Data & AI Architecture Singapore schedule

In this 5-minute networking session, the goal is to connect with three new people. Let the questions on the screen spark your conversation. Enjoy the opportunity to expand your network!

In this session, Sachin will walk through his organisation’s end-to-end data strategy — from architecture to implementation — illustrated through real use cases and measurable success metrics.

• What “AI-ready” really means for enterprises in Japan, South Korea, and Singapore
• The rise of orchestration tools, feature stores, and MLOps platforms in supporting AI models
• Avoiding costly re-engineering when scaling from pilot AI projects to production

• Embedding governance into fast-moving, multi-cloud data ecosystems
• Balancing agility with compliance across diverse regulatory landscapes
• Leveraging automation to scale data quality and trust

Panellists

Dr. Yap Ghim-Eng Director of Data Engineering GovTech

Balaji Rajamani Director of Data Management Tetra Pak SEA

Carlo Bisagni Head of Data Foundation The HEINEKEN Company

• Evaluating the true business case: when speed really drives value
• Managing costs and complexity of real-time architectures at scale
• The AI factor: will intelligent systems reduce or accelerate the need for real-time insights?

Panellists

Subir Chandra Director of Engineering and Ops Temasek

Bhargava Krishna Sreepathi Director Data Science Syneos Health

Ian Lo Deputy Director, Data Science Singapore Tourism Board

• Balancing consistency, compliance, and agility in APAC’s evolving regulatory environment
• Lessons learned from adopting data mesh and federated governance models
• Hybrid strategies: combining central authority with domain-driven ownership

Large Language Models (LLMs) like ChatGPT and Gemini are reshaping how people in every field approach problem-solving, from brainstorming ideas to debugging complex systems. This talk will explain, in straightforward terms, how LLMs work, what they’re good at, and where their boundaries lie.

We’ll explore the principles of prompt engineering—how to frame questions and instructions to get clearer, more reliable results—and show practical ways to make LLMs a valuable part of everyday work. Attendees will leave with a grounded understanding of how to think about LLMs and concrete strategies for using them effectively in their own workflows.

• How e-commerce giants in Southeast Asia are moving from batch to real-time event streaming
• Technologies enabling instant recommendations, payments monitoring, and supply chain visibility
• Ensuring low latency in markets where infrastructure and bandwidth remain inconsistent

• How organisations are moving away from centralised data lakes towards modular, purpose-built architectures that streamline data flow and reduce latency
• Leveraging automation, metadata management, and cost-aware storage tiers to optimise data movement without compromising accessibility or performance
• Designing architectures that balance scalability, governance, and real-time processing to support modern AI and analytics workloads

• Key lessons from shifting from fragmented data systems to a federated, product-centric architecture
• How embedded lineage, ownership, and governance improved trust, security, and cross-domain collaboration
• The role of cloud-native platforms in enabling scalable, agile, and cost-efficient data delivery across the organisation

• Using GenAI to turn complex model behaviour into clear, human-friendly explanations
• Automating checks for bias, drift, and model risks through GenAI-driven analysis

• Adding interactive, conversational layers for real-time explainability beyond static dashboards

• Evolving from batch ETL to intelligent, event-driven pipelines that support real-time analytics, AI inference, and continuous data movement across diverse business domains
• Designing a governance-first warehouse architecture with embedded lineage, quality controls, access policies, and monitoring to ensure trust and regulatory readiness
• Enabling multi-cloud and hybrid flexibility by adopting modular, cloud-native warehousing components that scale efficiently while reducing dependency on monolithic platforms

As AI adoption accelerates, companies are taking on risk without fully understanding what they are securing. In 2025 alone, we saw attacks targeting model training data, analytics pipelines, API integrations, and even content rights. These incidents show a clear pattern. AI systems magnify weaknesses in the underlying data. They expose governance gaps. They turn small oversights into systemic vulnerabilities. This talk helps CTOs and business leaders see the full picture with clarity rather than hype. We will walk through the anatomy of recent incidents, explain why traditional data governance frameworks fall short in generative AI environments, and share a practical blueprint for building a governance layer that can withstand the next decade of AI.

• Will the industry converge on universal frameworks, or remain in constant evolution?
• Balancing stability for compliance with the need for innovation and adaptability
• Preparing talent, culture, and technology for the next decade of data-driven growth

Panellists

Jeremy Markson Head of Data & Insights Takeda

Desiree Chen VP, Enterprise Financial Planning – Data & Business Analytics SGX

Ishan Agrawal Head of Engineering, AI, Data, & Growth Airwallex