What type of data clean room do you need and why?

Advertisers and publishers were granted an extended reprieve last year when Google delayed the phase-out of third-party cookies into the second half of 2024. But it’s fair to say the industry is already implementing robust alternatives for collecting, analyzing and leveraging insight from their audience data.

First-party data is no doubt the key to this. However, data clean rooms are also emerging as a popular solution when it comes to augmenting this with second-party data.

Through mutually beneficial data collaboration partnerships, companies can combine and compare data sets in a secure and privacy-compliant space without sharing and raw data. For instance, data clean rooms enable publishers and advertisers to unlock deeper ad performance insights by layering in event-level data and tracking cross-platform consumer journeys.

As a relatively new concept, it can be hard to know which type of data clean room to choose, so so let’s start with the current industry definitions:

Walled garden clean rooms

The most recognized form of data clean rooms today are walled gardens.

First established by media giants – such as Google , Amazon and Meta – walled gardens offer advertisers a space to analyze their performance within the individual publisher’s platform using data collaboration. Not only are they able to gain access to event-level data held within the walled garden, but they can also enrich it with their own first-party data to bring to light previously hidden performance insights. This insight helps to drive informed campaign decisions while protecting consumer privacy.

With Google, Amazon, and Meta accounting for three-quarters of all digital ad spend ( 74% ) walled gardens are a popular choice, though it’s prudent to remember these closed ecosystems are owned by the provider, meaning they retain significant control over the hardware, applications and content.

1. Excellent credential management – each source will require its own particular credentials to access data, so these will need to be accessible, secured, and easy to manage.
2. Comprehensive integrations – the best ETL tool will be compatible with every API you require.
3. Maximal automation & scheduling – since ETL can involve hundreds of integration jobs per day, automating as much as possible will save time.
4. High-performance quality – software quality consists of three main elements: accuracy, stability, and speed. Accuracy is the minimization of errors, stability is consistent behavior without crashing, and speed is how fast it performs.

Independent clean rooms

Independent clean rooms are a service offered by companies entirely dedicated to the technology. Also referred to as pure players, they have built data clean room software solutions designed for media companies and brands to use. Major players in the space include Habu, Optable, InfoSum and LiveRamp.

Newest to the space is Amazon with their AWS Clean Rooms, entering the market in preview mode.

Independent data clean rooms provide a service where any publisher, advertiser or marketer can enter into data partnership to enrich their first-party data sets. They offer greater flexibility than walled gardens, are easier to configure, can be tailored to meet the needs of the participants, and are generally more mutually beneficial in terms of the data that is shared.

However, some have limited downstream integrations, require additional tools for data ingestion, or provide a lack of detail in first-party data analysis.

Clean rooms within other platforms

As the name suggests, there are clean room solutions that exist within the confines of another technology platform. Delivered by organizations with a specialized marketing application or services in adjacent industries like cloud data storage. Leading providers like Snowflake, BlueConic, Epsilon and Merkle have all announced a clean room solution that exists within their platforms.

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Snowflake’s warehouse-level offerings give advertisers greater architectural flexibility and more robust governance in their data clean rooms. However, they require greater technical expertise to set up and implement.

Those embedded within marketing applications provide ease of use, but are often constrained by their access to additional data, often having limited access to partner data outside their ecosystem and no access to the walled gardens that many advertisers value.

Approaches to data clean rooms

Within these different types of clean rooms exist two different approaches: single-party centralized and multi-party centralized.

Single-party centralized data clean rooms refer to a data processing environment where a single organization controls both the data and the clean room setup. The organization has complete control over the data, the processes, and the personnel involved in the cleaning and analysis of the data.

In contrast, multi-party centralized data clean rooms involve multiple organizations sharing the control and responsibilities of the data and the clean room setup. These organizations collaborate to ensure the security and privacy of the data, while also sharing the costs and benefits of the data processing. The data is processed in a neutral and secure environment, where access and usage are strictly regulated by mutually agreed-upon protocols and procedures. The multi-party setup provides a level of transparency and accountability that is not present in single-party clean rooms, making it a preferred option for sensitive or regulated data.

Making the most of data clean rooms in 2023

As with any emerging technology, data clean rooms remain in a constant state of flux. But by taking the time to understand the different types and approaches to data clean rooms, early adopters can begin to choose the right option(s) for them before the culling of the cookie.