As buy-side firms face tighter budgets, evolving AI strategies, and increasing scrutiny of data licensing, vendors must adapt to changing expectations. This panel explores what investors value most in a data partnership - from flexible licensing and transparent pricing to AI-ready terms and demonstrable ROI. Hear directly from buy-side leaders on how vendors can better align their commercial models with today's procurement priorities and tomorrow's innovation needs.

In this showcase session hear from some new entrants to the market data space, as they share the datasets and product launches set to challenge established vendors on quality, flexibility (and price!).

How can investors identify emerging retail-driven short-squeeze risk before it becomes obvious? Travis Whitmore discusses how securities lending, digital media, and social sentiment data can be combined into a useful signal for detecting crowded positions and potential market dislocations.

Market data has always been the foundation of quant investing, but AI is exposing where that foundation needs to change. This session examines how quant teams are rethinking their data stack to support AI at scale: real-time vs. historical architecture, tick-level granularity, and vendor consolidation. Speakers will discuss what's actually changing in how market data is sourced and delivered for AI-driven investing.

As AI and GenAI models become embedded in investment decision-making, the integrity of underlying data is under scrutiny. Dhagash Mehta, Head of Applied AI Research at BlackRock, examines how data quality control shapes model reliability, bias mitigation and responsible AI outcomes. Drawing on his work in applied and generative AI for asset management, he'll unpack the practical frameworks buy-side firms need to build trustworthy, defensible AI systems.

As AI transforms investment research, access to information and to the tools that process it is being rapidly democratized. But not all data, and not all research, is created equal. As the analysis layer commoditizes, a harder question comes into focus: where does edge actually come from when everyone has capable AI? This panel brings together buy-side and vendor perspectives to examine what still generates alpha in 2026. We want to explore which datasets, sources and vendor relationships remain genuinely hard to replicate, how buyers distinguish real differentiation from repackaged public information, and the extent to which human analyst judgment, the forward-looking insight and institutional knowledge that models can summarise but not originate, still carries measurable value. Drawing on Neudata's cross-market view of hundreds of providers, the session aims to separate what is genuinely proprietary from what has already been arbitraged away, and to map where the next wave of differentiation is likely to emerge.