Data 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!

• Identifying the key challenges associated with managing multi-cloud environments.
• Exploring strategies for optimizing performance and security across hybrid architectures.
• Discussing best practices for data integration and management in cloud ecosystems.

Learn how to enhance efficiency and reduce costs using a scalable data architecture powered by intelligent and automated data management.  Discover strategies for aligning with emerging data ecosystems, transitioning from legacy systems, strengthening data governance and security, and developing AI-ready data architectures.

• Exploring how AI can enhance compliance through automated governance frameworks.
• Examining the balance between machine-driven oversight and human judgment.
• Discussing the role of AI in ensuring data privacy and ethical use.

Moderator

Ville Kulmala President DAMA Singapore

Panellist

Amit Saxena Head - Data and Regulatory IT Section Crédit Agricole

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

Dr. Yap Ghim-Eng Director of Data Engineering GovTech

The prosperity of GenAI applications has brought new challenges for modern data architectures. In this talk, we will present what a real-time data platform requires to ensure speed, security, scalability and availability with real-life examples. We will also introduce the key features in next-generation of real-time data platform -- Redis v8, which is released in April 2025.

• Exploring the potential for seamless integration between unified and distributed data systems.
• Identifying strategies for balancing centralized control with decentralized autonomy.
• Discussing the role of automation and orchestration in achieving harmony in hybrid clouds.

• Quantify how traditional approaches to RAG workflows create inefficiencies through unnecessary data movement, governance gaps, and infrastructure sprawl in hybrid and multi-cloud environments
• Demonstrate how Starburst's Trino-based engine provides a performant, single-point-of-access layer across distributed data sources while maintaining granular security controls
• Technical blueprint for executing RAG workflows natively in SQL - eliminating copies while meeting compliance requirements for sensitive data

Artificial Intelligence promises transformative insights and automation — but without high-quality, well-governed data, even the most advanced AI models fall short. This session explores how organizations can build the foundation for trusted AI by ensuring their data is accurate, consistent, and rich in context. From integration and cleansing to cataloguing, governance, and enrichment — we’ll walk through the essential steps in the Precisely Data Integrity Journey, showcasing how each stage contributes to making your data truly AI-ready. Whether you're building models, automating decisions, or scaling analytics, success begins with the right data.

This group discussion is aimed at unpacking the real-world implications of moving from centralized to decentralized data management through the data mesh approach. Participants will explore how empowering business domains with greater data ownership can enhance agility and value delivery, while also addressing the cultural, technical, and governance challenges that come with this transition.

Discussion Questions

1. What are the key drivers pushing organizations to consider a data mesh approach?
2. How does decentralizing data management impact data governance and security?
3. What cultural or organizational shifts are required to enable autonomous data ownership?
4. How can business units be equipped to take on data responsibilities without overwhelming them?
5. What role should central data teams play in a decentralized model?
6. How do you measure success in a data mesh implementation?
7. What technical enablers (e.g., platforms, tools, or architecture) are essential for supporting data mesh?
8. What common pitfalls have you seen or experienced in transitioning to a decentralized architecture?
9. How do you balance local autonomy with the need for organization-wide data standards and quality?
10. In what ways has or could data mesh help your organization deliver value faster or more efficiently?

This group discussion aims to explore how AI can be embedded into data architectures to streamline and scale data management. Participants will share practical insights on automation, deployment challenges, and successful use cases that highlight the value of AI in reducing operational overhead, improving data quality, and enabling real-time decision-making.

Discussion Questions

1. What areas of data management are most ripe for AI-powered automation in your organization?
2. How do AI-powered data architectures differ from traditional data management frameworks?
3. What are the key benefits your team expects or has experienced from implementing AI in data operations?
4. What challenges have you encountered when deploying AI-powered architectures at scale?
5. How do you ensure data quality and integrity when relying on AI to automate management processes?
6. What infrastructure is needed to support AI integration into existing data architecture?
7. How do you manage the risks of AI-driven decision-making in critical data processes?
8. Can you share a successful use case where AI significantly improved a data workflow or operation?
9. How do you build cross-functional alignment between data, IT, and business teams when adopting AI-driven architectures?
10. What are your future priorities or investment plans for AI in data management?

In the highly competitive age of digital transformation financial service organizations are facing accelerated urgency to improve their customer and employee experience while simultaneously reducing operating costs, and managing risk and compliance.

To meet these competing demands on their business, these organizations are racing to deploy deep learning to achieve a new competitive edge by optimizing their back office operations with intelligent document processing, personalizing their customer experience with cutting edge NLP models, and reducing fraud and risk using state-of-the-art deep learning.

AI is here and delivering new capabilities to help businesses solve large and complicated challenges. Join Bob Gaines to learn what that means for your business and how deep learning is helping organizations:

• Achieve higher compliance, faster and with lower costs • Dramatically improve Customer Experience • Reduce time to value from years to weeks

Sergio Rego is a customer engineer at SambaNova Systems where he helps clients deploy purpose-built, deep learning solutions in weeks rather than years. Sergio started his career in financial services, where he worked in strategy; active and index management; and product design and management. Sergio also served as a senior data scientist and team manager for a system integrator where he helped federal government agencies deploy ML and AI solutions.