Future-Ready Markets: Data, AI and Strategic Transformation

Artificial intelligence (AI) innovation continues to evolve rapidly, changing the shape of the financial industry landscape. As market operators and other institutions increasingly develop and deploy AI applications, they’re learning that data management is the foundation of success.

For financial market infrastructures (FMIs), this data imperative is coming into focus. From compute to storage and cloud enablement, establishing a modern data management framework is taking strategic priority, requiring investment and attention. But where do market operators start?

I explore these considerations in new white paper for Nasdaq Financial Technology: Preparing Data for AI Applications: Technical and Organizational Considerations for Market Operators. The paper discusses key operational and organizational best practices, while also introducing real-world examples of how we’re leveraging AI.

In this article, I’ll synthesize the key themes and learnings from our journey with Nasdaq Eqlipse Intelligence to powering AI in our business.
 

Modern market operations are built on successful adoption of technology. Beyond infrastructure for mission-critical workloads (i.e., trading or clearing), data management must also be optimized and streamlined—spanning governance, standardization, integration and quality control.

The capacity to realize the promise of AI fundamentally relies on this modernization of data management, especially at the scale of market operations. FMIs need dependable tools and platforms for managing data across its life cycle. This is the necessary foundation to AI. Yet building and maintaining data ecosystems at scale can become a challenge as volumes swell and systems evolve.

To execute on AI strategy, FMIs must first understand and prioritize these four critical areas of market data preparation:

• Reliability: Data integrity is paramount. FMIs need solutions with reliability and auditability, ensuring that all data is traceable and transparent. This allows for accurate, explainable and compliant AI models. Traceable lineage and metadata are essential for refining model behavior.
• Accessibility: Comprehensive market data offers significant value through its extensive reach and detail. Having access to broad historical records spanning multiple years enables users to quickly obtain relevant information via advanced APIs and formats tailored for AI applications. This connectivity bridges static datasets with dynamic analysis capabilities. The practical benefit of historical data lies in being able to retrieve it dependably, while efficient access relies on a robust foundation of information that empowers sophisticated AI processes.
• Cloud-enabled flexibility: AI applications require adaptable compute, storage and networking resources. Cloud infrastructure allows for dynamic scaling, supporting agile development and cost efficiency. This evolution frees deployments from fixed resources, making experimentation and large-scale model training easier and faster.
• Economics: Data preparation for AI is both a technical and economic consideration. Achieving lower costs per data unit enables scalable AI without excessive infrastructure expenses. FMI platforms must prioritize cost-effective scaling, so teams can develop and deploy AI models without overspending on data preparation.
   

Real-world implementation of AI depends on people and business strategy. This is, perhaps, a less-advertised aspect of AI strategy given the large and necessary focus on technical considerations.

But without organizational alignment and empowerment, AI is a rocket without a launchpad.

• Succeeding with AI starts by hiring and retaining the right talent—data engineers, data scientists and product leaders. But it’s equally about upskilling existing teams and institutionalizing best practices. Knowledge must flow throughout the organization.
• Organizational culture must support change. Cross-team collaboration, stakeholder buy-in and empowering employees to lead pilot programs all help de-risk AI initiatives. A culture of learning, experimentation and adaptability is vital.
• Internal AI champions drive momentum. Leaders who communicate a clear vision, encourage collaboration, and celebrate wins help build sustainable AI programs that go beyond technical pilots to strategic transformation.

Nasdaq has learned many of these lessons through our history of innovation. One key milestone that has helped us succeed with AI and data is Nasdaq Eqlipse Intelligence. Internally launched in 2013, the client-facing platform now helps FMIs master their data management to generate insights, empower teams and engage their own users.

A comprehensive platform for democratizing data access, enhancing operational efficiency and monetizing data, Eqlipse Intelligence has been leveraged in our owned and operated marketplaces. FMIs using the Eqlipse Intelligence can similarly gain scalability, improve resilience and accelerate innovation, following use cases we have established to improve performance and efficiency through AI.
 

AI brings significant opportunity but also significant risk. The journey requires investment, effort and robust change management across the enterprise.

Working with the right technology partner can help manage and accelerate the journey, bringing managed services and best practices. Nasdaq Eqlipse Intelligence is built for modern markets—cloud-native, scalable and built with the principles of reliability, auditability, historical accessibility.

By prioritizing robust data ecosystems and embracing change, we can unlock new possibilities, safeguard integrity and deliver lasting value to market ecosystems globally.

Learn more by downloading the full white paper here or reading about the Nasdaq Eqlipse solution suite for market infrastructure providers.
 

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