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Ahead of the Data & Analytics Leaders Summit Singapore 2019, we had an interview with Girish Sundaram. Girish has 18+ years of multi-faceted experience in the IT industry starting from database engineering, customer support, solution architecture design & implementation and technical pre-sales to leading international teams in complex large-scale engagements. He is a globally recognised thought leader in the industry within the areas of data/ big data, prescriptive and predictive analytics and insights visualisation, with effective story- telling skills. He has significant experience in asset creation and re-use having 15 filed patents, 16 published disclosures and more than 20 publications in renowned international journals. Girish is currently the Technology Director for Retail Data Analytics, part of the CIO office responsible for managing large complex Data science and Analytics solution implementations for the bank on a global scale.
Tell us a bit more about yourself and your professional background

I have spent close to 2 decades in the IT industry and worked across various domains across the globe. I am a data person and love anything and everything about data. I’m a passionate inventor and have more than 15 filed patents, numerous journal publications, technical disclosures and conference presentations. My area of interest is primarily data analytics, AI, Machine learning and Cloud computing. I believe that learning never stops and as a PhD candidate with the University of Arkansas focusing on Information Sciences, I hope to make a credible contribution to this data centric, knowledge driven, technology world. I currently lead the Retail analytics technology practice for a leading bank in Singapore.

How can AI be used to create a more personal experience for customers?

AI should be the backbone on which enterprises build their customer personalization strategies. I have seen AI getting widely accepted as the go-to means for taking crucial decisions when it comes to say, running a campaign for a retail use case in a bank. One of the ways that organizations are using AI is to formulate, what is called the unified data platform, for end to end campaign management. Organizations can use the UDP to integrate data from various exogenous systems and combine it with their 1st Party data to get valuable insights into customer behavior, propensity to buy a particular product, preferred channels to reach out etc, which can all be used to hyper-personalize the customer experience when interacting with your organization.

What has been the most effective use case of AI so far that you have encountered in your experience? 

There have been a few but my personal favorite is when I had the privilege of leading a team of data scientists and Big Data architects, to implement a real-time health monitoring system for a medical facility; based on streaming analytics, using real-time streaming data, appliance based analytics, OLTP DBs, Machine Learning algorithms and working with IoT sensor data coming from medical monitoring devices, to accurately predict anomalies early and prevent the onset of phenylketonuria (PKU) and an amino-acid metabolic disorder, which can lead to severe developmental issues in premature babies. Usually the typical use cases that we encounter are regular business and funding focused and rarely something that has a direct impact on human life. The project was a huge success and we were able to predict accurately the early onset of PKU 2-3 days in advance compared to traditional scanning techniques.

What are the biggest challenges you have encountered when using AI and how have you overcome them?

Everyone is excited about AI, Analytics, Machine Learning and Cloud, to name a few buzz words. I have seen some fantastic use cases that senior management wants to deliver but when we come to the ground realities, I see that most of the organizations are not adequately prepared to use most of the tools that are available, due to a variety of legacy reasons. One of the major obstacles, especially when it comes to a bank, is due to regulatory, legal and compliance bottlenecks. Whilst its paramount to safeguard customer data, it’s also important to embrace changes in the technology landscape and think of changes that need to be made to internal processes, to make it easier for tech teams within organizations to fast-track the adoption of AI and related technologies. Many of the innovations are in the cloud and I think the next generation of AI solutions will be completely cloud based simply because of scalability, elasticity, ease of management and most importantly cost. Enterprises that accelerate adoption of cloud-based analytics and AI will have an edge over their counterparts in the coming years.

What do you think will be the key take-away from your presentation at the upcoming Data & Analytics Leaders Summit in Singapore?

I will be talking about using a unified data platform to help organizations deliver hyper-personalized experience for customers. There are challenges in NBO orchestration, and I will talk about how we can use various strategies to overcome these challenges.

You can find more details about the Data & Analytics Leaders Summit here.

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