What Are Data Clean Rooms, & How Do They Work?

A data clean room is a secure, protected environment where different organizations can without actually seeing each other’s raw information.

Instead of directly sharing customer information, participating organizations upload their data into this secure space. The clean room then acts as a neutral zone where specific, pre-approved analysis can take place under strict controls.

Say a retailer and a brand both want to better understand their shared customers. Rather than exchanging customer lists (which could violate privacy laws), they use a data clean room.

They discover shared insights about their audiences, such as reach, frequency, and purchasing behavior, but only at an aggregate level, e.g., “users who perform action X are likely to respond to offering Y.”

Through this approach, the retailer might learn that customers who buy a certain brand tend to shop more frequently, while the brand discovers the best times to run promotions—all without either party seeing the other’s customer details.

While still a relatively new approach (Google launched the first major data clean room solution in 2017), data clean rooms are rising in popularity for several compelling reasons.

Enables responsible, secure insights

First, data clean rooms solve a fundamental dilemma: unlocking the value of data partnerships without compromising user privacy.

As companies gather more customer data, they face mounting pressure to use it responsibly. Clean rooms offer a way forward, enabling more strategic business decisions while securing sensitive information.

Ensures compliance

The rise of has made clean rooms particularly relevant. With in Europe and in California raising the stakes for data handling, organizations need new ways to analyze customer behavior without violating privacy laws.

Data clean rooms provide this crucial legal compliance while still allowing you to carry out meaningful analysis.

Addresses the third-party cookie problem

The way we monitor online behavior is changing. As and device tracking becomes more restricted, brands and publishers need new ways to understand their audience.

A clean room fills this gap by enabling privacy-safe collaboration that benefits all parties—advertisers get better , publishers maintain revenue, and customers keep their privacy intact.

Builds user trust

Beyond compliance, data clean rooms also build trust. Customers today are increasingly aware of how their data is used and shared.

By using clean rooms, companies signal their commitment to responsible data practices. This simple act helps maintain customer confidence, which is particularly vital in an era of frequent data breaches and privacy scandals.

Encourages a new way of thinking about data privacy

Perhaps most importantly, data clean rooms challenge the outdated assumption that organizations must choose between data utility and privacy.

Clean rooms prove that you can have both the right technology and . This shift in perspective .

Data clean rooms function through a careful sequence of steps designed to protect privacy while enabling analysis.

Data ingestion

First, participating organizations upload their data into a secure environment. This could be (from CRMs, websites, or apps) or second-party data from partners. Each dataset is immediately encrypted and stripped of any personally identifiable information (PII), making it impossible to identify the individual customers.

Matching

Next, the data clean room performs data matching. Instead of directly comparing raw customer information, the system uses sophisticated matching, sometimes called “privacy-preserving joins.”

These methods work by finding matches at the user level using common identifiers, such as hashed emails or mobile IDs. When direct matches aren’t possible, advanced techniques can help connect the dots.

Analytics

The data clean room then processes the combined data to uncover:

• Audience overlaps and intersections
• patterns
• Propensity scoring and predictions

However, clean rooms also enforce strict rules about what kinds of analysis can be performed. Users can’t simply query any data they want. Instead, they must use pre-approved analysis templates that ensure all results maintain privacy thresholds.

The results may only show aggregate data for groups of 50 or more customers, never individuals. That said, if all parties consent, you might be able to see data on a user level, too.

Overall, these privacy-safe outputs enable:

• More relevant
• that truly resonate
• campaign measurement
• Deeper performance analysis

Control and governance

Control and governance are built into every step of the data clean room process. Each organization maintains ownership of its data, and all parties must agree on how it can be used.

Detailed audit trails track every analysis to ensure transparency and accountability. When the analysis is complete, participants receive only approved results that meet privacy standards—never the original data.

Data clean rooms come equipped with essential components that help you properly use your data and keep it private. Here are some of the main features.

Privacy controls

Unlike traditional databases, clean rooms build privacy safeguards directly into their architecture. They automatically enforce minimum threshold requirements, prevent individual-level data access, and mask sensitive information. These aren’t just add-on features—they’re fundamental to the system’s operation.

Customizable access rules

Organizations maintain granular control over their data with data clean rooms and can set specific permissions for who can run what types of analysis. This sophisticated system means every query must pass multiple checkpoints before approval. Data can only be used in agreed-upon ways.

Audit and compliance features

Every action within a data clean room leaves a digital footprint. The system keeps detailed logs of all analyses, making it easier to demonstrate how your business is staying compliant with privacy regulations and internal policies, and provides a “paper” trail for any audits.

Real-time privacy checks

Before any results leave the data clean room, they pass through an automated privacy screen. These checks ensure that all output meets predetermined privacy thresholds and data minimization (using only directly relevant and necessary information) requirements. Every analysis is monitored at all times.

You’ll likely come across several “types” of data clean rooms. As this is still a growing area, companies often refer to them differently depending on how they use them.

However, we can generally divide clean rooms into two categories: walled gardens and independent platforms.

Walled gardens

Walled gardens are operated by major tech platforms, such as Google or Amazon, using their vast amount of data and resources.

Although powerful, they’re called “walled gardens” because they typically keep the analysis within their own system.

Key characteristics include:

• Access to rich first-party data from the platform’s users
• Sophisticated measurement tools built from years of data expertise
• Limited ability to export detailed data outside the platform
• Strong integration with the platform’s advertising tools

Google’s is an example of a walled garden (and was actually the first to be commercialized). Advertisers can analyze campaign performance across Google properties, but the insights stay within Google’s environment. This situation creates a trade-off: powerful analytic capabilities but less flexibility in how you can use the results.

Independent platforms

Unlike solutions tied to specific tech giants, independent data clean rooms operate as neutral third parties. They’re built by specialized companies focused solely on secure data collaboration—Habu and Infosum fall into this category. They don’t have a stake in the data but simply facilitate secure analysis.

These platforms typically offer:

• Flexibility to connect with various data sources and systems
• Custom privacy rules that adapt to different industry needs
• More transparent operations since their business model doesn’t depend on data collection
• Freedom from vendor lock-in that often comes with big tech solutions

For example, Infosum provides an independent clean room where retailers and brands can analyze shopping patterns without sharing customer lists. Companies can be given insights without compromising their data independence.

A is where all your company’s personal customer data comes together. It collects and unifies your first-party data from various sources into detailed customer profiles—great for creating a complete view of your customer’s interactions with your brand.

CDPs can do a lot of things, but are mostly best at:

• Building unified customer profiles from your own data sources
• Activating customer segments for marketing campaigns
• Managing customer consent and preferences
• Providing real-time access to customer data for your teams

Data clean rooms are where data from different organizations can “meet” and be safely combined and analyzed. They’re less concerned with building customer profiles and more about discovering insights while keeping the original data private.

Some key differences between CDPs and data clean rooms are that:

• CDPs focus on your data—data clean rooms enable collaboration with external data
• CDPs provide individual-level data access—most clean rooms show only aggregated insights (but some do allow individual-level analysis with proper permissions)
• CDPs help manage customer relationships—clean rooms support privacy-safe
• CDPs integrate with many marketing tools—clean rooms prioritize security and compliance instead

For instance, a retailer might use a CDP to understand how individual customers shop across their website and stores. They’d then use a data clean room when working with a brand to analyze how advertising impacts sales. Both tools serve complementary roles.

Let’s look at some practical applications of data clean rooms and how they work in different industries and scenarios.

Retail and consumer goods

Major use clean rooms to collaborate with brands without jeopardizing their customer data. For example, a grocery store chain might help a cereal manufacturer understand which store promotions drive the most sales. The brand learns what works while the retailers maintain customer privacy and strengthen vendor relationships.

Digital advertising and media

Many publishers and advertisers now use clean rooms to measure campaign effectiveness in a “post-cookie” world. could work with advertisers to understand how many subscribers purchased products after seeing specific ads while keeping viewer profiles private.

Healthcare and pharmaceuticals

Research institutions can study treatment outcomes across different hospitals without exposing patient records. A pharmaceutical company might use a data clean room to analyze prescription patterns across multiple providers to identify unmet patient needs and uphold strict HIPAA compliance.

Financial services

and insurance companies use data clean rooms to detect fraud patterns by combining datasets without sharing sensitive account details. Credit card companies could collaborate with one another to identify suspicious transactions.

Travel and hospitality

Airlines and hotels analyze shared customer behavior to improve their services. An airline may work with a car rental company to enhance their joint loyalty programs based on traveler preferences without showing individual customer profiles.

Cross-industry applications

Data clean rooms don’t only apply to companies working in the same sector. We’re now seeing more examples of cross-industry or cross-team applications.

Supply chain enhancement

Manufacturing, retail, and logistics companies use clean rooms to enhance their supply chains without revealing competitive secrets.

A manufacturer can analyze its production data alongside retailer inventory levels to predict demand more accurately. It might discover that certain weather conditions affect both production efficiency and consumer buying habits, leading to smarter inventory management.

Customer journey mapping

Nowadays, customers rarely interact with just one business to accomplish their goals. Data clean rooms help companies understand these complex .

For instance, a mortgage lender and a real estate listing platform might analyze their combined data to understand the typical homebuying journey. They could identify common patterns, such as how long people browse listings before applying for pre-approval.

Market research and trend analysis

Organizations from different sectors can pool their market intelligence without exposing proprietary information.

Take a shopping mall operator who is working with multiple retailers to understand broader consumer behavior trends. The data clean room could uncover valuable patterns about how different store types complement each other and affect foot traffic.

Product development insights

Companies developing complementary products can better understand user needs through clean room analysis.

For example, a fitness equipment manufacturer might collaborate with a nutrition company to identify which types of exercise equipment correlate with specific dietary preferences. This helps both companies tailor their distinct offerings.

So, if data clean rooms are so beneficial (and also make sense from a regulatory perspective), why aren’t more companies using them?

Certain challenges can help explain why data clean room adoption isn’t yet standard practice. However, as more privacy rules are introduced and third-party data becomes less available, many organizations are finding these hurdles worth overcoming.

Technical complexity

Setting up and maintaining a data clean room isn’t like installing standard software. It requires significant technical expertise and resources.

Organizations often struggle with standardizing their data across different sources, and many lack the specialized skills needed to configure and manage these environments properly.

Cost barriers

The investment to take full advantage of data clean rooms can be substantial. Beyond the platform costs, companies need to factor in staff, training, and ongoing maintenance. For smaller organizations, these costs can be prohibitive, especially when the isn’t immediately clear.

Trust issues

Even with solid security measures, some businesses hesitate to put their valuable data into shared environments.

There’s a persistent fear that combining datasets, even in a secure environment, might somehow expose competitive advantages or sensitive information. This “trust gap” often slows adoption.

Operational friction

Data clean rooms can add complexity to data analysis workflows. What used to be straightforward queries now require multiple approvals and privacy checks.

This additional friction can frustrate teams that are used to having direct data access, leading to resistance from end users.

Limited understanding

Many organizations simply don’t fully grasp what data clean rooms can and can’t do.

This knowledge gap leads to either unrealistic expectations or undervaluation of the technology’s benefits. Without clear use cases and demonstrated value, it’s hard to justify the investment.

The case for data clean rooms is growing—a 2024 survey showed that said they use the platforms today for use cases.

As global privacy regulations tighten and third-party cookies fade away, these environments will continue to go from “specialized” tools to everyday business infrastructure.

We’re already seeing signs of in the market, with simpler, more affordable solutions emerging to help smaller companies participate.

Much like how website builders made web development accessible to everyone, future data clean rooms will become more user-friendly while improving their robust privacy protections.

Other developments on the horizon include:

• Industry-wide standards that will make it easier for different clean room platforms to work together
• AI-powered automation to streamline privacy controls and data matching
• Deeper integration with existing business tools and
• More accessible pricing models and simplified implementation options

Data clean rooms can be thought of as the natural evolution of data collaboration. Just as cloud computing quickly became essential infrastructure, clean rooms are poised to become the main way organizations share and .

As data clean rooms reshape how organizations work together, integrating them with analytics tools like Amplitude creates new opportunities for deeper insights.

Amplitude’s security features and privacy controls make it an ideal platform for analyzing the valuable insights generated through data clean room partnerships.

Once you’ve used a data clean room to securely combine datasets with your partners, Amplitude helps you transform those insights into actionable improvements.

The platform complements data clean room workflows by:

• Processing and analyzing aggregated data from collaborative partnerships
• Maintaining strict security standards that align with clean room protocols
• Turning cross-organization insights into concrete product improvements
• Enabling privacy-safe experimentation based on combined learnings