How is front-office decision making evolving with real-time data?

Bloomberg Professional Services

What does immediacy look like in today’s trading landscape? How are firms reimagining infrastructure to handle high-velocity data, integrate AI into decision-making, enable collaboration, and scale securely?

At Bloomberg’s 2025 Enterprise Data & Tech Summit in London, Colette Garcia, Global Head of Enterprise Data Real-Time Content at Bloomberg, spoke with Ranil Boteju, Group Chief Data and Analytics Officer at Lloyds Banking Group, Gabriele Butti, Head of Credit and Public Finance Quant Research at J.P. Morgan, and Stu Taylor, Managing Director, Head of eTrading and Digitization at MUFG about how financial institutions are adapting workflows to market volatility, integrating AI into decision-making, modernizing data foundations, and balancing resilience with innovation across hybrid cloud environments. 

In focus 

Featured insights from the discussion panel: 

On a changing definition of real-time

Ranil Boteju: A few years back, people would call intraday feeds real-time... There is now the ability to take [streaming data] and actually try and understand the data in real-time as well. And so trying to get your data organized such that you can do that has become real focus...…and so a lot of the investment is less about, trying to react to something in the moment…[and] more about building your core data foundation… With all the new technology, particularly AI, it has exposed lots of organizations where they may not have actually invested in those foundational pieces.* 

On modernizing data infrastructure for long-term growth

Gabriele Butti: Resilience is probably a higher priority than scalability… because you don’t want things to go wrong. And that is obviously achieved through strategic technological choices. Nowadays, obviously the number of options is quite extensive. There’s a cloud platform and probably more than one cloud platform. There’s on-pre: what you do on one, what you do on another, which one serves as a backup for the other, and how many you want to be onboarded. How many do you want to be onboarded? Obviously there are costs implied and scalability. 

I would say the one lesson that I’ve learned… is that no matter how hard I try to be imaginative, I probably cannot, with a certain degree of confidence, predict what the future need is in terms of data platform like nowadays, we probably store ten times the number of ticks that we used to in only five years ago, and probably that’s still a sort of understatement. 

On the biggest transformational shifts with data 

RB: The notion of being a decision maker but expecting someone else to handle data and insight is gone. That world is dead. The real shift is building data literacy. That is the big innovation because it is now a core part of everyone’s role. You don’t need to code Python, but you do need to understand insights and data. It’s not something taught in schools, although most UK universities now offer many data-related subjects. This idea that data literacy is a fundamental part of every role is a major change. There’s still lots of room to go, particularly at the more senior ends of the organizations where people feel really comfortable… generating their own insights rather than relying on someone else.

*Quotations have been edited for brevity and clarity.

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