In the modern advertising landscape, two forces stand out as major disruptors: privacy and artificial intelligence.
Although AI's long-term impact will extend far beyond advertising, the immediate imperative for all organizations is clear: consumer data handling must prioritize privacy.
Before we dive into data clean rooms, let's briefly revisit how today's data privacy landscape came to be.
Governments Intervene to Protect Consumer Privacy
The rise of the Internet allowed organizations to start collecting consumer data at unprecedented scale—more efficiently than ever before, but often with little regard for how that data was collected in the first place.
It wasn't until 2016 that a government took decisive action to address growing consumer privacy concerns. Europe led the charge by introducing the General Data Protection Regulation (GDPR), its first comprehensive privacy law.
In the United States, California followed suit in 2018 with the California Consumer Privacy Act (CCPA), strengthening it in 2020 to further protect consumers—and impose stricter rules on businesses.
The snowball effect is real: More states, including Colorado, Connecticut, Florida, Montana, Oregon and Utah, have recently implemented their own privacy regulations, with others poised to join the trend.
Globally, the movement is unstoppable. Data protection and privacy laws are now present in 71% of countries, according to the United Nations Conference on Trade and Development, and new legislation continues to roll out and evolve.
The Tech Industry Responds With Privacy-Protection Initiatives
In addition to government regulations, large tech companies have been rolling out their own privacy-focused initiatives in recent years. Among the most controversial and widely discussed efforts is the ongoing shift around third-party cookies, an element that has been the backbone of the advertising industry since the 1990s.
After years of announcements and delays, Google has opted to maintain third-party cookies in Chrome for now—no immediate cookie-apocalypse—but tighter restrictions on access and usage are expected in the future, including controls, such as user consent.
According to eMarketer, up to 87% of web traffic could soon be freed from third-party cookies once Google's consent-based solution rolls out and Microsoft eliminates third-party cookies in its Edge browser. Meanwhile, major browsers, such as Apple Safari and Mozilla Firefox, have already made third-party cookies inaccessible.
But it's not just about cookies. Both Google and Apple continue to roll out consumer privacy initiatives. For instance, Apple's App Tracking Transparency (ATT), introduced in 2021, requires apps to obtain explicit user consent before collecting device identifiers for advertising purposes.
Ultimately, these changes are transforming the entire ecosystem of advertising "currencies."
The Genesis of Data Clean Rooms
The emergence of data clean rooms can be traced back to Google's decision to discontinue sending log-level data to advertisers around the time the first consumer privacy regulations were being introduced.
Those logs are vital for campaign performance analysis. Without them, advertisers operate with limited visibility, hindering optimization efforts.
Google's response was Ads Data Hub, a platform enabling campaign analytics and reporting in a privacy-preserving environment. That solution, initially termed "next generation insights and reporting," laid the groundwork for data clean room technology.
The catch? Advertisers could no longer directly see or extract the log-level data. Instead, the platform provided a privacy-preserving environment for data analysis.
That new approach would eventually become data clean room technology.
What Is a Data Clean Room?
It's hard to say exactly what causes a specific technology category to take off, but one sign it's gaining traction is when it gets its own widely recognized acronym. Enter data clean rooms, or DCRs.
The concept behind data clean rooms stems from the same challenge Google addressed with Ads Data Hub: enabling data collaboration between two parties without exposing the underlying data.
First-party data is among the most valuable assets an organization owns, hence the sensitivity around making it accessible. Still, there are critical scenarios where analyzing data sets owned by different parties is essential.
Data clean rooms enable a secure, controlled environment that allows multiple organizations—or even business units within a single organization—to collaborate on sensitive or regulated data without compromising privacy.
A key component of this configured protection is the use of privacy-enhancing technologies (PETs), including methods such as differential privacy, aggregation and projection policies, and synthetic data generation.
Who Are Data Clean Rooms for, and What Are the Common Use Cases?
As noted earlier, data clean rooms initially gained traction in the advertising industry, particularly for measuring ad campaign performance without requiring the publisher to give direct access to granular data.
Over time, the scope of collaboration expanded, involving various stakeholders with different roles in advertising initiatives:
- Brands focus on acquiring new customers and driving revenue through paid advertising.
- Publishers and media networks aim to monetize their data and ad inventory.
- Agencies support advertisers and publishers with campaign execution and strategy.
- Tech vendors and data providers sell data, identity solutions, and services such as integrations within the ad ecosystem.
Through the partnerships formed among those stakeholders, typical advertising collaboration use cases now include the following:
- Data enrichment and identity: Partners can enhance first-party data and increase addressability.
- Strategic planning: Advertisers can decide where to spend ad budgets and identify the most relevant audiences.
- Campaign activation: Consumers can be reached through direct or partner-supported channels.
- Measurement and optimization: Organizations can understand channel impact on conversions and refine their media spend.
Advertising is just the starting point for proving the value of this technology. As industries continue to recognize the benefits of secure, privacy-preserving data collaboration, we can expect broader industry adoption in the coming years. Here are two prime examples:
- Healthcare: The industry is accelerating drug research and development by enabling secure data analysis between labs and healthcare facilities without exposing sensitive information.
- Financial services: Organizations are accelerating fraud detection and improving credit scoring models while safeguarding customer data.
Data Clean Rooms Compared to Other Technologies
A common misconception is that data clean rooms are the same as data-sharing technologies.
Secure data-sharing solutions allow data owners to share their data sets with specific controls in place. The objective of data sharing is to provide access to the granular underlying data—a direct contrast to the purpose of data clean rooms, which are designed to prevent such access—while enabling data analysis.
Another technology category often compared to data clean rooms is the customer data platform (CDP). Although both rely on first-party data to deliver value, the similarities end there. CDPs focus on making a brand's first-party data accessible for marketers and advertisers to orchestrate personalized customer experiences. However, CDPs lack the tools and measures needed to facilitate secure collaboration with external data owners.
How a Data Clean Room Works
Once a collaboration agreement is established between two or more parties, a data owner—referred to as the "data provider"—sets up a clean room environment. The data provider determines what data is accessible within the clean room and specifies the activities permitted on those data sets, such as audience overlap analysis or lookalike modeling.
Each party involved in the collaboration retains full control over their data sets at all times. They can decide to grant or revoke data access as needed, ensuring their data remains governed and under their ownership.
After the data sets are made accessible within the clean room, a matching process between them is required. Some data clean room technologies enforce the use of a specific identifier as the matching key, while others are agnostic, allowing the collaborators to agree on the matching criteria of their choice. Successful collaboration relies on an exact match of values for a designated data point (for example, a specific field) between the data sets.
Collaboration in a clean room often concludes once the desired insights are obtained. However, in some scenarios, the clean room can enable activation of the resulting data set to a permitted channel.
Data Clean Rooms Are Part of a Larger Privacy Strategy
Although data clean rooms facilitate secure data collaboration, it's essential to remember that privacy isn't achieved by deploying a single piece of technology. True privacy requires a comprehensive strategy that starts with the consumer.
If an organization wants to collaborate on data with other parties, obtaining consumer consent is nonnegotiable. To secure consent, organizations must prioritize transparency and ensure there's a clear value exchange. Consumers today are increasingly aware of the value of their data, and they are far less likely to share it without understanding what they're getting in return.
Even with the advanced privacy and security technologies provided by data clean rooms, organizations need to establish robust data governance practices. Those practices should govern every activity involving data access and usage to ensure compliance and maintain trust.
Learn more about how advertisers are using Snowflake Data Clean Rooms for privacy-preserving data collaboration.
