How Stagwell built privacy-safe ID matching on Databricks
How Stagwell built privacy-safe ID matching on Databricks
https://www.databricks.com/blog/how-stagwell-built-privacy-safe-id-matching-databricks
Publish Date: 2026-06-18 11:56:00
Source Domain: www.databricks.com
The identity matching problem brands face today
Brands invest heavily in building first-party data assets, including purchase histories, CRM records, loyalty programs,and website interactions. That data is fragmented across systems and difficult to activate across channels. However, first-party data alone only tells part of the story.
To build complete audience profiles, brands need to match their records against identity providers’ spines for cross-channel identity graphs spanning email, device IDs, cookies, and offline touchpoints.
The traditional approach is painful. Brands export customer records to a third-party platform, the identity provider runs their matching algorithms, and results come back days later. Every step introduces risk: data leaves the brand’s secure environment, PII travels across networks, and compliance teams must review data-sharing agreements that can take weeks to negotiate.
At the same time, privacy regulations and platform restrictions have made:
- Third-party cookies unreliable
- Data sharing risky
- Identity stitching more complex
This creates a fundamental gap: Brands have data but lack the ability to connect it to a unified identity layer safely
To bridge this, brands need to:
- Match their data against a comprehensive identity graph
- Enrich it with additional signals and attributes
- Do so while protecting raw user-level data
The Marketing Cloud, a Global Marketing Services Agency, a Stagwell company, experienced this friction firsthand across their brand clients. They pushed for a better model: one where brands could access Stagwell’s identity matching capabilities without ever sending their raw data outside their own infrastructure.
How Marketplace Apps change the distribution model
Traditional clean room implementations are high-touch, engineering-heavy, and can be slow to deploy.
Databricks Marketplace Apps flip the traditional data-sharing model. Instead of “send us your data and we will process it,” the model becomes “install our app and it runs…