Modernizing the Trade Lifecycle With Governed Data and AI

Capital-markets firms are modernizing the trade lifecycle under pressure from every direction: growing data volumes, higher expectations for real-time insight, AI initiatives moving toward production, and shorter settlement cycles. At this point, nobody's debating whether to use AI. The real question is whether research, trading, risk, ops, and compliance are all working off the same governed data — or just telling themselves they are.

As firms move from experimentation to production, a pattern is emerging: the durable advantage is not a model in isolation. It is the ability to make proprietary data, like orders, executions, positions, research, risk, client, and operational data more discoverable, reliable, and governed across the lifecycle.

I spoke with Andrea DeSosa, Global Head of Capital Markets GTM at Databricks, about the operational pressures reshaping pre-trade, execution, post-trade, and surveillance workflows, to get insight on practical ways to prioritize modernization. This conversation has been edited for clarity and length and draws on themes explored in the ebook Modernizing the Trade Lifecycle in Capital Markets.

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Kim Hatton: What is forcing trading and research leaders to revisit the trade lifecycle now?