Should we stay or should we go? That was a question posed during our leadership offsite in December 2024. With so much confusion in the data clean room market, should InfoSum abandon the category we helped create? But as my colleague Ben Cicchetti said, “It's better to be in the category driving change than outside of the category throwing shade.”
The data clean room market is at a crossroads. What was once a clearly defined category—designed to enable secure, privacy-first data collaboration that drives marketing performance—has become muddled by misrepresentation and overextension. Solutions that centralize data, expose it to unnecessary risk, or funnel business to their core propositions (cloud storage, identity systems, or media sales) now claim the same label as true data clean rooms.
This confusion isn’t just frustrating—it’s risky. Businesses that rely on solutions that fail to deliver privacy, neutrality, and scalability jeopardize compliance, consumer trust, and competitive advantage.
As one of the original pioneers of the data clean room, InfoSum believes it’s time to clarify what a real data clean room looks like. Here, we’ll explore the anatomy of a data clean room in 2025: the features and signals buyers should seek to ensure they choose a secure, privacy-first, and future-ready solution.
The Anatomy of a Data Clean Room
A data clean room is more than just a buzzword. It’s a platform designed to enable privacy-first collaboration without compromising control over your data or introducing unnecessary risks. Below, we outline the key features and signals that define a data clean room in 2025.
1. Privacy-by-Default
What to Look For:
- Privacy controls are baked into the platform’s design—not optional features or add-ons.
- These controls are always on, ensuring privacy is enforced automatically and consistently.
- Includes safeguards like role-based permissions, differential privacy, and non-movement of data, preventing even unintentional exposure of sensitive data.
Why It Matters:
Privacy by default means businesses don’t have to rely on manual configurations to enforce privacy. When controls are built into the platform’s foundation, every action—data matching, analysis, or activation—happens securely. This automatic enforcement eliminates the risk of user error or misconfiguration while assuring regulators and collaborators alike.
Red Flags:
🚩 Privacy settings that can be toggled off.
🚩 Reliance on external privacy tools or add-ons to supplement inherent weaknesses in the platform.
🚩 Limited transparency around how privacy is enforced or audited.
2. Neutrality
What to Look For:
- The platform is independent of cloud providers, identity vendors, and media owners.
- Neutrality ensures no hidden agenda, like pushing proprietary IDs, media channels, or cloud infrastructure.
- The platform fosters trust between collaborating parties, even competitors.
Why It Matters:
A neutral data clean room ensures fairness and avoids conflicts of interest. Businesses can collaborate without fear that their data will be used to benefit the platform owner’s other interests.
Red Flags:
🚩 The solution is tied to a media owner or identity provider pushing their proprietary systems.
🚩 The vendor requires specific cloud infrastructure, creating lock-in.
3. Interoperability
What to Look For:
- Seamless integration with multiple data sources, identifiers, and platforms.
- The ability to adapt to evolving identity solutions, like first-party data, hashed emails, or privacy-enhanced identifiers.
- Scalability to accommodate large, complex datasets and multi-party collaborations.
Why It Matters:
The future of data collaboration depends on flexibility. A data clean room breaks down silos, enabling partners to collaborate across different technologies and regions without friction.
Red Flags:
🚩 Requires adopting the vendor’s proprietary ID or data framework.
🚩 Limited support for cross-cloud or cross-border collaborations.
4. Decentralization
What to Look For:
- Data remains decentralized and is never moved or pooled into a central environment for processing.
- The platform uses decentralized processing, such as Private-Set Intersection (PSI), and non-movement architectures, to analyze and match where data resides.
- Enables cross-cloud and cross-region collaboration, ensuring data remains within its regulatory region.
Why It Matters:
Decentralization ensures businesses retain complete control over their data, reducing risk and improving compliance. It eliminates the vulnerabilities of central repositories, such as data breaches or unauthorized access, while supporting modern collaboration needs, like multi-party partnerships across jurisdictions with differing privacy regulations.
Red Flags:
🚩 Any requirement to upload or transfer data to a third-party environment for analysis.
🚩 Claims of decentralization contradicted by operational workflows that consolidate data.
5. Layers of Privacy-Enhancing Technologies (PETs)
What to Look For:
Multiple PETs, such as:
- Differential Privacy: Protects individuals by adding statistical noise to datasets.
- Secure Multi-Party Computation (SMPC): Allows parties to jointly compute results without exposing their inputs.
- Point-in-time synthetic IDs: Ensuring privacy by creating temporary, anonymized keys that cannot be reverse-engineered or re-used, guaranteeing privacy and eliminating exposure.
- Flexibility to apply the right combination of PETs for different collaboration needs.
Why It Matters:
PETs are the backbone of privacy-first data collaboration, ensuring data can be analyzed securely without exposure. Solutions that rely on only one method or oversimplify their PETs fail to offer the protection modern businesses require.
Red Flags:
🚩 Limited transparency around how privacy is enforced.
🚩 Reliance on a single PET without flexibility for varying use cases.
Buyer’s Checklist: How to Spot a True Data Clean Room
As you prepare to invest in a data clean room in 2025, here are the nine questions you should ask providers:
- Can I turn down specific privacy controls or turn them off entirely?
- Is your platform neutral or tied to a larger business interest like cloud, identity, or media?
- Can it connect and analyze data from multiple sources simultaneously without risk?
- Does my data remain decentralized when collaborating, or does it require centralization for analysis?
- Do you become a data controller for my data under the local privacy regulation?
- What PETs are built into your platform to ensure robust, secure collaboration?
- Do I need technical resources who know SQL or other query languages?
- Can I generate instant match rates and insights across datasets, or do I need to wait days or weeks?
- Can I efficiently work with partners across the globe, or am I restricted to a single region?
Why the Anatomy Matters
The anatomy of a data clean room is built on privacy, security, neutrality, and interoperability. These aren’t just nice to have or optional features—they’re requirements for businesses to collaborate confidently, effectively, and in compliance with evolving privacy regulations.
Separating real data clean rooms from solutions that fall short is vital as the market grows. At InfoSum, we remain committed to defining and delivering on the promise of privacy-first collaboration. Anything less isn’t just a compromise—it’s a risk.
