What are Data Clean Rooms and why do you need them in your business?

At a time when cybersecurity is a fundamental pillar in companies, more and more companies are turning to data clean rooms to securely group and analyze their data.

With these, obtaining more detailed information and ensuring that data privacy is protected is possible. We tell you in detail what they are and the keys to incorporating them into your data strategy effectively and securely.

What is a Data Clean Room

Data clean rooms allow companies to easily collaborate with data in a secure environment, where multiple parties can securely combine sensitive data without compromising the privacy and security of the data.

This concept is intended to be the equivalent of a physical clean room, to have an environment where technology cannot be contaminated by external influences. Instead of worrying about contamination by physical elements, the main concern of a data clean room is to keep user data isolated and private.

By implementing strict protocols and advanced technologies, these “rooms” enable organizations to share data securely, ensuring compliance with regulatory and privacy requirements.

Therefore, in an era of increasingly frequent data breaches, companies must be aware of the importance of taking robust measures to protect sensitive information. These solutions address these challenges by providing a secure ecosystem for data analysis, ensuring the confidentiality of the data involved.

Benefits of Data Clean Rooms

These rooms provide numerous benefits to content providers, marketers, and advertisers, such as:

• Regulatory compliance.
• Aggregate user information to view trend data.
• User segmentation to create custom audience groups.
• Aggregate data analysis to better understand user behavior and activity.
• Provide a secure place to access and share aggregated data.

How does a Data Clean Room work

For a data clean room to be enabled, two or more parties must collect, compile, and aggregate their first-party user-level data. These user-level data sets from the parties involved do not need to be the same, but they do need a means to match them.

Once in the “room,” these data sets can be matched using identifiers with common hash algorithms, such as email addresses, phone numbers, or user IDs.

Once the data has been collected, it can be uploaded into a secure environment, provided that all pre-determined agreements between the parties are respected. Any type of user-level data can be uploaded into this secure environment.

Once uploaded, information matching and cleansing are performed. When setting up the “room”, rules are applied to ensure that all parties only have access to their client’s data and to the new enriched data sets described in the original agreement.

The main features of data clean rooms are:

• Data segregation and isolation: access controls and permission levels are applied to maintain data confidentiality and integrity.
• Secure collaboration: provide a secure framework for data exchange between authorized parties through robust encryption protocols, file transfer mechanisms, and secure APIs.
• Access controls and audit trails: Access controls ensure that only authorized individuals or entities can access specific data sets. They also provide visibility and accountability for interactions with the data.
• Anonymization and de-identification: these techniques help protect privacy by removing personally identifiable information, as well as enabling meaningful analysis.
• Compliance frameworks: align with relevant regulatory frameworks to ensure that collaboration and data management practices comply with legal and industry-specific requirements.

Main challenges Data Clean Rooms

As with any technology, there are many challenges to be faced to achieve the best results. Some of the key challenges of this solution are:

• Establish and agree on the scope of the data exchange. They are intended to be neutral but are often set by the owner of the data clean room.
• Governance and oversight in an era of compliance.
• Find the right partners who are willing to tackle the implementation.
• Other tools and technologies that complement privacy challenges will also need to be assessed.
• Technical challenges with the rest of the enterprise stack, ensuring data management and configuration matching.

Steps to create an efficient Data Clean Room

If you want to create an effective data clean room and achieve your business goals, there are three main steps to do so. Let’s go through them one by one.

Determining readiness to start using a data clean room

Before starting to organize data sets, it is necessary to determine the overall readiness of the company and to clarify the objectives of the data clean room. Doing so avoids major conflicts that could jeopardize the project, such as unrealistic expectations or misunderstandings about priorities.

In addition to this, the state of the data must be evaluated, as it must be of high quality and that data silos are eliminated and centralized.

Some of the questions that companies should ask themselves before implementing this type of initiative include:

• What is our business case for developing a collaboration layer with a data clean room? What use cases do we want to focus on?
• What data are we willing to share and how will it be used?
• What privacy and compliance regulations apply to the types of data we want to collaborate with? How should we manage access to the data?
• Who will use the information in our clean room?
• How will we measure progress and success?

Evaluate data clean room capabilities

Once the readiness of the company is clear, as well as the state of the data, it is time to choose a solution. A suitable data clean room should enable organizations to get the most out of data from various parties, while still prioritizing user privacy and security. To this end, the following must be taken into account:

• Ensure data governance and privacy: Privacy is the cornerstone of this service, so privacy needs to be defined. A good solution will provide controlled access to shared data and allow multiple parties to answer questions about this data while preventing personally identifiable information from being disaggregated.
• Enable secure data sharing between clouds: they should provide the ability to share up-to-date data sets without having to copy or move data. This is more secure, retains control of the data, and ensures that it is up to date and not spread across silos.
• Encourages scalability and high performance: the data infrastructure must be able to scale without compromising performance.
• Provides access to trusted third-party data: using third-party data to enrich what is already available can turn your aggregated data sets into a very useful source of information. These can improve segmentation, which helps deliver more relevant and effective messages, as well as optimize the activation of segmented data to improve marketing strategies.
• Ease of use: thanks to the modern solutions available, it is much easier to develop and use data clean rooms, which natively support various programming, data science, and engineering languages, shortening the learning curve and increasing efficiency.

Developing a strategy focused on use cases

In the process of creating a data clean room, many companies choose to apply a progressive framework, where the use cases and number of collaborators or data sources mature over time. With this type of framework, one or more collaborators can be selected to join the clean room, data can be securely overlaid with a mutual identity key, and the overlay of data sets can be processed in a matter of minutes.

1. Sizing and activating the audience: here you need to choose a simple use case, explore possible solutions, and take the first steps to benefit from the advantages of this service:
   1. Two collaborators move their first-party data to the clean room and the PII is anonymized to enable collaboration without violating data privacy regulations.
   2. These partners can match data and identify audiences based on various factors (demographics, behavior, interests, etc.), segment audiences, and develop personalized content to reach that segment.
2. Advanced targeting and lookalike modeling: once you have mastered the basics, you need to refine your strategy and create more precise and effective campaigns.
   1. After collecting, processing, and analyzing data from two or three partners, you can start to do more detailed analytics to better target customers.
   2. It should also allow you to deliver more personalized and relevant content to your target audience and improve the response rate to your ads.
3. Measurement and attribution: here you can proceed more quickly to know the effectiveness of your initiatives and adjust the strategy accordingly.
   1. First-party customer data, as well as exposure and conversion data from various partners or sources, should be collected to create a complete picture of the customer journey.
   2. Ad impression data can be linked to transaction data to calculate conversion rate, upsell analysis, reach, and frequency.

Data Clean Room Implementation

Data clean rooms are rapidly emerging as a key mechanism for improving the return on customers’ data investments.

For a data clean room to be effective, you must have a solid data strategy and robust identity management, as well as capabilities that ensure the protection and privacy of shared data.

At Plain Concepts, we help you assess your readiness and the capabilities of data clean room solutions to choose the one that best fits your needs. In addition, we will select together the use case that can create the most value for your business to turn it into a larger initiative.

Don’t wait any longer and start getting the most out of your data securely and efficiently!