-aware culture across business and technology.

Problem

Selling a regulated wealth division means separating customer, investment, and insurance data across seven core applications under strict regulator and contractual deadlines — with zero tolerance for leakage between buyers.

Approach

• Planned, mapped, and resolved data issues across seven core applications.
• Designed BI and data architecture supporting the split and downstream compliance.
• Coordinated onshore/offshore teams and embedded risk management and governance.

Outcomes

• Successful Successor Fund Transfer (SFT) and Project EDISON delivery.
• Clean data separation across seven applications under tight, audited deadlines.
• Stakeholder engagement maintained throughout a politically sensitive divestment.

Problem

Each client had different risk frameworks, regulators, and AI maturity — but all needed structured governance to defend AI-driven outputs to internal audit, board, and external regulators.

Approach

• Mapped each client’s obligations to data assets, analytics outputs, and operational controls in Collibra.
• Delivered data quality remediation, metadata management, lineage, and secure ETL pipelines.
• Built audit-ready governance artefacts supporting data quality, AI usage, and risk controls.

Outcomes

• Improved client confidence in AI-driven solutions through compliance traceability.
• Embedded structure, accountability, and governance across teams of up to 23 developers.
• Built a reusable assurance pattern across predictive analytics, BI, and automation programs.

Problem

Reporting was unreliable, data standards were inconsistent across domains, and ETL pipelines were hard to monitor — limiting trust in enterprise reporting and downstream digital initiatives.

Approach

• Implemented structured data quality, metadata, and secure handling processes across business units.
• Improved ETL pipelines and monitoring for stable business reporting operations.
• Worked across stakeholders to support Toyota’s digital transformation.

Outcomes

• Enhanced governance maturity and consistent data standards across finance, sales, and customer domains.
• Stabilised business reporting and improved data transparency.
• Enabled cohesive, high-performing teams across onshore and offshore contributors.