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At every industry event, and across my many friends in the advertising business, I am most often asked, “Why should I buy a data clean room?” My response is always the same: “What are you trying to build? What compelling products can you offer the market today that you could not offer yesterday?”
The data clean room (DCR) is currently one of the most discussed solutions within the ad-tech and mar-tech ecosystems. There is, however, some confusion around this new acronym, with many organizations knowing they need a DCR but unsure of what they should be investing in.
Data clean rooms are about building anew, not ticking a box on a mar-tech shopping list. DCRs can and do replace antiquated and obsolete technology of the past. But the most exciting part of my job is helping customers build the next generation of advertising products—performant products that enable planning, targeting and measurement while respecting user privacy and maintaining control over sensitive data.
Not all data clean rooms are created equal
At this stage, let’s deal with definitions. There’s a strong argument to be made that these technologies are better described as data collaboration platforms (DCPs), and I would agree. A DCP is the underlying technology that can enable DCR use cases and do so much more. However, as “data clean room” has been common parlance in ad tech, I’ll stick to that term for the sake of clarity.
It’s important to understand that not all DCRs are created equal, and some aren’t or shouldn’t be called data clean rooms because they lack even the most basic privacy principles and protocols. Various technologies have been positioned in the market under this title, leading to the IAB Tech Lab publishing its Data Clean Room Guidance & Recommended Practices to help the industry reach a common understanding of what defines a DCR.
Some solutions described as DCRs don’t even provide basic functionality like enhanced privacy protection and decentralization of data. Broadly speaking, there are two different types: Firstly, there is a centralized multiparty clean room, such as a data warehouse. These do allow for collaboration on analytics and data science processes between multiple organizations, with first-, second- and third-party data sets stored together in one place. However, they are generally very complex to use and may not provide adequate control or ownership of data, which could compromise the privacy of customers.
