Mumbai, December 28: The dynamic Data Analytics industry is emerging with new cutting- edge technologies. In order to thrive in this evolving landscape, business are deploying new innovative solutions to achieve their goals. These solutions are highly efficient and helps business to make quick decisions and drive growth. To thrive in this data-driven culture, developing data literacy is essential.
Mr. Marquis Fernandes, who leads the India Business at Quantic India, guides this lively discussion with Mr. Nilotpal Gupta, Head of Data Science of ICICI Securities. They uncover actionable insights from the enthralling world of ICICI Securities, revealing light on its approaches for fostering invention. This interesting dialogue is created to reveal useful insights for the ever-changing Data Analytics Industry.
Mr. Nilotpal Gupta, has more than 2 decades of experience in the areas of Data Management, Data warehousing, Business Intelligence, Descriptive/Predictive Analytics, Data Visualization. He have implemented large scale Analytics Implementation in the BFSI sector.
What professional aspirations keep you motivated and driving your commitment to innovation, leadership, and achieving impactful outcomes?
In order to keep the motivation level going one must enjoy the new challenges that comes in and not feel let down by work pressure. Thankfully Data Analytics is one area which is all about exploration of new ideas of how it can contribute to outcomes. Hence, it throws up constant challenges.
In navigating the dynamic landscape of Data Analytics, what core skills do you consider essential for sustained success and relevance, and how do you cultivate these skills within yourself and your team?
While the things keeps changing in the Area of Data Analytics Rapidly, one needs to have certain core skills to adopt new things. Some of the key core skills we look at is fundamentals in Mathematics, Statistics and Computer Science. Post this it is the hunger to learn new technologies in the Areas of Data Engineering, Machine Learning, Artificial Intelligence, and Data Visualization. The recent disruption in the Gen AI space is keeping our fingers crossed that this might be the next big thing in the Analytics space.
What strategies do you employ to align data science initiatives with overall business objectives, ensuring that data-driven insights contribute to organizational success?
One thing which we need to ensure we must understand the business problem well, before building the Analytical Solution. We need to understand what are the critical Business KPIs and Metrics that would get impacted through the Analytical Solution. For example, when we deploy a Machine Learning Model to increase business conversion, we monitor what was the conversion before deployment of the model and what is after deployment, we also perform A/B testing to ensure we know the performance difference between Target and Control Set.
In managing a team of data scientists, how do you balance the need for autonomy and creativity with the requirement for cohesion and alignment toward common goals?
Because the DS team works so closely with Business teams, it helps the team to understand the common goal which is to drive business through support from DS team, however, while doing so the team has complete freedom of applying the innovative ideas to build the solution. This helps us to ensure the innovation is always tagged to solve a business problem.
What pivotal advice would you offer to aspiring data scientists entering the field, drawing from your experience and insights in the dynamic landscape of data science?
Keeps us grounded and constantly keep yourself updated with the kind of innovations happening in the Industry. Enjoy the challenges coming in the work space and solve them through innovative ideas.
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