---
title: "From Wall Street to Data Platforms"
description: "Kim Hatton, Databricks’ Global Financial Services Marketing Leader, describes how two decades in regulated financial-services marketing led her toward technology and data-centered strategy. She says financial institutions still need to unlock value while navigating compliance, but their divisions and systems make a unified customer view difficult. Her account points to Unity Catalog’s unified governance and single source of truth for breaking down silos and supporting requirements including GDPR, customer identity, and sensitive workloads. It also describes Lakebase’s separation of compute and storage for faster ML/AI agent experimentation, alongside Genie’s plain-language data analysis, which can reduce work that otherwise takes months and expensive third parties. Hatton connects these tools with faster, more confident marketing and accurate decision-making in regulated workflows, while also describing Databricks’ inclusive, high-energy culture."
---

# From Wall Street to Data Platforms

[Databricks](https://yomu.fyi/company/databricks) · Andrea Fernández, Kim Hatton · Jun 13, 2026

**Type:** Explainer

## Summary

Kim Hatton, Databricks’ Global Financial Services Marketing Leader, describes how two decades in regulated financial-services marketing led her toward technology and data-centered strategy. She says financial institutions still need to unlock value while navigating compliance, but their divisions and systems make a unified customer view difficult. Her account points to Unity Catalog’s unified governance and single source of truth for breaking down silos and supporting requirements including GDPR, customer identity, and sensitive workloads. It also describes Lakebase’s separation of compute and storage for faster ML/AI agent experimentation, alongside Genie’s plain-language data analysis, which can reduce work that otherwise takes months and expensive third parties. Hatton connects these tools with faster, more confident marketing and accurate decision-making in regulated workflows, while also describing Databricks’ inclusive, high-energy culture.

## Context

Financial institutions must unlock value from data while navigating compliance, but their many divisions and systems make it difficult to establish a unified customer view and scale AI across regulated workflows.

## Approach / What changed

The account describes Unity Catalog’s unified governance and single source of truth, Lakebase’s separation of compute and storage for ML/AI agent experimentation, and Genie’s plain-language data analysis for faster access to insights.

## Takeaways

- Unity Catalog is presented as a way to break down financial institutions’ data silos while supporting GDPR, customer identity, and sensitive workloads.
- Lakebase separates compute and storage to enable faster ML/AI agent experimentation, letting agents process data and synthesize findings for continuous business-process improvement.
- Genie lets users analyze data in plain language and uncover context and answers in minutes, instead of relying on processes that can take months and require expensive third parties.

**Tags:** [AI](https://yomu.fyi/topic/ai), [Lakebase](https://yomu.fyi/topic/lakebase), [Unity Catalog](https://yomu.fyi/topic/unity-catalog)

- Source: [Databricks](https://www.databricks.com/blog/wall-street-data-platforms)
- Source URL: https://www.databricks.com/blog/wall-street-data-platforms
- Ingested by Yomu: 2026-08-30T17:02:53.378Z

[Read original post](https://www.databricks.com/blog/wall-street-data-platforms)