The Rise of Data Clean Rooms: A Win-Win-Win for Publishers, Advertisers, and Consumers

Data Clean Rooms have been making headlines recently, the latest news being AWS introducing AWS Clean Rooms. But what exactly are Data Clean Rooms, and why are they suddenly making so much noise?

Simply put, Clean rooms, also known as data clean rooms or privacy-safe data sharing solutions, are becoming increasingly popular in the advertising industry. These clean rooms allow advertisers and publishers to work together and share data in a way that is compliant with privacy regulations. This allows for more accurate reach and frequency measurement, as well as the ability to target and acquire users without relying on third-party cookies.

There are two types of Data Clean Rooms: Media Clean Rooms, which are provided by publishers and walled gardens such as Facebook or Google ; and Partner Clean Rooms, formed by publishers and advertisers working together. These Clean Rooms provide a way for advertisers and publishers to generate customer insights and target users without infringing on consumer privacy. Disney has been building out its clean room for roughly a year and recently announced an integration with VideoAmp. This integration allow advertisers to match their own first-party data with Disney's audience graph, resulting in deduplicated campaign measurement based on ad exposures across Disney channels.

AWS, as we said earlier, also recently introduced its own clean room technology, which provides a privacy-safe and compliant way for brands to learn more about each other's customer data. This allows to build partner clean rooms formed by publishers and advertisers working together, for example.

The rise of Data Clean Rooms is due in part to shifts in privacy regulations and consumer attitudes towards data sharing. As privacy becomes increasingly important, Data Clean Rooms offer a way for advertisers and publishers to work together without compromising consumer trust.

Disney's clean room is an example of how these solutions can be used to improve measurement and audience targeting. With the integration of VideoAmp's TV viewership data, Disney's clean room capabilities are set to expand even further. As the use of clean rooms continues to grow, they are likely to become an essential part of the advertising industry's approach to data privacy and audience targeting.

But the rise of Data Clean Rooms also presents some challenges. Building and maintaining First Party data can be time-consuming, and if important data sources in the omnichannel aren’t connected to the effort, there may be gaps in the profiles and audiences. This is where Customer Data Platforms (CDPs) can be especially useful. CDPs can unify omnichannel sources and automate the unified first party data directly to the multichannel, as well as to a secure extract that can be moved to a trusted Data Clean Room. Data clean rooms are becoming an essential part of the advertising industry, but they come with their own set of challenges. Building and maintaining first-party data can be difficult and time-consuming, and if key data sources are not included in your clean room efforts, you may be missing out on valuable insights.

This is where Customer Data Platforms can be especially useful. CDPs can unify your omnichannel sources and automate the process of sending your unified first-party data directly to the multichannel. Plus, CDPs allow you to collect, check, normalize, enrich and send in real time your first-party customer data to a trusted data clean room.

With a CDP, you can streamline your data clean room efforts and gain valuable insights from your first-party data.

Overall, the Data Clean Room represents the future of advertising and data sharing. As privacy regulations continue to evolve and consumers become more concerned about their data, it is expected that Data Clean Rooms will become the norm in the coming years. Brands and advertisers that are able to adapt and make use of this technology will be well-positioned to succeed in the changing digital landscape.