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Artificial Intelligence – A Data Practitioner’s Perspective

The importance of building a reliable, scalable, Cloud-based data pipelines that deliver standardized-high-quality data to a variety of consumers – Analytics, Deep Learning, Machine Learning & AI.

Almost every AI discussion today revolves around a consumer’s point of view – the ways we will live and interact with AI. Although this is important and it is very exciting to speculate and predict our ‘future augmented life’, it is equally important that we took a look at AI from the other perspective. Of that of a producer, i.e., the data that feeds an AI entity. This is fundamental to the idea that without high quality and standardization of data inputs, the output/actions of an AI entity, will be questionable at best. And this is where Data Integration plays an integral role. The importance of building reliable, scalable and Cloud-based data integration hubs/pipelines that deliver high-quality standardized data cannot be understated. We hold a huge social and moral responsibility in providing clean data, to AI entities that we will interact with.


Gaja Krishna Vaidyanatha, Global Head of Data Services, Global Private Banking, HSBC

Gaja Krishna Vaidyanatha is a seasoned data practitioner with a 26+ year proven track record of managing and integrating large data footprints, on-premises and on the Cloud, across verticals such as Finance, Banking, Retail, Healthcare, High-Tech, Govt. and Utilities in the Americas, Europe, and Asia. He is passionate about data integration and building cloud-native serverless data pipelines for Analytics, Machine Learning, and AI. He currently heads up Data Services for the Global Private Banking division of HSBC.

Jacqueline Teo, Chief Digital Officer, HGC Global Communications Limited

Jacqueline Teo is Chief Digital Officer, responsible for technology and digital capabilities in the service of customers and internal staff. Her remit covers strategy, road mapping, architecture, delivery, and support, as well as accountability for P&L, and she has led significant technological transformation projects for large and complex organizations.

During a career that stretches back 25 years, Jacqueline has held a number C-level posts in the global telecommunications, media and entertainment industries, and has earned a reputation for spearheading game-changing initiatives on behalf of customers. One of her passions is the concept of ubiquitous access to technology with the aim of improving lives around the world.


Jeffrey Ng, Head Data Lab, BNP Paribas

Jeffrey Ng now heads the Data Lab of APAC Architecture and Digital, responsible for developing machine learning POC/ MVP solution and platform for entities of BNP Paribas in the region. Prior to joining the team, he headed the team that applies client analytics in business development within BNP Paribas Corporate & Institutional Banking – Greater China for 3 years and helped set up the regional team 8 years ago. With 13 years of experiences in Machine learning in banking (including retail, commercial and investment banking), he is proficient in statistical and machine learning approach in risk, operations, and customer relationship management. Ex PwC Consulting and GE Capital.

He is a CFA and had an MBA in Finance with the Chinese University of Hong Kong, 1stHonor in Computing and 1st Honor in Management with Hong Kong Polytechnic University.

Outside of his organization, he is the vice-chairman of the FinTech Committee at the French Chamber of Commerce Hong Kong and has active involvement in Artificial Intelligence Hong Kong.