The industry conversation over clean rooms has become overly complicated. New technology terms often come with confusion, and clean rooms will benefit from forthcoming standards from neutral trade organizations like the Interactive Advertising Bureau. But clean rooms are simpler than this widespread “confusion” makes them out to be.

Whether they’re offered by third-party providers, walled gardens, retailers, or media companies, clean rooms are a vehicle for multiple companies to cross-reference their datasets and generate insights on audiences without sharing or exposing user-level data. The need for data collaboration is not new, and the use of clean rooms is already common. To dispel this confusion, let’s review the problems clean rooms solve, why the largest tech companies use them, and how they work.

Data collaboration challenges are nothing new

Businesses have always needed to share information to understand their audiences at scale in order to calibrate decisions regarding functions like marketing and product development. Data collaboration has historically faced two kinds of challenges: economic and legal. Parties struggled to agree on business terms, and they also worried that sharing data could compromise user privacy or security.

When third-party data was widely available via trackers like cookies and mobile IDs, the need to share information with a wide array of parties — peers, retailers, and media organizations — was less urgent. Many firms could rely on the walled gardens or third-party data providers, who could virtually track audiences across the scope of their digital activity. With recent privacy changes, the need for more creative data collaboration arrangements has arisen — hence the recent focus on clean rooms.

But the added attention to and need for clean rooms does not make them a mystical or entirely novel technology. Companies have always shared data. The difference is that, with clean rooms, data sharing — actually sending user-level data to another organization, thus creating privacy and security risks — is no longer necessary. Organizations can cross-reference datasets to generate insights without taking on this risk, which is why the world’s largest organizations have their own clean rooms.

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The tech giants have validated clean rooms

The walled gardens all use proprietary clean rooms to empower advertising partners to benefit from their massive audience intelligence without compromising user-level data. Via Google’s Ads Data Hub, Facebook’s Advanced Analytics, and Amazon Marketing Cloud, the giants’ brand partners can learn more about their audiences to more effectively capitalize on the marketing opportunities the trio offers.

For example, let’s say a global beauty brand has a first-party dataset of 10 million luxury hair care product buyers. The company can cross-reference its data with Amazon’s shopper intelligence to determine what sort of media is effective with this audience or what other products they might like to buy. This can inform marketing and product development. Importantly, Amazon does not send any data out of its clean room. It just lets the beauty brand surface overlapping trends based on its intelligence. So, there is no privacy exposure — the beauty brand learns about consumer trends without creating risk for individual shoppers.

Google, Meta, and Amazon have the corporate world’s deepest pockets and face the most intense scrutiny from regulators. There’s a reason they have developed their own clean rooms — and work with third-party clean rooms — to facilitate data collaboration. This is not a new problem or an entirely experimental technology shrouded in mysterious hype; it’s a core part of how the biggest ad networks currently do business.

How different kinds of clean rooms work

 Much of the confusion about clean rooms stems from the multiple arrangements in which the technology can be deployed. Walled gardens have clean room solutions, but so do retailers, media companies, and third-party vendors. Does every brand need to work with all these different clean rooms? Are they interoperable? How do they differ?

Retailers and media companies set up clean rooms for the same reason as walled gardens: they have valuable audience data at scale from which other organizations, especially brand advertisers, can benefit. But these organizations do not have the scale of the tech giants, so brands cannot depend on one or two retail or media company clean rooms alone.

This is where third-party solutions come in. They allow brands to share data with peers — again, cross-referencing datasets to generate insights without the need for data scientists or data sharing that leads to security risks. In addition, third-party solutions like Habu, can automate insights across an array of clean rooms to create holistic audience intelligence, going a long way to replacing the insights of deprecated third-party trackers without the privacy risks they entailed. With third-party solutions, brands can streamline intelligence across tech giant, retail, and media clean rooms, developing a holistic, privacy-safe picture of their audience.

The bottom line, then, is that there need not be hype over data clean rooms, nor confusion. The need for data collaboration is longstanding and has become more urgent in the past couple of years. The biggest companies in the world are using data clean rooms to fuel that collaboration. Now, many other organizations are adopting the same technology to streamline audience insights across clean rooms. No magic, no mystery — just better insights, less risk, and more respect for the consumer.

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