As third-party cookies lose prominence, advertisers need ways to compare their customer data with platform data without broadly exposing either dataset. A walled garden is a closed ecosystem controlled by one platform. In advertising, its data clean room lets businesses collaborate with that platform, but restricts access and use outside its ecosystem. section 8.0 section 8.1.1
A typical walled garden clean room follows these boundaries:
- Each data partner exchanges data directly with the platform, often using standardized files containing customer identifiers. section 8.1.1
- Common identifiers, often hashed, support matching and analysis; partners do not exchange data directly with one another. section 8.1.1
- Partners receive aggregated, anonymized insights rather than unrestricted access to the underlying data. section 8.1.1 section 8.1
Platform-specific collaboration
Google Ads Data Hub, Amazon Marketing Cloud, and Meta’s data solutions are examples. Google Ads Data Hub links a partner’s BigQuery project with Google’s log data through an API. This gives the partner a way to analyze activity within Google’s environment, not a general-purpose route to combine every advertising platform’s data. section 8.1.1 section 8.1.2
Table 8.1 Benefits and limitations of walled garden clean rooms
| Aspect |
Benefits |
Limitations |
| Data and tools |
Access to rich, platform-specific datasets and native analytics |
Data stays locked within the platform; limited use outside the ecosystem |
| Integration |
Seamless activation of insights (e.g., running ads directly) |
Potential overreliance on one provider’s infrastructure and tooling |
| Security |
Platform-managed security and compliance controls |
Must trust the platform’s handling of hashed but still raw-ish data |
| Control and ownership |
Efficient workflow if you are already a major advertiser on that platform |
Data partners may have less visibility into how matching is done |
| Interoperability |
Straightforward if campaigns live primarily on one platform |
Challenging to combine data or insights across multiple platforms |
| Scalability |
Often large scale due to the platform’s global reach |
Additional scale means deeper lock-in and less cross-platform freedom |
Compare the practical trade-off: platform datasets, native analytics, and direct ad activation come with limited portability, less visibility into matching, and dependence on the provider’s controls. section 8.1.1
Choose this model when most campaigns already run on one platform and its native data and tools meet the need. If collaboration must span platforms, a neutral clean room offers more flexibility, potentially at additional cost. In either case, weigh access and convenience against privacy, consent, security, and trust in the provider. section 8.1.1 section 8.1.2 section 8.1
