February 23, 2026
Moderator and Panelists
James McGeehan of Snowflake was the moderator of this event. McGeehan is based in New York and serves as the Head of Banking, Capital Markets, & Payments for Snowflake. He has held executive roles leading sales and trading businesses across the world’s largest banks, such as UBS, HSBC, & Wells Fargo. At AWS, McGeehan served as the Global Capital Markets Lead, overseeing global strategy and implementation efforts. Prior, he was the co-founder and CEO of FX Transparency, a technology platform aimed at benchmarking FX transaction costs to assist asset managers in reducing costs and improving investment returns. He holds a BA degree in Government from Dartmouth College.
Carmen Li is the CEO and Founder of Silicon Data, a company dedicated to revolutionizing compute markets through robust data transparency. Under her leadership, Silicon Data has developed innovative products which include GPU price insights, carbon footprint tracking, and benchmarking solutions designed to evaluate and certify GPU performance. Li has extensive experience in high-frequency trading and financial infrastructure, which inspired her to bring similar transparency and efficiency to compute markets. She previously served as the Global Head of Strategic Alliances – Enterprise Data at Bloomberg and as Senior Vice President – Global Marketing Manager for Working Capital Products at Citi. Li holds a degree from University of Illinois Urbana-Champaign and an MBA from Harvard Business School.
Brad Franklin is a Director of Product Management within FactSet’s Data Solutions business, overseeing the strategy and development of company, market, pricing, and reference data products. Since joining FactSet in 2007, Franklin has held diverse roles across consulting, sales, strategy, and product management. He holds a B.A. in Economics from the University of Richmond and an M.S. in Data Science from Northwestern University.
Dr. Andy Li, CFA of Merrill Lynch Bank of America, is an AI and finance leader with over 18 years of experience driving data- and AI-powered innovation across financial services and technology companies. He previously led generative AI initiatives at Amazon Ads and now serves as a Director at Bank of America, focusing on scalable AI adoption, risk management, and value creation in regulated environments. Li holds advanced degrees from Carnegie Mellon University and Harvard Business School and teaches graduate-level courses on LLMs and AI agents in finance at Fordham University.
Panel Discussion
The event explored the role of data in financial services and how it has evolved amid rapid technological change and the rise of artificial intelligence. Panelists examined how organizations are rethinking data not simply as an input, but as a strategic asset central to monetization, risk management, and competitive differentiation. The conversation highlighted both the operational foundations required to support this shift and the emerging opportunities shaping the future of data-driven decision-making.
Data was once treated largely as a commodity—collected, stored, and consumed as a static input into investment and operational processes. Institutions focused on volume and access, assuming that more data inherently translated into better outcomes. Today, however, data is viewed through a fundamentally different lens: not as a raw input, but as a differentiated asset whose value depends on how it is refined, contextualized, governed, and deployed. Like crude oil transformed into usable energy products, raw data must be structured, enriched, and integrated into workflows to generate measurable insight and commercial value.
Differentiation now hinges on several core components. First is data quality and hygiene—clear metadata, standardized reference frameworks, lineage tracking, and auditability back to source. Second is connectivity: the ability to systematically link datasets across entities, securities, transactions, and workflows so insights can be generated at scale rather than in silos. Third is governance and entitlement control, ensuring contractual compliance, security, and appropriate usage. Fourth is contextual augmentation through AI—embedding data within intelligent systems that can interpret, synthesize, and act. Finally, robustness in methodology and transparency—particularly for new asset classes—ensures trust, resistance to manipulation, and consistency in publication and normalization processes.
Clients today are not simply purchasing datasets; they are seeking actionable intelligence embedded directly into decision workflows. They want transparency in pricing and performance, audit trails for regulatory confidence, and tools that reduce time to generate meaningful insights. Financial institutions use internal transaction data to manage risk, hedge volatility, forecast demand, and identify potential competitive advantages. Panelists also highlighted how proprietary transaction data can be transformed into leading economic indicators that provide institutional investors with differentiated, near real-time insight into macroeconomic trends. Increasingly, clients expect data products to be interoperable—available across platforms, accessible through APIs, and compatible with AI-driven applications. They also expect data to move beyond structured formats into multimodal domains such as voice, text, and image, enabling sentiment analysis, customer experience optimization, and predictive analytics.
Looking forward, the future of data usage will likely be defined by a drive to reduce the distance between raw data and actual decision making. AI agents will increasingly translate inputs directly into recommendations or actions, compressing traditional analytical layers. At the same time, this acceleration will elevate the importance of governance, cybersecurity, model validation, and responsible AI deployment. While automation may shift work tasks, the consensus was that AI will more often augment professionals rather than replace roles outright, particularly in regulated environments where accountability remains essential. As volatility, compute costs, and data modalities expand, institutions will focus on cost-performance optimization, infrastructure flexibility, and risk mitigation. Ultimately, the competitive edge will belong to organizations that can combine trusted, well-governed data foundations with intelligent, scalable workflows – transforming information into insight at speed while maintaining transparency and control.
This article was prepared by Thomas Prendergast, based on a transcript summarized with the assistance of ChatGPT (OpenAI). All content was revised, edited and reviewed for accuracy and clarity by the author.