The way marketers use data is shifting fast, mainly because of privacy laws like GDPR and CCPA. Companies are under pressure to find new ways to analyze performance, target audiences and share data — without crossing legal or ethical lines. That’s where data clean rooms (DCR) come in. They’re not a cure-all, but they’re becoming a key part of the privacy-safe data collaboration and measurement toolkit.
What is a data clean room
A data clean room is a secure environment where two or more parties can bring their data together to run analysis without actually revealing any sensitive or raw data to each other. They use privacy tech like hashing, encryption and strict access controls.
How data clean rooms work
Typically, both sides upload data using hashed identifiers (like email addresses or user IDs). Once inside, the data can be matched and analyzed, but no one can access the raw stuff. The outputs are usually aggregated, like showing overlap in audiences, but don’t reveal individual users. Privacy protections are baked in to avoid anything personally identifiable slipping through.
Why use a data clean room
Clean rooms are becoming popular because they make it easier to comply with privacy laws while still gaining value from data. Anonymized data reduces legal risk and limits exposure in the event of a breach. It also gives companies more control over how their data is used when working with external partners.
Dig deeper: How data clean rooms might help keep the internet open
Clean rooms let companies run analytics and performance insights using aggregated and anonymized data.
Use cases for data clean rooms (DCRs)
- Data privacy compliance.
- Data anonymization.
- Data cleansing and normalization.
- Data transformation and enrichment.
- Attribution.
- ROI measurement and modeling.
- Mixed media modeling.
- Predictive analytics.
Where does it fit in your stack
Generally, the DCR fits between the organization’s data layer and activation layer. Here is a basic map that is by no means exhaustive:
At the bottom of the stack is the data infrastructure layer that might include a data warehouse, data lake or similar container. Data governance and identity tools also live in this layer.
Types of data clean rooms
Not all clean rooms are built the same. Clean rooms work differently depending on who’s offering the service and what the intended use is. That’s led to many different flavors on the market, and vendors often collaborate or overlap in what they offer.
Walled gardens / media or platform clean rooms
These are clean rooms offered by big tech or media platforms—Google, Meta, Amazon, Disney, NBCU, etc. You bring your data into their environment to match it with theirs (for example, ad exposure data).
- Suitable for: Advertisers spending a lot on one platform and looking for insights within that platform’s walls.
- Limitations:
- You’re stuck within that one platform—no easy way to get a cross-channel view.
- You’ll need a strong relationship with the platform even to get access.
- Adoption is low relative to scale. They are very complex and difficult to use.
Third-party or neutral clean rooms
These are independent vendors like LiveRamp, AppsFlyer and Habu InfoSum whose entire business is clean rooms. They often support data matching and enrichment activation across multiple partners.
- Suitable for: Brands looking to collaborate with various partners beyond a single platform.
- Limitations:
- Can be costly and complicated to set up.
- It’s hard to scale if every partner uses the same vendor.
- Real-time performance can lag; ad activation may be limited.
Dig deeper: Evaluating data clean rooms for your organization
Clean rooms built into data warehouses (DWH)
Cloud providers like Snowflake, BigQuery, AWS and Databricks now offer clean room tools. You can avoid moving data around if your partners are on the same platform.
- Suitable for: Teams that already use the same cloud platform and have the technical skills to manage it.
- Limitations: These are more like DIY toolkits—expect to roll up your sleeves and use SQL or code.
CDPs with built-in clean rooms
Some customer data platforms (like Adobe and Blueconic) now include clean room capabilities. The data usually stays inside the CDP.
- Suitable for: Brands and partners that are both using the same CDP.
- Limitations: The clean room is only as useful as your partners’ adoption of the same CDP.
Dig deeper: IAB Tech Lab launches first clean room standards
Custom, in-house clean rooms
Some big companies build their own from scratch. This usually happens when the needs are very specific or sensitive.
- Suitable for: Large enterprises with deep pockets and unique requirements.
- Limitations: Extremely expensive and complex to build and maintain. Meta’s internal version, for example, wasn’t easy to scale beyond a few major partners.
Data onboarding vendors adding clean room features
These vendors (e.g., identity resolution providers) offer clean room tools focused on matching datasets and enriching them with third-party data.
- Suitable for: Teams looking for identity resolution and enrichment from one provider.
- Limitations: Their activation options may be more limited than purpose-built clean rooms.
Data clean room issues
Even though the clean room landscape is growing, they’re not a perfect solution. Some of the biggest pain points include:
- They’re not one-size-fits-all. Clean rooms are just one tool—suitable for certain jobs, not all.
- They’re often hard to set up. Technical complexity, time cost are significant hurdles.
- You need partner buy-in. Everyone involved has to agree on the same vendor or setup, which can slow things down.
- Cross-platform measurement is tricky. Walled gardens especially make it tough to get a complete picture.
- They’re not great for real-time insights. Some are just too slow for things like ad optimization.
- Governance rules are uneven. Whoever owns the clean room sets the terms.
- Adoption is still low. Despite growing interest, many marketers haven’t used one yet—likely due to the above challenges.
Data clean rooms aren’t a magic bullet, but they’ve become a go-to option in a world where privacy matters more than ever. They let companies work together using data without compromising user privacy or breaking laws like GDPR or CCPA.
Choosing the right type depends on your goals, tech stack who you’re partnering with. While there are still barriers—cost, complexity, partner alignment—clean rooms are evolving fast. Expect easier-to-use, more scalable versions soon, especially ones that include built-in activation and cross-platform features.
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