Summary
AI is only as powerful as the data that fuels it, but staying at the forefront of technological advancements is just as critical. So, where should you invest to maximize impact—better data or more advanced tech?
Join expert speakers from The University of Chicago Booth School of Business, The Hebrew University Business School, and S&P Global Market Intelligence as they explore this question. Through practical examples, they’ll shed light on how to navigate the balance between data and technology investments to achieve better outcomes in AI-driven initiatives.
This session will delve into how S&P Global’s structured, high-quality, and linked datasets—such as Compustat Financials, S&P Capital IQ Estimates, and machine-readable textual data can significantly enhance the accuracy and relevance of your AI models. Explore how our NLP-ready textual datasets allow you to extract insights quickly and easily from lengthy textual documents.
Join this webinar to understand how data and technology must work together to create real impact and explore how your investments today will shape the future of your AI initiatives.
Speakers
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S&P Global Market Intelligence
Harpreet Kaur
Associate Director, Business Development
Harpreet Kaur is an Associate Director of Business Development at S&P Global Market Intelligence where she currently leads strategic initiatives for the Industry & Company Data business, driving growth in private company data coverage. Harpreet previously served as the Go-to-Market Lead for S&P's Data, Valuations & Analytics and Private Markets businesses, successfully launching new products, and expanding data offerings. She brings deep expertise in strategic business development and product management. Harpreet holds a degree in business and technology, enhancing her leadership in data-driven initiatives. Harpreet Kaur holds a Bachelor of Business Studies (B.B.S) in Finance from Deen Dayal Upadhyaya College
S&P Global Market Intelligence
Kevin Zacharuk
S&P Global Market Intelligence
Product Manager, Textual Data
Kevin Zacharuk is a Manager of Product Management within the Data, Valuation, Analytics (DVA) group at Market Intelligence. He oversees the management and development of text-based data feeds for machine readable use cases.
Kevin Zacharuk is a Manager of Product Management within the Data, Valuation, Analytics (DVA) group at Market Intelligence. He oversees the management and development of text-based data feeds for machine readable use cases. He is responsible for the growth of our existing textual data feeds as well as uncovering new opportunities to serve quantitative analysts, data scientists, and other users leveraging Natural Language Processing (NLP) in their workflow.Kevin began his career at S&P Global in 2014 working out of our New York City office. He has held a variety of roles in Product Management focusing on feeds since 2018. He is based out of Toronto, Canada.
Kevin holds an HBA from the Ivey School of Business.
S&P Global Market Intelligence
Prof. Ronen Feldman
Head of Technology, Quantitative Signals
Ronen Feldman is a Full Professor of Internet Studies at the Hebrew University Business School. He is currently Head of the Data Science Department.
Feldman is an expert in text mining and data mining, and his research areas include Information Extraction (IE), algorithmic trading, social networks, natural language processing, extracting meaning and relationships from text, and sentiment analysis from texts.
He has won the Abe Gray Prize for Excellence in Research, the Soref Post-Doctoral Fellowship from Bar-Ilan, and the Levi Eshkol Fellowship. Additionally, he has received academic grants from many institutions, including from the INTEL Corporation, MAFAAT, the Israel Science Foundation, the German-Israel Foundation, the Israeli Sciences and Arts Ministry, and the BSF (Bi-national Science Foundation).
Feldman earned his Ph.D. in Computer Science from Cornell University. He holds an M.Sc. in Computer Science from Bar-Ilan University, and an B.Sc. in Math, Physics, and Computer Science from Hebrew University.
University of Chicago, Booth School of Business
Alex Kim
Ph.D. candidate
Alex Kim is a Ph.D. candidate in accounting at the University of Chicago, Booth School of Business. He received a master’s degree in business administration with a concentration in accounting and a dual Bachelor's degree in Economics and Business Administration, Summa Cum Laude, from Seoul National University.
Alex’s research mainly examines the information processing of investors in the capital market. Recently he has been interested in studying how large language models and artificial intelligence (AI) can assist investors' information processing and economic decision-making. His research has been featured in major media outlets such as Financial Times, Bloomberg, Fortune, and Forbes, and funded by Ernest R. Wish Ph.D. Research Fellowship and Fama-Miller Center for Finance Research.