An institutional mandate is, in effect, a high-stakes job description. When a large organization such as a pension fund decides to invest a substantial sum, it does not simply “buy stocks.” It defines a specific set of rules and objectives for how the money should be managed, then hires an investment firm to follow them. Winning that work is a sales process, and it is getting harder.
Asset-management teams face mandates that open and close faster than ever, growing consultant influence, and intensifying competition for every opportunity. A firm that cannot pitch quickly and precisely risks losing the deal. Yet many sales teams are slowed by information scattered across disconnected systems, which sends them down unproductive paths while the best opportunities go unnoticed. Compounding the problem, fresh market data has traditionally required technical teams to wait weeks for files to be transferred and cleaned. A new distribution of Nasdaq eVestment data on the Databricks Marketplace is aimed squarely at that bottleneck.
What the Databricks Marketplace listing provides
Nasdaq eVestment data is now available through Delta Sharing on the Databricks Marketplace, according to Databricks. Because the data is delivered via Delta Sharing, it lands in a customer’s lakehouse instantly and without duplication, with standard identifiers intact, ready to join with existing CRM data, internal performance records, and client-interaction history. Daily updates on new mandates, consultant sentiment, and investor activity flow into the same tables, while lineage, versioning, and access controls are preserved through Unity Catalog. In practice, market intelligence becomes available for analytics and AI use cases in hours rather than after a multi-week data-engineering cycle.
What it enables once the data is joined
Combining Nasdaq eVestment data with internal CRM records, meeting notes, and product-performance data unlocks several capabilities without standing up new systems or copying data:
- Automated mandate discovery with Next Best Action scoring. Nasdaq eVestment’s Next Best Action (NBA) dataset ranks open institutional mandates on a 0-to-5 scale according to how well each opportunity fits a firm’s strategy and historical win patterns. The underlying model weighs factors such as performance versus incumbents and consultant sentiment, and the scores can be matched with CRM and relationship data in Databricks SQL and AI/BI dashboards to give teams a real-time view of fit, prioritization, and coverage.
- AI-assisted meeting intelligence. Tools such as Databricks Genie and an AI knowledge assistant can synthesize an investor’s priorities, mandate requirements, performance gaps, and a firm’s strengths into concise briefing documents drawn from governed, auditable data. A delivery leader might ask which opportunities best fit the firm and receive a ranked list with next-step recommendations.
Why it matters
The practical value comes down to speed and context. Live data lets a firm identify well-matched mandates as they emerge and respond before competitors, acting on intelligence in hours rather than days. It also meets asset managers where they already work: many already run analytics and AI on Databricks, so external data that arrives natively through Delta Sharing avoids yet another integration project. Databricks states that automating opportunity scoring with the NBA dataset can cut manual research time by more than 80 percent, though, as with any vendor figure, results will vary by firm and workflow.
Example workflows
Several patterns illustrate how teams might use the data:
- Integrate sales intelligence across sources. Combine Nasdaq eVestment data with a CRM such as Salesforce, Dynamics, or HubSpot, internal performance data, and client meeting notes for a single, governed view of the opportunity landscape.
- Automate opportunity scoring. Trigger an AI-powered workflow using the Next Best Action dataset to rank mandates by win probability and surface where the firm is the strongest fit.
- Prepare for client meetings with AI agents. Query Databricks Genie for a summary of a prospect’s current allocation, open mandate requirements, and how the firm’s strategy compares, and receive a briefing in seconds.
- Scale securely. Use Unity Catalog and MLflow for lineage, interpretability, and compliance with data-licensing and privacy requirements.
Limitations and what to watch
The promise is real, but several caveats apply. The headline efficiency figure comes from the vendor and assumes a firm already has clean CRM and performance data to join against; organizations with fragmented or poorly governed internal data will see less benefit until that groundwork is done. NBA scores and AI-generated briefings are decision-support tools, not decisions: a 0-to-5 fit score reflects historical patterns and can misjudge a novel opportunity, and AI summaries can omit nuance or contain errors, so human judgment remains essential before acting on them. Access also depends on a commercial data license with Nasdaq, and the approach assumes a Databricks-centric data platform, which is a poorer fit for firms standardized on other stacks. Finally, regulatory and privacy obligations around client and investor data do not disappear because the pipeline is simpler; governance through Unity Catalog supports compliance but does not replace it.
The bottom line
Putting Nasdaq eVestment data on the Databricks Marketplace is less about a new dataset than about how it is delivered: live, governed, and ready to join with a firm’s own records, without the weeks of plumbing that used to stand between data and decisions. For asset managers already invested in Databricks, that combination can shorten the path from market signal to client conversation, provided the internal data, licensing, and human oversight are in place to use it well.
