OVERVIEW

As organizations expand their use of data and AI, governance needs to evolve to encourage innovation while maintaining trust and compliance. This two-day masterclass equips you with practical frameworks, tools, and strategies to manage data and AI responsibly in cross-functional, self-service environments.

Led by global data expert Bhavika Unnadkat, the program combines strategic insights with hands-on exercises and real-world case studies. You’ll learn how to design governance models that balance autonomy and control, embed responsible AI practices, and strengthen data quality, security, and accountability.

By the end of the workshop, you’ll walk away with a clear roadmap and ready-to-use templates to advance governance maturity, minimize AI risks, and drive confident, data-driven decisions across your organization.

PROGRAM OBJECTIVE

• Cross-Functional Governance Design – Understand how to build governance models that balance autonomy and control across multiple domains.
• Self-Service Analytics Enablement – Equip teams to manage decentralized data access without sacrificing quality or compliance.
• AI Governance & Risk Management – Learn to assess, monitor, and mitigate ethical and regulatory risks across the AI lifecycle.
• Metadata & Data Quality Management – Develop strong foundations for data lineage, accuracy, and reliability at scale.
• Data Security & Privacy Controls – Apply zero-trust and access-control principles in cross-functional data ecosystems.
• Operating Model Implementation – Translate governance principles into actionable roles, workflows, and councils.
• Change & Adoption Leadership – Drive organizational buy-in for data and AI governance frameworks through stakeholder engagement and communication.
• Governance Road mapping & Metrics – Build and measure a 90-day action plan for sustainable governance improvement.

KEY TAKEAWAY

• Understand cross-functional governance principles & how they support self-service analytics.
• Learn how to govern AI responsibly across its lifecycle.
• Collaborate through breakout sessions and case studies to design governance frameworks, address AI risks, and apply practical solutions in real scenarios.
• Leave with a personalized roadmap to strengthen governance and manage AI risks effectively.
• Explore tools and templates for metadata, data quality, and data security.
  

OUR DISTINGUISHED TRAINER

Bhavika Unnadkat
Award-Winning Data & AI Governance Leader
Currently leading Data and AI Enablement at a top Australian energy company

Bhavika Unnadkat is an award-winning executive and global leader in data, governance, and artificial intelligence transformation. She has been recognised as one of the Global Top 100 Innovators in Data and Analytics in 2022 and 2024, and was named one of Chief Data Officer Magazine US’s “40 Under 40 Data Leaders” in 2024. With more than 15 years of leadership experience across the energy, utilities, and enterprise sectors, she is known for turning complex data strategies into clear business results through ethical, sustainable, and insight-driven approaches.

Currently, Bhavika is Head of Data and AI Enablement at EnergyAustralia, where she leads organisation-wide programs focused on responsible AI, strong governance, and better decision-making through data. Previously, she was Head of Data Strategy, Analytics and Governance at Momentum Energy, where she built and delivered an enterprise data strategy that improved governance, compliance, accessibility, and performance. She also led data and information management at Citywide Service Solutions, modernising systems and strengthening transparency and operational efficiency.

Outside her executive role, Bhavika serves as Chairperson of the Women’s Association South East Melbourne and is a Board Member of the Australian Parents Council, advocating for inclusion, digital empowerment, and informed communities. A frequent speaker and mentor, she shares practical insights on data strategy, AI ethics, and change leadership, helping organisations navigate complexity and unlock real value from data.