What is a data clean room and how does it work? 

A data clean room is a secure software environment where companies can match and analyze their first-party customer data with data from other businesses. As third-party cookies disappear and privacy regulations tighten, the data clean room is one of the leading solutions for advertisers and publishers who need to run targeted campaigns and protect consumer privacy. In this article, we will explore how data clean rooms work, the pros and cons of them, and why some brands are building their own. 

How does a data clean room work? 

The first step involves companies adding their first-party data to the data clean room. In the next step, various security and privacy-protection measures are applied to the data, such as pseudonymization, restricted access, differential privacy, and noise injection. The third phase involves placing the data into cohorts.

The data can then be activated, i.e., used for various advertising and marketing processes such as targeting, measurement, and audience analysis.
 

Advertisers and publishers can analyze reports provided by the data clean room to improve the campaigns they are currently running or will run in the future. 

To understand how data clean rooms work, think of a metal box traveling along a one-way conveyor belt. Using this analogy, here’s how the process works: 

1. Loading 

Advertisers put a package of their first-party data on the belt. The package can contain user-level data as well as transactional and historical data. On the other side of the belt, another advertiser or publisher places their package of first-party data on the belt. 

2. Cleaning 

The belt takes packages to the metal box, which is the data clean room. In that box, the data from the two parties is matched and cleaned, i.e., audiences are matched, and privacy techniques such as encryption, hashing, pseudonymization, restricting access, and noise injection are applied. 

3. Ready to use 

From there, you can show ads to members of your target audience and receive reports, which you can then analyze and utilize for other advertising-related activities. 

Because privacy is the key focus of a data clean room, you’ll receive reports based on aggregated data. So, you’ll know how many people clicked on one of your ads, but you won’t know anything about them, e.g., you won’t receive user-level data such as IDs. 

Our AdTech development teams can work with you to design, build, and maintain a custom-built data clean room for any programmatic advertising channel.

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The main use cases of a data clean room 

The various privacy changes in web browsers and mobile apps, as well as new privacy laws, are creating a better world for consumers and Internet users but are making it harder to run digital advertising activities that companies have relied on in the past. Data clean rooms offer a good balance between protecting user privacy and allowing companies to reach their target audience, measure the performance of their campaigns, and attribute impressions and clicks to conversions. 

A data clean room allows companies to establish co-marketing partnerships by identifying the customers they share with each other and to create more detailed user profiles by analyzing the anonymized reports. 

The pros, cons, and risks of using a data clean room 

Like any technology, data clean rooms come with a distinct set of benefits and trade-offs worth weighing before adoption. 

The pros of using a data clean room: 

• A privacy-friendly solution for analyzing audiences, targeting ads, and measuring performance. Even though user-level data is added to a data clean room, it’s not exposed to other companies.  
• Some data clean rooms deliver a holistic view of the performance of campaigns across various distribution channels. 
• The data added to a data clean room is not shared with other companies, allowing the data owners to maintain control of it. 

The cons of using a data clean room: 

• Aggregated data for reporting and ad targeting will be less accurate than ID-based data. 
• Before you can upload the data to a data clean room, it has to be unified into one format in order to use it. 
• A reluctance to share first-party and transactional data may adversely affect the overall effectiveness of a data clean room and the various functions it can carry out for the companies that use it. 
• Many data clean rooms work for a specific platform (e.g., Google or Facebook). This means advertisers are forced to manually combine results from different data clean rooms. 
• Because data clean rooms are fairly new tools, there are no universal standards for their implementation yet. 

The risks of using a data clean room: 

• To generate insights, advertisers have to hand over their valuable first-party data. In the worst possible scenario, a data breach could lead to hefty fines, not to mention reputation and clients loss. 
• Manually operated data clean rooms are prone to human error, such as misgranted access permissions, faulty query configurations, and insecure data transfers. 

Despite the cons and risks of using data clean rooms, they offer a very promising solution to the current challenges facing the programmatic advertising industry, i.e., running advertising processes, such as ad targeting and measurement, in a privacy-friendly way. 

What’s the difference between a CDP and a data clean room? 

Both advertisers and publishers collect valuable first-party data from different sources. To help them collect and manage this data, they can use a customer data platform (CDP). A data clean room extends the capabilities of a CDP and takes data management to the next level.

But what are the main differences between a CDP and a data clean room? 

1. A CDP allows you to collect, share and process first-party data, but you are focused on user-level data and IDs. With data clean rooms, the focus is on using anonymized first-party data. 
2. A CDP with basic security levels (e.g., granting access) is more prone to data leakage compared to high levels of security in a data clean room as the data is anonymized using various data security techniques. 
3. You can’t analyze data from other companies in a CDP, but with a data clean room, you can get anonymized reports based on aggregated data, from which you can extract insights. 

What are the privacy alternatives to data clean rooms? 

There are essentially three main alternatives to a data clean room: 

• Universal IDs. Universal IDs have emerged as a replacement for third-party cookies, whereby email addresses are used to create hashed IDs.  
• Google Chrome’s Privacy Sandbox. A number of standards focused on better protecting user privacy while at the same time allowing advertisers and publishers to run, measure, and report on programmatic advertising campaigns. 
• Contextual ad targeting. This was the first ad-targeting method available when online advertising began back in 1994 and thanks to the changing privacy landscape, it’s making a comeback. Contextual targeting allows advertisers to show ads to users based on the context of the page or mobile app. While this may sound like a very primitive targeting method, it can be quite effective and can even be enhanced by using other pieces of data from the publisher. 

Other alternatives could also be explored in the future, such as crypto identities, which aim to represent people via avatars. The technology enables matching, acquiring, and testing data without sharing personally identifiable information (PII). 

Which companies offer data clean rooms? 

There are three kinds of data clean rooms. The first kind is provided by the walled gardens of AdTech, the second kind is provided by independent companies, and the third is owned by companies with huge amounts of users and content. 

What’s the difference between them?

With the first kind, Google, Amazon, and Facebook run media clean rooms where each company delivers hashed and aggregated data to companies that use their advertising platforms. 

In the second case, two data owners, e.g., a publisher and an advertiser, put their data into one neutral room and share it safely between one another.

And in the third case, companies that have massive amounts of user data and content, such as Disney, Spotify, and TikTok, build their own data clean rooms.

Let’s now look at some examples of companies that offer data clean rooms. 

Decentriq 

Known as the “Switzerland of Data”, Decentriq is a data clean room and data collaboration platform. Decentriq’s technology is built using the latest advancements in encryption and Privacy Enhancing Technologies such as synthetic data, differential privacy, and Confidential Computing. The Decentriq platform is used by companies from various industries, including media and advertising, healthcare, and banks.

Google Ads Data Hub 

Google Ads Data Hub is a privacy-safe data warehousing solution built on Google Cloud. It provides tools to create custom reports that don’t contain personally identifiable information (PII). The sources of data come from Google Campaign Manager, Display & Video 360 (DV360), Google Ads, and YouTube.

Amazon Marketing Cloud (AMC) 

Amazon Marketing Cloud (AMC) is a holistic data clean room solution built on Amazon Web Services. It helps companies discover the true impact of cross-media investments by matching and analyzing two sources of data: advertiser’s data sets and data sets delivered by Amazon Advertising events.

InfoSum

InfoSum created a privacy-enhancing environment with the utmost respect for the safety of data. The mechanisms behind InfoSum’s data clean room process the data in a fully decentralized and cloud-agnostic room which eliminates all the data-leakage risks related to centralized data lakes or warehouses.

Snowflake 

With Snowflake, advertising companies can build an environment capable of processing shared data sets. Snowflake’s clean rooms provide real-time information and hide customers’ personal information at the same time.  

Saptharushi (previously Aqilliz) 

Saptharushi offers a new-age middleware technology for the currently disjointed digital marketing ecosystem. Rooted in the pillars of differential privacy and federated learning on a distributed ledger, Saptharushi benefits brands, platforms, and consumers alike by delivering collaboration solutions that ensure a privacy-compliant approach to insights, activation, and measurement, leading to better productivity.

Disney Advertising Sales 

Disney Advertising Sales introduced its clean room in 2021. The cloud-agnostic solution is powered by Disney Select data and Disney Advertising’s Audience Graph. The key strategic cloud collaborators are Habu, InfoSum and Snowflake.

FAQ

What do data clean rooms provide for businesses?

Data clean rooms provide a secure environment to analyze first-party data and generate insights without exposing user-level information.

How do clean rooms enable secure data sharing?

Clean rooms enable privacy-safe data sharing by applying encryption, pseudonymization, and differential privacy techniques before any collaboration takes place.

Can data clean rooms safely combine data from different companies?

Yes. They combine data from multiple parties in a privacy-compliant way, ensuring the results remain secure data outputs without revealing raw records.

Conclusion 

Data clean rooms are not a silver bullet, but they offer one of the most viable paths forward in a privacy-conscious advertising ecosystem. For companies willing to invest in the right infrastructure and partnerships, they unlock new opportunities for collaboration, measurement, and audience intelligence, while keeping sensitive customer data protected. Every business has unique data, privacy, and advertising needs. Contact us to learn how Avenga’s experts can help you turn first-party data into a competitive advantage, securely and at scale.