Nasdaq Eqlipse
    Preparing a Data Foundation for AI Success
    Preparing a Data Foundation for AI Success

    Future-Ready Markets

    Preparing a Data Foundation for AI Success

    Before market operators can capitalize on AI opportunities, they need to assess data management infrastructure. Nasdaq’s Head of AI Research, Doug Hamilton, explores the key considerations for success—from cloud computing and data quality to business alignment and change management—and lessons from Nasdaq’s journey with AI. 

    MODERNIZING Data management

    Innovation Capacity Hinges on Infrastructure

    The rapid pace of change is pressuring financial market infrastructures (FMIs) to innovate quickly. But success with AI is tied to first establishing a resilient, robust data foundation. This common backbone is essential to training models and ensuring cost-effective innovation. Auditability, integrity, accessibility and cloud computing are all priorities, but so too are organizational investment and leadership culture. AI is truly a comprehensive endeavor which will require FMIs to be strategic in how they approach technology, talent and deployment.

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    AI Readiness in Financial Market Infrastructure: Strategy, Data, and Architecture with Douglas Hamilton

    Listen to Nasdaq's Doug Hamilton discuss the challenges and opportunities of AI initiatives, the key role of data architecture and how Nasdaq Eqlipse Intelligence can help FMIs accelerate readiness through cloud-native, modular data management infrastructure. 

    Listen to Nasdaq's Doug Hamilton discuss the challenges and opportunities of AI initiatives, the key role of data architecture and how Nasdaq Eqlipse Intelligence can help FMIs accelerate readiness through cloud-native, modular data management infrastructure. 

    Pillars of Data Preparation

    In his commentary, Hamilton walks through the key steps to data preparation, as well as what lies beyond the technical concerns, including:

    • Reliability: Ensure transparent, verifiable processes with clear audit trails for trusted data.
    • Accessibility: Access historical datasets instantly via modern APIs and AI-ready formats for analytics and automation.
    • Flexibility: Scalable cloud infrastructure can support agile development and optimize TCO across workflows.
    • Economics: Lower data processing costs and allow experimentation at any scale without excess resource use.
    • Strategy: How will FMIs tie model performance to return on investment? Business alignment on talent, resources and outcomes is critical.
    Learn More

    In his commentary, Hamilton walks through the key steps to data preparation, as well as what lies beyond the technical concerns, including:

    • Reliability: Ensure transparent, verifiable processes with clear audit trails for trusted data.
    • Accessibility: Access historical datasets instantly via modern APIs and AI-ready formats for analytics and automation.
    • Flexibility: Scalable cloud infrastructure can support agile development and optimize TCO across workflows.
    • Economics: Lower data processing costs and allow experimentation at any scale without excess resource use.
    • Strategy: How will FMIs tie model performance to return on investment? Business alignment on talent, resources and outcomes is critical.
    Learn More ->
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