Description

In this session, Bhumika Shah will explore how AI-powered automation, predictive analytics, and intelligent data pipelines are revolutionizing efficiency in data engineering. Attendees will gain insights into optimizing data workflows with AI-driven ETL and anomaly detection; enhancing decision-making through proactive data management; and scaling AI solutions to manage complexity without excessive costs.

ABOUT THE SESSION FACILITATOR: Bhumika Shah is a Data Solution Engineer, PhD Scholar, and Professor, specializing in AI, Data Engineering, and Information Governance. With over seven years of experience, she has designed scalable data pipelines, improved data governance strategies, and developed data-driven predictive models that enhance decision-making and efficiency. Her expertise spans multiple industries, including healthcare, insurance, and retail, where she has worked on data automation, regulatory compliance, and business intelligence. She has played a key role in optimizing data systems, ensuring regulatory compliance, and improving operational workflows through AI and automation. Bhumika’s research has been published in peer-reviewed journals, reflecting her deep commitment to leveraging AI and data engineering for solving real-world challenges. She is passionate about building intelligent systems that improve data integrity, automate compliance processes, and support organizations in making data-driven decisions. As an educator, she enjoys mentoring future data professionals and bridging the gap between academic learning and industry practices.

Facilitator: Bhumika Shah, Data Solution Engineer and Professor, Chubb Insurance