Many industry insiders believe the honeymoon phase is over for data clean rooms. In the face of third-party cookie deprecation, clean rooms were supposed to solve the data scale issues brands and publishers predicted. If your first-party data is limited, just combine it with your business partners’ data, in a secure and privacy-compliant environment. That makes sense, right?
In reality, it’s never been quite so simple. And it certainly hasn’t helped the reputation of clean rooms that so many vendors positioned them as a cure-all for a smattering of issues the industry is contending with – identity, targeting, measurement, attribution, analytics, and so on. The results brands and publishers are seeing generally don’t, and can’t, match the hype.
The hype and the backlash threaten to diminish the role data clean rooms can and should play in today’s digital ecosystem. They have a purpose, but the industry could use some clarity on that purpose, and the value clean rooms deliver, so stakeholders don’t get taken in and disillusioned by snake oil peddlers.
The reality of data clean rooms
First, let’s clear up what clean rooms are not. They are not a comprehensive replacement for third-party cookies. They are not a data solution. What they are is a tool – or, more specifically, an environment. They are a place for two or more trusted business partners to compare data sets, without entirely sharing their data – each business ultimately retains ownership of its data, and contributes only the data it wants to contribute. The data is scrambled to keep personally identifiable information private, and the partners in the clean room need to comply with each other’s policies for consent.
A potluck dinner only works if enough guests bring enough food to satisfy everyone’s appetite, and it’s best when the food is good, and there’s an interesting variety. A clean room works the same way. Every participant must take care to make sure they’re bringing what others need and to remember the best data provides the best leverage.
A data clean room will not somehow clean anyone’s bad data. It’s a “clean” room because the data is supposed to go in clean — with personally identifiable information (PII) and sensitive data scrubbed, parameters for comparing data sets established ahead of time, and identity solutions highlighted. This makes the data actionable and helps give it value. If the data can’t be used to follow consumers or audiences across the buyer’s path, across datasets provided by different clean room participants, it won’t be able to replace the cross-channel tracking capabilities currently enabled by third-party cookies.
But if participants embrace the spirit of collaboration, and bring data to the table that can connect the dots and help to meet each other’s business objectives, the possibilities are endless. Two or more publishers, for example, can bring together data that strengthens their audience profiles and collectively raises the value of their inventory. Or, an e-commerce site and a publisher can compare data sets to track customer journeys and understand the performance of referral programs.
The different data clean room environments
Several high-profile launches of clean rooms — from the likes of Google, Amazon, Disney, and NBCUniversal — have added to the hype. But we need to remember that the term “clean room” describes multiple types of environments:
- Walled garden clean rooms deliver their value by allowing a business to layer additional data onto the data of what already sits within the owner’s walls.
- Clean rooms within independent environments deliver the most mutual value for participants.
- Centralized clean rooms, where the terms are set by the platform’s owner, are less collaborative and are more of a means to make the data within walled gardens available to the clean room owner.
Stakeholders exploring the possibilities of clean rooms need to ask: Does entering this particular environment enhance the value of our own data sets, or would we simply be handing our data over to a platform’s owner?
Ensuring interoperability across stakeholders won’t be a cut-and-dry process. It will likely call for industry-wide initiatives. For a precedent of what that might look like, consider the state of customer data platforms (CDPs). CDPs also went through a similar hype-followed-by-reputation-management cycle, until the CDP Institute was established to ensure the CDPs delivered what they promised.
Before industry-wide trust and satisfaction in clean rooms overall drops further, the legitimate, trustworthy clean room providers should get in front of the issue, highlight the technology’s intended use cases, and decry the trend of dubbing any privacy-compliant data platform a clean room.
Clean rooms — with the right combination of participants — bring too much potential value to risk becoming victims of their own hype. They need to be marketed and promoted appropriately, promising only what they can deliver. And industry stakeholders need to seek out those unbiased, independent environments and interoperable solutions that will drive toward a collaborative, privacy-forward digital future.
About the author
Eliza Nevers drives Lotame’s global strategy and product roadmap to serve the evolving data enrichment needs of marketers, agencies, and publishers. Fluent in the full product life cycle, from strategy to development and build, she brings nearly two decades of hands-on experience in tech to her role as Chief Product Officer at Lotame. Prior to her tenure, Eliza served as VP of Product for Verizon Media. During her 12+ years there, she steered the development and launch of AOL’s first DSP, SSP, and DMP, driving the evaluation and product integration of multiple strategic acquisitions as well as developing and launching AOL’s unified Advertising platform, ONE by AOL for Advertisers. Most recently, Eliza launched her own consulting business where she defined product strategy and product development agile processes for various companies in and out of adtech.