Sundar Venkataraman is looking forward to working on interesting AI experiments, such as specialized computing infrastructure, and simplification of advanced scientific methods.

Can you share the most interesting part of your career journey?

I started my career as a consultant focused on solving clients’ problems using traditional data management solutions such as SQL databases and massive Excel spreadsheets.  Later in my career as a solution architect, I had naturally gravitated toward building applications and systems that were reliant on data analytics and presenting insights in interesting ways.

Years later, I find myself building and helping several teams build solutions on Deloitte’s AI platform, CortexAI™.

This was the same time when buzz words like cloud, artificial intelligence (AI), and machine learning (ML) were replacing their traditional equivalents of data centers, analytics, and business rules.  Years later, I find myself building and helping several teams build solutions on Deloitte’s AI platform, CortexAI™. I find this transition from being a practitioner of data to being one who enables a massive community of Deloitte practitioners to become AI savvy through our AI platform truly inspirational and I’m grateful.

What excites you most about working with data and AI?

Data was locked away by DBAs, and models can only be created if you have a PhD in statistics—at least, this was once the popular belief. As data was put in the hands of business users, insights were democratized, and business decisions became more data driven.

The ability to observe interesting patterns in data and combine them in new ways, much like a chemist would, excites me the most.

The same is now happening with the creation of citizen data scientists and their ability to solve complex business problems using data and an unlimited amount of computing infrastructure. The ability to observe interesting patterns in data and combine them in new ways, much like a chemist would, excites me the most. With the explosion of data types (numerical, text, audio, video, etc.), access to specialized computing infrastructure, and simplification of advanced scientific methods, I am looking forward to working on more interesting AI experiments.

Describe an interesting project that you have worked on.

I am most fascinated by every opportunity I get to solve problems of varying scale, across a variety of industries, for some of the most impactful global organizations by using Deloitte’s CortexAI platform.

The experimental models we built not only helped identify the “features” in the data that were most useful to predict the likelihood of attrition, while balancing it with the employee privacy, but also scratched the surface on measuring the effectiveness of the interventions.

One such recent example would be the use of AI and data to reduce employee attrition in a timely manner. The wealth of information available to employers to support employees who are at a higher risk of attrition has always been locked away in unstructured data sources or hidden communication patterns. The experimental models we built not only helped identify the “features” in the data that were most useful to predict the likelihood of attrition, while balancing it with the employee privacy, but also scratched the surface on measuring the effectiveness of the interventions. The journey to solving this timely challenge has just begun, but I find the approach we are using to solve this problem using data and ML most interesting.