Problem statement

Statement 1:

We want to build an interoperable Data Clean Room that operates between multiple data warehouses and make sure that our architecture is trustable.

Statement 2:

We want to build a Data Clean room orchestrator that will orchestrate clean room services with keeping the Publisher-Advertiser use case in mind.

Statement 3: (Implementing this for now)

We want to create a privacy-first data clean room solution where data movements from sources are possible and trustworthy enough for customers to use.

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Background:

What is a Data Clean Room?

A data clean room is a secure environment where organizations can collect data from multiple sources and combine it with their first-party data. Doing so allows marketers to leverage large, aggregated datasets of consumer behavior to provide insight into critical factors like performance, demographics, campaigns, etc.

Data clean rooms allow companies to extract value from aggregate datasets sourced from multiple parties while prioritizing user privacy and maintaining strict security measures.

Why do we need Data Clean Rooms?

Data clean rooms allow separate organizations or teams to share and analyze data without compromising data security or privacy. There are several reasons why data clean rooms are needed:

1. Data security and privacy: Data clean rooms provide a secure and controlled environment for data sharing and analysis, ensuring that sensitive data is protected from unauthorized access or breaches.
2. Compliance: Data clean rooms can help organizations comply with relevant regulations and standards related to data security, privacy, and governance.