CDAO Singapore schedule

During this 5-minute networking session, the aim of the game is to go and meet three people you don't already know. Have fun!

• How leaders can drive organisational alignment to treat data as a strategic asset, not an afterthought
• Building trusted, accurate, and well-governed datasets that form the foundation for advanced analytics and AI
• Preparing infrastructure, talent, and processes to leverage emerging AI capabilities at scale

Here’s the reality: Most organisations are not data-poor… they are reuse-poor.

You are already paying for your data. Multiple times.

Every time a team rebuilds a pipeline…
every time a metric gets redefined…
every time a dataset is recreated…
you pay again.

And that’s the hidden tax that prevents data from scaling.

Esther Lim talks about how leading organisations are breaking that cycle and turning data into something that compounds in value… instead of resetting every time.

• Exploring how leaders can foster creativity while maintaining control and oversight
• Sharing strategies to align innovation with governance and risk frameworks
• Highlighting real-world examples of balancing agility with compliance

Moderator

Shanmuga Sunthar Muniandy Director of Architecture & Chief Evangelist, APAC Denodo

Panellists

Chen Huaxing Head, Group Wholesale Data Analytics UOB

Rahul Patil ED, Head of Data Governance Office Sumitomo Mitsui Banking Corporation

Gek Peng Tay Chief Data Officer Republic of Singapore Air Force

Budiman Bun Head of Services ABeam Analytics

• How agentic AI can automate entire business processes end-to-end, beyond isolated use cases
• Helping business leaders understand what AI can (and can’t) do to ensure relevant, high-impact applications
• Balancing rapid AI innovation with governance, ethics, and measurable business value

Moderator

Tarun Samtani Ex-IAPP, Advisory Board Member / Director – Digital Governance

Panellists

Tahir Zafar International Head of AI Strategy Nomura

Yitong Chen VP Data Scientist & Analytics Validus

Varun Sharma APAC Head of Data & Analytics The HEINEKEN Company

• Explores how the combination of AI and reliable data leads to remarkable business success
• Discover how advancements in master data management and AI governance can shape your future and prepare you for what’s next.

• Preparing teams to adapt and excel in fast-changing, high-scale environments
• Fostering a culture that embraces innovation, agility, and continuous learning
• Aligning people strategies with organisational growth and transformation goals

Speakers

Cher Jing Ting Head of Data Science SP Group

Shobana Ravisankar Director, Data & Analytics PSA International

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.

As organizations accelerate into an agentic future, governed and reusable data products are what ensure AI delivers consistent value built on trusted and accurate data. As part of this session we will cover:

• An industry blueprint for an autonomous enterprise and anatomy of data products
• 5 steps to building trusted and re-usable data products for AI Agents
• How modern master data solutions built using data products overcome challenges faced by traditional data management
• How organisations embed decision confidence in agentic solutions, built on highly connected master data

In this session, Quantexa will share industry examples around how leading FS organisations are grounding their AI using contextual knowledge layer delivering business outcomes across customer protection, operational efficiencies and growth.

• Embedding governance at every stage of the AI lifecycle to ensure trust and compliance
• Defining clear policies, accountability, and ethical guardrails for AI initiatives
• Aligning AI governance with business objectives to drive responsible and measurable impact

• Role of Automated Data Integrity testing in implementing an effective data governance program
• How to use Data Integrity testing to validate and enforce your data governance strategy
• How to measure data quality, including the checkpoints and milestones that can help you track your progress over time

• 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

A legal ops chatbot worked brilliantly in POC. But in production, the data became massive and messy: relevance and quality were unclear, sensitive information couldn’t easily be filtered out, and there was no metadata to point AI toward the right answers. See how adding a data context and curation layer before ingestion cuts data prep from months to days, lowers compliance risk, and improves AI accuracy.

• Showcasing how robust data foundations speed up AI adoption and innovation
• Highlighting case studies where data readiness drove measurable business outcomes
• Translating strong data practices into faster, more confident decision-making

Artificial Intelligence is entering its most transformative phase yet. The evolution from AI to Generative AI and now Agentic AI is redefining how organisations sense, decide, and act. What began as automation and insight generation is rapidly progressing toward autonomous, context-aware systems capable of anticipating change and executing intelligently. This session explores how businesses across industries are harnessing this shift — using trusted information ecosystems, real-time news context, and advances in context engineering to power smarter, more connected decisions. From detecting early risk signals in finance and supply chains to surfacing personalised opportunities in sales, marketing, and operations, we’ll showcase real-world case studies of how AI is being embedded into workflows that enhance resilience, agility, and competitive edge.

• Analyzing object shape data is a challenge across many disciplines, including computer vision and biology
• Typically, the analytical method needs to be fast, accurate, lightweight, and scalable while remaining invariant to position and rotation of the object
• This session talks about our shape embedding and its performances in various benchmark tests

Most enterprises expect AI to drive growth and efficiency, but few are seeing meaningful results. This session explores how to turn agentic AI from scattered experiments into a systematic operational advantage. Learn how to connect AI to the full context of your business—data, people, and processes—so every employee can work faster, smarter, and differently. Walk away with a practical blueprint for deploying secure AI assistants and business agents that deliver measurable impact across departments.

• How organisations can enable experimentation and innovation without compromising trust, compliance, or data quality
• Where governance should act as an accelerator — not a blocker — to analytics and AI initiatives
• Practical approaches to embed guardrails that support speed, accountability, and measurable value

Speakers

Cher Jing Ting Head of Data Science SP Group

Sankara Prasad Kondareddy Head of AI & Data Science Amplify Health

• Transforming legacy data environments to unlock enterprise-wide intelligence
• Maximising ROI by optimising performance, cost, and security across modern data foundations
• Building future-ready data platforms that accelerate AI adoption and business transformation

Speakers

Wouter van Groenestijn Data & Analytics Lead, APAC Cargill

Dinesh Ramchandani Director, Data & Analytics Unilever

Presentation of She Loves Data’s latest report on women’s participation in technology sector.

In a world overflowing with data, maybe the real challenge isn’t analytics itself, but how we see it. What if the clues to better analytics lie in how we live, decide, and make sense of life?

Life is an optimisation problem — every choice mirrors an analytical frame.

Like connecting scenes in a movie, analytics is about stitching data and context to understand experiences, not just events.

It’s all about attribution — and the question is, can we do a better job at it?

In 2025, the challenge was implementation; in 2026, the challenge is Industrialisation. As organisations move toward an agent-first business model, the C-Suite is tasked with balancing the need for rapid innovation with the necessity of absolute governance and economic transparency.

• Assigning accountability for delivering business outcomes from data investments
• Bridging the gap between data leadership and financial leadership
• Should value realisation sit with business units, not central teams?

Moderator

Ban Horng Tan Head of Data Platform Solutions OCBC Bank

Panellists

Soumyajit Sarcar Head of Data & Analytics Product Development, Chief Data Office Nomura

Manish Kumar Head of Data Manulife

• Identifying how poor data quality drives hidden costs across operations, compliance, and customer experience
• Demonstrating the ROI of data readiness through reduced errors, faster decision-making, and stronger AI outcomes
• Building executive buy-in by linking data quality investments directly to business growth and risk reduction

Moderator

Shobana Ravisankar Director, Data & Analytics PSA International

Panellists

Monica Srinivasan ED, Data Conduct - Data Quality Standard Chartered

Rakesh Menon Data Evangelist - Enterprise Efficiency Maybank

Shawn McAllister Chief AI Strategy Officer Solace

• Tackling data consistency and governance challenges across multi-cloud, hybrid, and edge environments
• Ensuring accessibility and performance without compromising security or compliance
• Applying best practices to integrate, monitor, and optimise data flows across distributed architectures 

With AI accelerating rapidly, analytics remains the backbone of trusted decision-making. This session unpacks how teams can safeguard data accuracy, generate deeper insights, and evolve their capabilities to make high-quality data accessible for AI-driven use cases.

• Understanding the risks and inefficiencies created by fragmented and siloed data environments
• Exploring strategies and technologies to unify, integrate, and govern data across business units and systems
• Turning fragmented data into a strategic asset that drives accuracy, accessibility, and actionable insights

• Exploring operational and financial challenges of scaling data platforms
• Sharing practical approaches to optimise efficiency, reduce waste, and manage risk
• Highlighting lessons learned from organisations navigating rapid growth

Speakers

Hou Kiat Chao Deputy Group Head, Digital & AI Office SBS Transit Rail

John Burns Director, Data & Analytics APAC Yara International

• Plain Language to data questions: Business users ask questions in plain English, and the system translates them into the right data questions (SQL) without needing them to know tables, joins, or metrics definitions.
• Trust built-in: Metric definitions, accuracy and guardrails are embedded to keep outputs reliable and compliant.
• Real impact: Faster decision cycles, less back-and-forth with analysts, and higher adoption of self-serve analytics across teams.

• Exploring the concept of monetising data and analytics directly
• Lessons from organisations that generate revenue from their data
• Is this a future every enterprise can realistically pursue?

• Enabling experimentation and rapid insights while maintaining compliance
• Implementing frameworks that balance creativity, privacy, and regulatory requirements
• Translating responsible data practices into sustainable business advantage

• Scaling reusable, modular data assets to accelerate business initiatives
• Driving agility and faster insights through well-structured data products
• Aligning data strategies with organisational goals to unlock measurable innovation

• Turning organisational values into concrete, ethical data management practices that build trust
• Embedding transparency, fairness, and accountability into every stage of data governance
• Ensuring responsible data practices drive both credibility and long-term impact

Presentation of She Loves Data’s latest report on women’s participation in technology sector.