Traders should prepare data for AI by starting with the execution decisions they want to improve, then ensuring the underlying data is complete, consistently defined, time-aligned, traceable, and accessible within the relevant workflow. The goal is to make the data behind a specific use case reliable enough for a trader or model to use with confidence.

In the 2026 Equities Leaders Summit session Blueprints for Better Trading, Lauren Weintraub, Head of High Touch Trading, Americas and EMEA at Nomura Asset Management International, described how her team uses custom FIX tags to capture trading context and make data more useful across internal tools and transaction cost analysis. "We use those FIX tags to provide ourselves with a lot of data tools," she said, where data becomes more valuable when it preserves why and how an order was handled.

Recent conference discussions also emphasized that AI-ready data needs a clear purpose. For a trading desk, that means linking order, execution, market and workflow data to a defined task, such as identifying exceptions, preparing trader context, or comparing execution outcomes.

Chief data officers, trading analytics leaders, and market structure teams should begin with the five datasets most relevant to the chosen workflow. Document each field's business definition, source, owner, timing, coverage, and known limitations; then, resolve the gaps that would alter a trading decision or undermine a model's output. Add controls for missing values, inconsistent identifiers, timestamp mismatches, and undocumented changes before moving to a pilot.

Join the upcoming Equities Leaders Summit to compare how trading leaders are making execution data more usable for AI, analytics, and better-informed human decisions.