What Is a Data Clean Room? How It Works and Use Cases

Sharing data while adhering to privacy regulations has always been challenging. But by using distributed data clean rooms, it’s now possible to collaborate with data in a secure way that aligns with privacy rules. The capabilities of distributed data clean rooms are especially beneficial as advertisers and the media industry face signal loss due to reduced access of key data signals such as cookies or device IDs. Data clean rooms allow organizations to manage, de-identify and share data effectively. Let’s explore what a data clean room is and how it works, the benefits you can experience with a data clean room, and how companies use data clean rooms for business growth.

A data clean room is a secure and controlled environment that allows multiple companies, or divisions of a company, to bring data together for joint analysis. Internal clean room guidelines can be established to keep data handling and sharing aligned with core privacy regulations such as GDPR, HIPAA and the CCPA. In the data clean room, personally identifiable information (PII) can be anonymized.

The most popular use case of data clean rooms is to link anonymized marketing and advertising data from multiple parties for attribution. Data clean rooms can be set up to prevent the exposure of data that could identify specific users, thereby helping clean room users comply with privacy requirements.

Data clean room configurations control what data comes in, how it can be joined with other data in the clean room, the types of analytics each party can perform on the data and what data can leave. Data clean rooms may permit PII loaded into the clean room to be secured and encrypted. Data clean rooms generally grant data owners full control over their data in the clean room, while approved partners can get a feed with anonymized data.

It’s important to distinguish between traditional data clean rooms and distributed data clean rooms. In traditional data clean rooms, all data is stored in a single physical location, limiting how the data can be shared. With the developments of cloud technology, distributed data clean rooms eliminate the need to move data from one local storage location to another, since the data can live in the cloud. This allows each partner to control their own data while enabling governed analytics with another partner, or even with multiple other partners, simultaneously.

Data clean rooms offer key advantages for advertisers, media companies and retailers, including:

Data clean rooms enable media companies and publishers to combine their audience data with partners’ data without exposing personally identifiable information, while advertisers can improve attribution tracking.

Data clean rooms facilitate the creation of custom audiences for advertising platforms, allowing marketers to optimize their ad targeting.

Organizations can use data clean rooms to perform in-depth analysis on combined data sets, gaining insights into customer behavior, segmentation and customer lifetime value.

Let’s look at three specific use cases for data clean rooms.

The omnichannel customer journey is complex, and it rarely starts with a brand’s advertisement. For example, if a consumer is planning an upcoming purchase of a kitchen appliance, the journey is likely to start with online review sites. A reviews site collects top-of-funnel data that would be invaluable to the appliance brand. With a data clean room that can manage PII, the site owner could create a privacy-preserving third-party data product.

Data clean rooms allow retailers and CPG companies to collaborate with brands that advertise with them. For example, a retailer can share transaction data in a privacy- and governance-minded manner to provide insights into conversion signals and enable better targeting, personalization and attribution.

A real-world example of an organization using a data clean room to achieve business growth is Snowflake customer Merkle.  With Merkury Clean Rooms, powered by Snowflake Data Clean Rooms, Merkle’s clients and partners combine data from multiple sources without gaining unauthorized access to sensitive data.

A growing number of Merkle’s clients and partners have adopted Snowflake, which opens up new opportunities to collaborate. “When I joined the customer-facing side of our business three years ago, we were frequently having to consider competing offerings,” says John Gajewski, Senior Vice President of Architecture at Merkle. “Now the conversation has pivoted a lot to using Snowflake first.”

It used to be the case that working together securely across organizations, clouds and regions required moving data — and introducing unwanted risk. Merkle now can use Snowflake Secure Data Sharing for live access to data for Merkle’s clients and partners with reduced extract, transform and load (ETL) processes or SFTP. And Snowflake Data Clean Rooms — on which Merkle’s Merkury Clean Rooms are built — allow multiple parties to have appropriate permissions and can analyze data without exposing PII and other sensitive information.

Snowflake enables companies to securely and privately share data within data clean rooms for efficient, real-time analytics and deeper analysis. Participants can "list" the data they want to share — making it visible only to authorized parties — without necessarily needing to move the data.

Essentially, Snowflake provides a platform which companies can configure to enable data to be securely accessed and analyzed by selected partners while preserving data privacy and security.