Advertising has, for a long time, assumed that more data in an advertisement means better results. Today that assumption is less reliable than it has been in the past. As we transition to privacy-based advertising ecosystems, we not only see less access to data but a complete redesign of how we can use that data.

While this may seem like just another option to solve the same issue, what is really emerging is another layer of infrastructure. There are two primary infrastructure tools contributing to this shift: Clean rooms, and Data Collaboration Platforms. These types of platforms offer very different methodologies for the use of data across multiple parties without compromising data security.

While clean rooms and data collaboration may both appear to be solutions to the same problem (data sharing), on the surface both allow the ability for multiple parties to access/use data without the need for direct data-sharing. Likewise, both types of platforms were also created with regulatory and platform constraints in mind. However, the method by which each approaches this issue and the use cases unlocked by each platform is vastly different.

Design-wise, clean rooms are facilities that have restrictions to allow for control of the environment, where multiple sources of data can be collected, reconciled, and analysed without putting individual data points at risk by being exposed at the micro level. The primary focus of clean rooms is containership. Data does not move between locations; rather, the transfer of data happens through queries. The results of the queries are generally returned as aggregated outputs, and precautions have been built into the system to ensure that neither form of data exposure occurs.

The model of the clean room is most effective in instances with high sensitivity and non-existent trust boundaries. For instance, all types of measurement done across virtual boundaries (clean rooms), overlap analysis of audience groups, and attribution of marketing activities all benefit from the use of clean rooms. In terms of trade-offs, speed is a factor due to clean rooms being more oriented toward governance than speed; this may restrict the degree to which data can be dynamically activated.

The approach of data collaboration platforms is broader in terms of their view of the data and the degree of privacy in the data. They also operate within privacy-safe parameters, but the primary objective of the data collaboration platform is to enable a more flexible interaction between datasets and facilitate the execution of campaigns based on analysis of the data rather than strictly analysing the data in a secure manner.

Distinct yet critical. Cleanrooms have been established to help answer questions. Collaboration platforms then help you act on that answer. They are designed to provide support for many integrations, allowing for connections to occur easier between multiple activities; this creates a more circular flow of data, not just a set of calculated stops along the way.

However, with this increased opportunity for flexibility there is also an increased need for complexity. Controlling how multiple partners, environments and use cases have consistent governance requires a more coordinated response. Access is less of an issue; synchronisation is much more of a concern to ensure all participants are operating under the same set of rules without creating bottlenecks.