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

• Empowering non-technical users by highlighting the tools and training necessary to enhance data literacy among all staff members
• Examining the importance of dismantling data silos to promote collaboration among departments
• Addressing the challenges of maintaining data quality and governance in a democratised environment

• 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

• Discuss how Natural Language Processing (NLP) can make complex data more accessible to users by allowing them to interact with data using natural language queries
• Exploring how NLP can provide contextual insights from unstructured data sources, enabling organisations to make more informed decisions
• Addressing the challenges organisations face when integrating NLP technologies into their existing systems

• Discussing the evolving regulatory environment and its implications for data governance
• Exploring best practices for establishing compliance frameworks that ensure data protection and privacy while facilitating business operations
• Addressing strategies for fostering innovation in data use while managing compliance risks

• Overcoming issues like inconsistent and incomplete data is essential to provide accurate insights for advanced analytics
• Selecting and integrating the right data preparation tools is key for smooth data processing and analytics
• Preparing unstructured and varied data types requires advanced tools and techniques to unlock meaningful insights

• 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

• Understanding the foundational principles necessary for effective data governance in AI
• Exploring how to incorporate AI technologies into existing governance frameworks to enhance data quality and security
• Highlighting methods for evaluating the effectiveness of governance frameworks

• Understanding the complexities of adhering to diverse data protection regulations across different jurisdictions
• Facilitating secure data transfers
  cross-border while maintaining compliance with local laws and international standards
• Highlighting the need for collaboration between organisations, regulators, and stakeholders to create effective governance frameworks

• 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.

• Instilling a sense of ownership among employees regarding data governance, encouraging accountability at all levels
• Creating training initiatives in enhancing understanding of data governance principles
• Understanding the importance of establishing feedback loops to continuously improve governance practices

• 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
  
  

• Empowering users with self-service capabilities
  to foster a data-driven culture.
• Enhancing decision-making with augmented analytics to simplify complex data analysis
• Addressing data quality and governance challenges to ensure reliable insights while empowering user autonomy.

• 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.

• 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.

• 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

• 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

• 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

• Highlight the importance of cultivating a security-first mindset across all levels of the organisation to enhance data protection
• Discuss the role of cutting-edge technologies, such as encryption and AI-driven monitoring, in safeguarding sensitive data against breaches
• Emphasise the need for clear communication about data security practices to build trust among customers and stakeholders

• Understanding the difference between data warehouse and data lakes and how it fits into your business
• Understanding the cost of the two options and what they can provide – cost-efficiency, optimised performance analytics
• Matching your choice to business goals—data warehouses for reporting, data lakes for advanced analytics and machine learning

• Explore how advanced data visualization techniques are used to translate complex metrics like player fitness, speed, and game statistics into clear, actionable insights for coaching staff and athletes
• Discuss the role of intuitive dashboards and visual storytelling in improving real-time decisions during matches, post-game analysis, and long-term player development strategies.
• Highlight how effective data visualization fosters better communication and collaboration between data teams and coaching staff, ensuring that insights are understood and applied effectively to drive athlete performance.

• 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

• Exploring strategies to attract top data talent and reduce turnover rates
• Understanding what motivates data professionals and fosters long-term commitment
• Creating a culture that supports continuous growth and skills development
  
  

• 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

• Identifying the AI skills gap and developing targeted training programs
• Building a flexible learning culture to keep pace with AI advancements
• Encouraging a mindset shift towards AI adoption and innovation

• Integrating data into day-to-day workflows to build familiarity and usage
• Implementing tools and resources that make data accessible to all employees
• Fostering a data-driven culture to empower employees in decision-making

• Exploring the strategic value of data investment and its impact on business competitiveness
• Understanding which data infrastructure and tools provide the highest return on investment
• Examining real-world examples of how data-driven companies gain market advantages

• 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.