data clean room

What is a data clean room?

A data clean room is a technology service that helps content platforms keep first person user data private when interacting with advertising providers.

The concept of a data clean room is intended to be a data-focused equivalent to a physical clean room, with the goal of having a pristine environment where technology can't be contaminated by outside influence. Instead of being concerned with contamination by physical elements, the primary concern of a data clean room is keeping user data isolated and private.

A data clean room provides aggregated and anonymized user information to protect user privacy, while providing advertisers with non-personally identifiable information (non-PII) to target a specific demographic and for audience measurement.

Content platforms collect user information as part of day-to-day operations. Sharing that user information directly with advertisers is increasingly complex and difficult, with the continuing need to protect user privacy amidst compliance regulations and privacy laws such as the European Union's General Data Protection Regulation (GDPR) and mandates in the U.S., including the California Privacy Rights Act (CPRA). Privacy regulations and the ongoing need of advertisers to better target users has helped fuel the rising demand for data clean rooms. The ability to use cookies to track user activity is also ending, which is increasing the need for data clean rooms.

How do data clean rooms work?

Most modern data clean rooms work in software as a service (SaaS) models in the cloud, enabling content providers and advertisers to collaborate.

The content provider, which holds first-person user information, uploads data to the data clean room. That data comes from different systems, including ecommerce, logging and customer relationship management (CRM). The user data is encrypted on its route from the provider to the data clean room, where it's anonymized and aggregated into user and demographic groups. The transmitted data remains encrypted, making it impossible for anyone in the data clean room to access PII.

Approved partners and advertisers are granted access to the anonymized data by the content provider. Approved providers can access data as a continuous data feed or in the data clean room platform. The protected data can be used by partners and advertisers for data analytics for audience measurement and engagement.

Benefits of data clean rooms

Data clean rooms provide numerous benefits to content providers, marketers and advertisers, including:

• Regulatory compliance. Among the primary reasons for using a data clean room is to better understand users, while still being compliant with privacy regulations, such as GDPR.
• Trend data. Data clean rooms provide aggregate user information that gives visibility into trends across groups of users, demographic and industry segments.
• User segmentation. With the aggregated user information, advertisers and marketers can build customized audience groups for better user segmentation.
• Data analytics. A key feature of data clean rooms is the ability to analyze aggregated data to better understand user behavior and activity.
• Security. With user privacy at its foundation, data clean rooms offer a secure location to access and share aggregate user data that's useful for platforms to monetize and for advertisers to target.

Challenges of data clean rooms

The promise of data clean rooms is that user information can be anonymized, but there can be some challenges, such as the following.

Data interoperability. Among the major data clean room providers are large hyperscaler networks, including Google and Facebook. A key challenge with those providers is that they can be limited to only providing aggregated user information for their own platforms, an approach known as a walled garden approach. With the single platform approach, it's generally not possible to combine data from one data clean room platform with another.

Data quality. Without direct access to first-party user data, it's incumbent on the content provider to deliver high-quality data. However, it's not always possible for users of data clean rooms to independently verify that data quality is high or even accurate.

Lack of standardization. Not only is there a lack of interoperability across different data clean rooms, there's also a lack of standardization. As such, formats and methodologies used to aggregate and anonymize the data and access are variable across providers.

Data clean room use cases

Data clean rooms support multiple use cases to help support the usage of user data in a privacy compliant approach, including the following:

• Data activation. First-party data collected by platforms can be "activated" in a data clean room, meaning it can be used by third parties without the need for direct user attribution.
• Collaboration. Data clean rooms also enable varying degrees of collaboration between providers and potential users of the data.
• Audience trends. By looking at the aggregated data provided by data clean rooms, a common use case is to gain better insight into activity trends from a particular user audience.