CDAO Singapore schedule

During this 10-minute networking session, the aim of the game is to go and meet three people you don't already know. Use the questions on the screen to guide your conversation. Have fun!

• Exploring how leaders must take charge of integrating AI into their organisations, moving beyond IT departments to embed AI into strategic planning and decision-making
• Defining success metrics and fostering a culture that embraces AI-driven change to enhance operational efficiency and competitive advantage
• Highlighting the necessity for leaders to champion ethical AI practices and promote a culture of continuous learning and innovation

Join VP, Field Chief Technology Officer – Nick Dobbins as he 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.

• Exploring how AI and data democratisation are reshaping business operations by enabling real-time insights and enhancing operational efficiency
• Discussing how enterprises can measure the return on investment (ROI) from AI initiatives in conjunction with data democratisation efforts
• Examining the ethical implications of leveraging AI within a democratised data framework

Moderator

Sigrid Rouam Chief Data Officer & Head of Data Management, Data Analytics & Gen AI EFG Bank International

Panellists

David Duan Head of Data GoTo Group

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

Vijay Jairaj ED, Responsible AI & Data Ethics Standard Chartered

Durjoy Patranabish VP – Head of Global Business Tiger Analytics

• Discussing the need for seamless data integration to provide agentic AI with a holistic view of the organisation to enabling intelligent and autonomous decision-making
• Ensuring high-quality, clean, and bias-free data for agentic AI delivers accurate, ethical, and effective outcomes
• Examining the technological upgrades necessary to support agentic AI’s high computational and real-time data demands

• Building effective collaborations among stakeholders to enhance innovation and driving business growth.
• Navigating regulatory and compliance challenges while forming data partnerships, ensuring that data sharing aligns with legal frameworks and ethical standards.
• Maximising value through data integration to unlock new insights

Moderator

Rakesh Menon EVP – Group Head of Advanced Analytics Solution Maybank

Panellists

Shobana Ravisankar Director, Data & Analytics PSA BDP

Hou Kiat Chao VP, Asset Management Department (Digitalisation & Data Analytics) SBS Transit Rail

• Discussing how innovative analytics tools can transform raw data into actionable insights, enhancing customer understanding and engagement strategies.
• Identifying key metrics for success -
  defining relevant metrics that align with business goals
• Integrating feedback for continuous improvement
  
  

• Exploring techniques for converting raw data into meaningful insights that drive strategic decision-making within organisations
• Quantifying the impact of advanced analytics on business performance through key metrics and case studies
• Highlighting strategies for encouraging a culture that prioritises data-driven decision-making across all departments

• Discussing the importance of designing analytics tools that are intuitive and user-friendly, enabling easy access to insights for all users
• Exploring methods for gathering user feedback to continuously improve analytics tools and ensure they meet user needs effectively
• Highlighting strategies for empowering users with self-service capabilities, allowing them to explore data independently and make informed decisions

AI has the potential to transform businesses, but its success relies on one critical factor—trusted, high-quality data. Despite the excitement around AI, only 4% of businesses believe their data is AI-ready. Without accurate, complete, and well-governed data, AI can produce biased, unreliable, and even damaging outcomes. In this session, we’ll explore:

• The risks of AI failures caused by poor data quality and governance
• Essential strategies for improving data integrity to enable AI success
• Practical solutions for integrating, enriching, and governing data to maximize AI value

• Encouraging experimentation and learning
  without fear of failure.
• Providing training and resources to enhance the team’s data literacy and analytical skills
• Aligning data innovation with business goals to maximise impact and drive growth.

Since the 1960s, the bulk of investments in enterprise IT have been focused on processing structured, transactional data. As a result, we now expect 24x7 reliability and straight-through processing from transactional systems. But while we were busy mastering the art of transaction processing, 90% of enterprise data has always been held in unstructured formats: documents: files and images. Until recently, most businesses had no ability to leverage this unstructured data for strategic business advantage. But now, Generative-AI has sparked a data revolution. Gen-AI systems are both trained on and produce unstructured data. So, corporates everywhere are suddenly looking to leverage their unstructured data to better train AI models and drive deeper business insights. Come and hear how Nuix is powering the application of Generative-AI to business by deeply curating enterprise data and enabling a variety of innovative use cases. 

• Utilising storytelling techniques that resonate with stakeholders and facilitate understanding.
• Simplifying data visualisation to make it more accessible and actionable for decision-makers.
• Connecting insights to strategic actions to drive business strategies and outcomes.
  
  

• Encouraging experimentation and learning
  without fear of failure.
• Providing training and resources to enhance the team’s data literacy and analytical skills
• Aligning data innovation with business goals to maximise impact and drive growth.

In today's rapidly evolving generative AI landscape, Chief Data & Analytics Officers (CDAOs) face unprecedented opportunities to revolutionize their organizations. While GenAI integration offers tremendous potential for innovation and competitive advantage, building secure AI systems presents significant challenges.

How do you safely tap all of the unstructured and structured data within your hybrid cloud landscape to feed these AI models? How can you ensure sensitive data is protected? What guardrails are needed to protect AI models from prompt injection and data exfiltration attacks? How do you implement continuous risk monitoring throughout your generative AI pipeline?

Join Tim Stead, Technical Director at Securiti to learn how CDAOs can implement a comprehensive framework to build safe, scalable generative AI systems that drive organizational success.

Key Takeaways:

• Critical risk factors in generative AI implementation
• Four essential components for developing secure, compliant generative AI systems
• Strategic best practices for embracing generative AI with innovative governance and compliance approaches

• Building effective collaborations among stakeholders to enhance innovation and driving business growth.
• Navigating regulatory and compliance challenges while forming data partnerships, ensuring that data sharing aligns with legal frameworks and ethical standards.
• Maximising value through data integration to unlock new insights

• Structuring the foundational elements—data quality, governance, and ecosystems—to build scalable and impactful data strategies
• Tackling the complexities of fragmented legacy systems and integrating diverse data sources to unlock performance transparency and drive value.
• Practical insights into how generative AI and advanced analytics can complement traditional methods to deliver operational and strategic benefits.

• Uncover monetisation opportunities by identifying trends, customer needs, and competitive advantages
• Evaluating data quality and relevance for monetisation efforts
• Creating targeted strategies based on insights to optimise revenue streams effectively

• Exploring the infrastructure requirements to support the computational intensity of agentic AI
• Discussing the importance of robust data pipelines and real-time processing capabilities to ensure agentic AI systems can make accurate, timely decisions in dynamic environments
• Examining how enterprises can enhance their IT infrastructure with advanced cybersecurity measures and fault-tolerant systems to safeguard autonomous AI operations against threats and disruptions

Speakers

Pawan Darda CTO Pelago

Aishwarya Prabhat Field Solution Architect Google

• People strategy: Recruiting and developing tech workforce
• Culture strategy: Nurturing enabling culture for the data and AI era.
• Structure strategy: Structuring for speed, agility and innovation.

• Monetizing AI with Purpose – Why generative AI alone doesn’t guarantee ROI and how to strategically implement AI to drive measurable business outcomes.
• Identifying the right AI applications that enhance user experience, boost productivity, and create revenue opportunities.
• How a strong data foundation, modern infrastructure, and stakeholder buy-in pave the way for impactful AI-driven transformation.

• How SBS Transit is leveraging advanced data analytics to optimize operations, improve efficiency, and ensure smoother railway performance
• The journey of turning in-house rail solutions into marketable IP and expanding SBS Transit’s impact beyond local operations.
• The journey of turning in-house rail solutions into marketable IP and expanding SBS Transit’s impact beyond local operations.
  
  

• Assessing the maturity level of your data capabilities and setting improvement goals
• Ensuring data initiatives are agile and closely aligned with key business goals
• Leveraging data maturity to drive informed, real-time decision-making

• Enabling faster and more relevant insights for each business unit by establishing the data owner
• Encouraging collaboration between data experts and business leaders with domain-oriented teams
• Allowing each domain team to operate independently, enhancing scalability across the organisation using data mesh

• Discussing strategies for leveraging customer data to enhance personalisation while ensuring compliance with privacy regulations like GDPR
• Explore best practices for collecting, storing, and using customer data ethically to build trust and maintain customer relationships
• Utilising Technology to protect customer data while still extracting valuable insights

Panellists

Tanveen Singh Director - Legal & Governance IRD Global

Tarun Samtani Data Protection Director (Privacy & Cyber Risk) Morgan McKinley

• From insights to strategy – how organisations can translate data insights into actionable business strategies that drive revenue generation
• Developing clear action plans that outline specific steps for implementing insights into revenue-generating activities
• Monitoring impact and adjusting tactics based on performance metrics

• Navigating the complexities of data governance in a rapidly evolving regulatory environment
• Addressing the cultural shift required to integrate AI responsibly across organisations
• Balancing the costs of data initiatives with long-term value and business outcomes

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.

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.