8 Data clean rooms

 

This chapter covers

  • Privacy-first data sharing with data clean rooms
  • Navigating privacy regulations in a cookieless future
  • Clean room benefits: security, compliance, and multiparty insights
  • Types of clean rooms: walled gardens and neutral hubs
  • Secure collaboration, flexible application integration, and adherence to industry standards

We’re just scratching the surface of what’s possible with data collaboration in Snowflake. As more organizations embrace the cloud and data sharing, we’ll see an explosion of new ideas and applications that we can’t even imagine today.
—Sridhar Ramaswamy, CEO of Snowflake

As privacy regulations tighten and third-party cookies lose their prominence, businesses must adapt to new methods of collecting, analyzing, and activating data. Data clean rooms—secure, privacy-first environments—offer a viable approach, enabling meaningful collaboration without compromising sensitive information. This chapter introduces the foundational concepts behind data clean rooms, examines their role in fostering effective partnerships, and highlights the privacy-enhancing techniques that preserve confidentiality. It also considers core use cases, from audience targeting and refined measurement to advanced machine learning.

8.1 Introduction to clean rooms

8.1.1 Data clean rooms of walled gardens

8.1.2 Data clean rooms of AdTech providers as intermediaries

8.2 Clean rooms for matching and activation

8.2.1 Example: Brand and publisher collaboration

8.3 Clean rooms for compliance and fraud prevention applications

8.3.1 Suppressing fraudulent customers

8.3.2 Supporting anti-money laundering (AML) compliance

8.4 Clean rooms for measurement

8.5 The future of clean rooms

Summary