Today, most teams achieve this by moving and sharing data across systems and partners. 

 Data is often:

• • • Shared across organizations

    • Translated into common identifiers

    • Processed inside external platforms

 But this comes with tradeoffs:

• • • Higher cost to move and process data

    • Operational complexity across partners

    • Fragmented control over how data is used 

Those tradeoffs aren’t required. They’re a byproduct of how these workflows are typically implemented.

Control Data at the Source

Data is transformed locally to reduce re-identification risk across identifiers and attribute combinations.

This ensures risk is addressed at the point of origin, before it can propagate across workflows. 

Link Data Without Exposing It

Data is matched across partners without exchanging identifiers or relying on shared identity systems.

Matching occurs through coordinated processes that align data without revealing the underlying inputs.

Deliver Data with  Control Intact

Transformed data is delivered in protected form, without reintroducing risk.

Control is preserved through delivery, so outputs remain governed and usable across environments.

Secure Activation

Activate audiences across partners using a coordinated cryptographic protocol for remote matching.

Identifiers never need to be shared, and activation can occur without exposing underlying identity. 

Governed Measurement

Measure performance across partners using data that remains protected and governed throughout the workflow.

Maintain accuracy and continuity without relying on third-party platforms to process or reconcile data. 

Controlled Collaboration

Collaborate with partners with protected workflows that minimize unnecessary data movement and reduce complexity.

Maintain control over how data is used across environments, whether distributed or centralized. 

Real-World Impact