Data may be plentiful in capital markets but clarity is in short supply. In our latest episode of Data in the AI Era, we sat down with Cat Turley, CEO and founder of ExeQution Analytics, to talk about the future of trading analytics and what it takes to turn data into action when speed, trust, and transparency are non-negotiable.
From her early days building analytics platforms at multinational banks to her current role helping firms make smarter use of high-cost data, Cat offers hard-won insights into the human, technological, and cultural forces shaping the front office.
Making data work at the speed of markets
Cat’s message is clear: in capital markets, data isn’t the product. Understanding is. And too often, there’s a gap between the two.
The conversation covers:
- The human side of data collaboration: bridging the divide between quants, traders, and technologists
- How GenAI and natural language interfaces are democratizing analytics
- The strategic value of real-time iteration and why ‘not bad’ shouldn’t be the bar
- Time series similarity search and its potential to redefine how we model risk and opportunity
She also lays out the often-overlooked operational and cultural bottlenecks that slow down even the most technically advanced firm, from front office silos to rigid specs that miss the mark.
Five takeaways from the conversation
1. Data costs more than money
“Data is becoming increasingly expensive. So it’s imperative that organizations are really making the most of it and transforming that data into analytics that can drive better decision making.”
The price of raw data is rising, but the real cost comes from missed opportunities. Many firms focus on acquisition and storage while falling short on usage. Cat urges leaders to shift their lens. Value comes from insight, not inventory. Real-time analytics that drive decisions are where investments pay off.
2. Good questions lead to better outcomes
“Every question that you answer allows you to determine a hypothesis… and then you have the opportunity to either validate or disregard that hypothesis based on kind of further investigation.”
The ability to challenge assumptions and adjust your approach matters more than any single answer. Cat highlights the importance of iterative questioning. A rigid analysis plan limits discovery. Markets move fast, and insights emerge when teams respond quickly to the signals they see, not the ones they expected.
3. Data can fix broken conversations
“The data and analytics can be a unifying language to enable better collaboration between [traders, quants, and tech].”
Cat sees analytics as a bridge between three groups that often struggle to connect. Traders, quants, and technologists bring different goals and languages to the table. A shared data layer, with accessible tools, helps teams focus on facts. It removes friction and creates space for more productive decisions.
4. Look close, then step back
“A good analytics platform should operate as both a microscope and a telescope.”
Teams need the freedom to switch perspectives. One moment might call for a deep dive into a single trade. The next might demand a view across thousands of instruments. Systems that support both levels of analysis—without slowing down—put users in control. That flexibility becomes a competitive advantage.
5. The value is already there. You just have to unlock it
“Don’t underestimate the value that is hiding in your data… it can really help areas of your business in a way that you can’t quite comprehend.”
Many capital markets firms are sitting on a goldmine of underused data. Cat argues that the right tools can surface insights that improve performance, reduce risk, and speed up time to value. You don’t need more data. You need faster access, clearer paths to insight, and the conviction to act when it counts.
Why this matters for your team
Whether you’re managing a quant team at an investment bank or leading a data-driven transformation on the buy side, Cat’s insights are a reminder that speed and scale alone aren’t enough.
You need clarity. You need alignment. And you need systems that don’t just handle data—but help you trust and act on it.
That’s the kind of thinking that drives us at KX.
Want to learn more? Explore how the high-performance analytical database for the AI era can help you ask better questions and act faster on your data.
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