Nasdaq eVestment Data Now on Databricks Marketplace

· Source: Databricks · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, medium

Summary

Nasdaq eVestment data is now accessible through Delta Sharing on Databricks Marketplace, offering live, query-ready access to comprehensive institutional investor data. This integration allows asset managers to automate mandate discovery using "Next Best Action" (NBA) scoring, which ranks opportunities based on win probability. By unifying third-party intelligence with internal CRM and performance data on the Databricks platform, sales teams can accelerate sales preparation, personalize client engagement, and manage pipelines dynamically. The system provides daily updates on new mandates and investor activity, enabling real-time analytics and AI-powered meeting intelligence through tools like Databricks Genie, which synthesizes investor preferences and mandate requirements into concise briefing documents.

Key takeaway

For asset management distribution leaders seeking to improve sales efficiency and win rates, integrating Nasdaq eVestment data on Databricks Marketplace allows for automated, AI-powered mandate discovery and real-time sales preparation. You can unify disparate data sources to focus on high-probability opportunities, reducing manual research time by over 80% and enabling quicker, more personalized client engagement to secure mandates faster.

Key insights

Integrating institutional investment data with internal systems streamlines mandate discovery and enhances sales efficiency.

Principles

Method

Nasdaq eVestment data is delivered as governed Delta tables via Delta Sharing into a Databricks lakehouse, where it's joined with CRM and performance data for AI-powered analytics and workflow automation.

In practice

Topics

Best for: Executive, Product Manager, AI Product Manager, Data Scientist, Data Engineer

Related on AIssential

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.