Your guide to clean rooms

In today’s privacy-first world, marketers face a critical challenge: how to leverage third-party data without compromising customer trust or compliance. The solution? Data clean rooms—secure, privacy-compliant environments where brands and data providers can collaborate without exposing personally identifiable information (PII).

Whether you’re looking to enrich your first-party data, identify new audiences, or improve segmentation strategies, clean rooms offer a powerful—and safe—way to do it.

What is a data clean room?

A data clean room is a secure, neutral environment where two or more parties can bring their datasets together to perform joint analysis. The catch? All the matching and analysis happens without either party seeing the other’s raw data.

Think of it like a walled-off room with one-way mirrors: you can view aggregated insights, but never the personally identifiable details behind them.

Why clean rooms matter now

With the ever increasing need for new data sources to provide rich demographic and firmographic data to be used for analytics, targeting and personalization, and the tightening of global privacy regulations (think GDPR, CCPA, SPI and others), brands can no longer afford to trade on exposed user data. Clean rooms provide a secure and compliant alternative, helping you:

• Safely match your first-party data with external sources
• Explore overlaps and gaps between your audiences and a partner’s
• Purchase enhancement data to enrich your records
• Protect consumer privacy while still gaining strategic insights

How it works

Here’s a simplified step-by-step of a typical clean room use case:

1. Upload your first-party data (e.g., name & address, email addresses, hashed identifiers) into the clean room.
2. A data provider does the same—either one provider or several.
3. The platform matches records based on intelligent, privacy-compliant algorithms which maximize match results while keeping PII out of sight.
4. You receive reports or insights showing where your audiences overlap.
5. Optionally, you can license additional data (demographics, behavioral traits, firmographics, etc.) to enhance your first-party file—all without either side ever seeing raw data.

Key use cases

• Audience enrichment: Append missing or valuable attributes (like income, household size, or purchase behavior) to improve targeting.
• Overlap analysis: Understand how much of your audience is shared with a potential media or data partner.
• Activation planning: Build better lookalike audiences or segment for omnichannel campaigns based on enriched data.
• Measurement: Evaluate performance post-campaign across shared audiences without exposing customer identities.

Choosing the right clean room partner

When evaluating clean room solutions, here are a few features to prioritize:

• Interoperability: Can it integrate with multiple data providers and platforms?
• Privacy controls: Is data hashed, tokenized, or encrypted? Does it comply with your legal and security standards?
• Scalability: Can it support multiple use cases—from planning to measurement?
• Transparency: Does it give you clear visibility into data overlap, match rates, and ROI?

Platforms like Snowflake, AWS Clean Rooms, Google Ads Data Hub, and some provider-specific options (like Data Axle’s own clean room environment) each have unique strengths depending on your goals.

The bottom line

Clean rooms are no longer just for the most data-mature organizations. They’re becoming an essential tool for privacy-first marketing, enabling smarter data use without the risk.

Whether you’re enhancing your CRM, planning targeted campaigns, or preparing for a cookie-less future, clean rooms give you a powerful way to connect the dots—safely.

Ready to explore the possibilities? Now’s the time to step inside the clean room.