Repositioning Financial Market Infrastructure for Artificial Intelligence: Why Data Management Is the First Frontier

Capital markets are at an inflection point with artificial intelligence (AI). The convergence of AI, cloud-enabled platforms and innovation-driven change is reshaping how financial institutions manage, interpret and act on data. But this transformation isn’t driven by algorithms alone, it’s powered by the strategic modernization of data management.

For market operators at the heart of financial infrastructure—exchanges, central counterparty clearinghouses (CCPs) and central securities depositories (CSD)—the next generation of marketplace technology will be defined by data ecosystem modernization. Specifically, they must address not only how they support AI systems and development but also scale, govern and extract database intelligence to drive value across their clients and market ecosystems.

These themes and more are explored in a new white paper from Nasdaq’s Head of Research Doug Hamilton. In it, he argues that data management is a requisite for AI-powered innovation. Plus, besides platform technology there are human-based organization considerations to business alignment.
 

This isn’t about chasing innovation. It’s about building infrastructure that enables it and positions financial market infrastructures (FMIs) to meet modern demands today and into the future.
 

Market operators process billions of data records from transaction to billing. Much of it may be underutilized—trapped in legacy systems or fragmented across silos. Artificial intelligence changes that equation. What was once a data exhaust (i.e. an unstructured stream of byproduct data) now becomes a strategic asset, capable of powering predictive analytics, optimizing liquidity, enhancing market surveillance and empowering capital markets participants.

But unlocking that potential requires thinking beyond compute power. It demands a deliberate approach to data management—one that prioritizes accessibility, auditability and scalability.
 

As market operators lean into modernization of mission-critical workloads via cloud adoption or modular platforms and managed services, they must also focus on building a data management foundation. To support AI development, these four cornerstones must frame the strategic planning. 
 

AI-powered platforms must be built on data that is explainable, traceable and compliant. That means embedding data governance into the infrastructure—ensuring every data point has lineage, metadata and auditability. In high-stakes environments, trust isn’t optional; it’s engineered.
 

Effective analytics require both context and immediacy. FMIs must ensure that years of historical data are not only stored but instantly accessible via APIs, AI-ready formats and scalable cloud infrastructure. This dual capability enables both retrospective analysis and real-time decisioning.
 

AI workloads are dynamic. Some require high-throughput training environments; others demand low-latency inference. Cloud-native platforms offer the flexibility to scale compute, storage and networking on demand—supporting agile development cycles and cost optimization. Cloud capabilities also enable institutions to integrate external data sources, expand analytics pipelines and deploy applications across geographies.
 

AI is a volume game. Institutions must architect systems where the cost per unit of data processed decreases as usage grows. This enables experimentation, iteration and deployment without runaway infrastructure costs—turning data preparation into a strategic enabler.
 

Technology alone doesn’t drive transformation. Human capital does. Successful AI adoption depends on aligning talent, leadership, processes and culture around a shared vision.

• Talent strategy: FMIs must cultivate teams with the right mix of data scientists, engineers and domain experts—supported by upskilling, training and knowledge transfer.
• Leadership advocacy: Internal champions are essential to build momentum, secure investment and navigate change.
• Cultural readiness: Organizations must foster environments that support experimentation, cross-functional collaboration and iterative learning.
• Human oversight: As AI systems scale, human governance becomes critical to ensure ethical deployment and strategic alignment.

This alignment of people and processes is what turns artificial intelligence from a pilot project into real-world impact.
 

Another key to unlocking AI potential is the marketplace technology powering FMIs. Change is rapid and accelerating still. Inaction becomes an operational and business risk to FMIs as markets and participants evolve around them. Financial firms are already deploying AI in processes and workflows and increasingly expect key infrastructures to provide AI tooling to help them optimize.

Technology is crucial and the white paper demonstrates how platforms like Nasdaq Eqlipse Intelligence help FMIs modernize data for AI applications. By unifying data management, compute and AI tooling, Eqlipse Intelligence allows Nasdaq to leverage AI in the business and in our products while maintaining governance and control.
 

Cloud-enabled data lakes have been crucial to the modernization and resilience of Nasdaq’s operations, markets and solutions. And for other FMIs, cloud infrastructure offers the same strategic optionality and agility to help them adapt to change, integrate innovation and deliver client services that drive competitive advantage.

For FMIs, the imperative is clear: modernize the data layer, embrace cloud-native infrastructure, incorporate standards and deploy intelligent platforms that turn information into insight. The risks of inaction and competition are growing.

Nasdaq Eqlipse Intelligence is more than a technology solution. It’s a strategic enabler for institutions ready to reimagine their infrastructure, unlock new capabilities and build resilience for what comes next.

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