Data Clean Rooms
A data clean room is a secure, privacy-controlled environment where two parties can match and analyze their data together without either exposing raw, user-level records to the other.
Key takeaways
- A clean room lets two parties combine data without sharing raw user records.
- Only aggregated, privacy-safe outputs leave the environment.
- It's used for measurement, audience overlap, and activation after cookie loss.
- Walled gardens and CTV publishers use clean rooms extensively.
How clean rooms work
Each party uploads data into a governed environment that permits matching and analysis "” say, overlapping a brand's customers with a publisher's audience "” but restricts outputs to aggregated results. Neither side sees the other's raw records, which lets sensitive first-party data be joined within privacy constraints.
Why they matter now
As third-party identifiers disappear, clean rooms became the primary way to connect a brand's first-party data with a publisher's audience for targeting and measurement. They are central to how walled gardens and CTV platforms let advertisers use their own data without releasing user-level information.
| Purpose | Privacy-safe data collaboration |
|---|---|
| Inputs | Two parties' first-party data |
| Outputs | Aggregated, non-identifying results |
| Common users | Walled gardens, CTV publishers, brands |
Frequently asked questions
What is a data clean room used for?
For measurement, audience overlap analysis, and building targetable audiences by matching two parties' first-party data without either exposing raw user-level records.
Why are clean rooms important after cookie deprecation?
Because they let brands and publishers connect their first-party data for targeting and measurement without third-party cookies or sharing sensitive raw data.