Jun 25 2026
Data Analytics

Databricks AI + Data: How Nasdaq's Unified Data Lakehouse Accelerates Financial Innovation

Nasdaq consolidated enterprise and market data on Databricks to improve governance, speed product development and support AI initiatives.

For an organization that sits at the center of global capital markets, data is both a critical operational asset and a strategic differentiator. At the Databricks Data + AI Summit, Nasdaq executives detailed how the company has modernized its data architecture, creating a unified data lakehouse that supports everything from executive decision-making to new AI-powered financial products.

During the session, Angie Ruan, CTO of Nasdaq’s Capital Access Platforms division, and Edwin Aoki, senior vice president of global technologies for the company, described a multiyear effort to consolidate data assets across Nasdaq onto Databricks.

The initiative was driven by a familiar challenge: Different business units had built their own pipelines, governance models and data repositories over time, making it difficult to share information consistently across the organization.

Click the banner below to get finance insights delivered to your inbox weekly.

 

To address this, Nasdaq standardized on Databricks technologies, including Delta Lake, Unity Catalog and Databricks’ broader lakehouse architecture. The goal was not simply to centralize data but to create a common platform that could be shared across business units while maintaining appropriate controls and governance.

“We really have to have Databricks,” Ruan said, describing the company's effort to move toward a common catalog, governance framework and control structure.

Aoki said Nasdaq recently completed a two-year initiative to bring together data from across the enterprise, including product information, sales data, HR systems, customer relationship management platforms and financial reporting systems.

The result is what executives described as a single source of truth for corporate data. That foundation supports a variety of internal applications, from sales intelligence tools to executive dashboards used by senior leadership.

One example is Beacon, a data platform that provides executives and board members with performance metrics, business unit insights and financial analysis. According to Aoki, Beacon has become a central source of information for Nasdaq's leadership team.

“Our CEO and CFO look at this every single day,” he said.

DISCOVER: Turn data into insights and accelerate artificial intelligence initiatives.

Nasdaq’s Expanding Financial Data Business

The company's Databricks strategy extends well beyond internal analytics.

Ruan highlighted Nasdaq’s index business as an example of the scale and complexity involved in managing financial data. Nasdaq operates more than 10,000 indexes and incorporates data from more than 50 markets worldwide, alongside pricing information, foreign exchange rates and company fundamentals.

Those workloads require highly accurate, reproducible calculations and support both batch and near-real-time processing.

Aoki said Databricks is embedded throughout that environment. Nasdaq uses Lakeflow and Delta Live Tables to manage data ingestion and transformation, Databricks SQL for research and index development, and Unity Catalog to provide governance and visibility across data sets.

The platform has also accelerated product development. Ruan noted that dozens of new indexes were launched on the modernized architecture over the past year, demonstrating how technology investments can directly support business growth.

Beyond operational efficiency, Nasdaq sees value in making data easier to discover, combine and reuse.

“What we're trying to do is make that data more valuable,” Aoki said.

By reducing the cost and complexity of working with data, teams can iterate more quickly and create new products faster, he added.

Click the banner below to read the recent CDW Cloud Computing Research Report.

 

AI Strategy Begins With Data Strategy

The final phase of Nasdaq's transformation focuses on AI.

Both executives emphasized that effective AI initiatives depend on strong data foundations. Rather than treating AI as a separate technology stack, Nasdaq is building on the governance, cataloging and data sharing capabilities already established through its Databricks environment.

That strategy is particularly important within the company’s Capital Access Platforms business, which supports institutional investors and asset managers. The platform encompasses data on more than 110,000 public- and private-market strategies and funds, representing more than $90 trillion in institutional assets.

Many of those data sets include unstructured content such as PDFs, reports, charts and manager commentary. Ruan said Nasdaq has developed a patent-pending approach for transforming that information into AI-ready data that preserves context and improves the quality of AI analysis.

By combining Databricks technologies with its proprietary data assets, Nasdaq is working to enable more sophisticated investment research and decision-making workflows.

According to Aoki, the approach has delivered measurable gains, including improved decision accuracy and significantly greater token efficiency for AI workloads.

For Nasdaq, the lesson is straightforward: AI success starts with data modernization. By consolidating data assets, establishing governance and creating a shared enterprise platform, the company has built a foundation that supports both current business needs and future AI innovation.

As financial institutions look to scale AI initiatives of their own, Nasdaq's experience offers a clear example of how a unified data strategy can become the foundation for long-term competitive advantage.

sanjeri/Getty Images
Close

New Research from CDW on Workplace Friction

Learn how IT leaders are working to build a frictionless enterprise.