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Data Clean Rooms

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What is a data clean room? 

Due to the end of third-party cookies in Google Chrome, companies are looking for solutions to maintain their advertising processes, such as ad targeting and measurement, while respecting users’ privacy. Data clean rooms are one of the alternatives to address this issue. 

A data clean room, also known as a clean data room or privacy clean room, is a controlled space where data from two or more parties can be brought together for analysis while ensuring privacy and compliance with data protection regulations.


What is the difference between a data room and a data clean room?

In short, a data room is used to securely store and share documents and resources, while a data clean room focuses on data aggregation and anonymisation to protect users’ privacy when sharing information between advertisers and publishers in online advertising and other similar contexts.


What is the difference between a CDP and a data clean room?

A Customer Data Platform (CDP) serves as the core of your first-party data strategy. It acts as the central hub where you merge first, second, and third-party customer data to construct a unified customer profile, a prerequisite for crafting personalised and relevant experiences on a large scale. Therefore, an organisation can establish a connection between its CDP and a data clean room, enabling the anonymization and analysis of first-party data alongside third-party sources. 

It’s essential to note that a CDP does not replicate the functionality of a data clean room, but it does grant data providers and organisations centralised control over their data and its utilisation. By combining a data clean room and a CDP, organisations can effectively manage, process, and analyse data in a manner that prioritises safety, efficiency, and compliance.


Why do brands need data clean rooms and what applications have? 

The main reason for adopting these solutions lies in the growing importance of protecting data privacy in response to regulations. For example, data clean rooms are GDPR compliant. Furthermore, all parties involved exercise full control over their data, which is typically subject to full encryption at all stages of the process and includes a rigorous governance and permission systems that allow each party to define what data is accessible and how it can be used. Finally, but equally important, these solutions offer a privacy-focused computing infrastructure, along with tools for querying and aggregate reporting, facilitating the integration of data sets.

DCRs are being used today for multiple advertising use cases:

  • Help advertisers precisely target and engage specific audiences.
  • DCRs leverage consumer insights from internal and external data sources to inform decision-making.
  • They continuously enhance the customer experience.
  • DCRs improve the accuracy of reach and frequency measurements.
  • They enable in-depth campaign analysis for refining strategies.

How do data clean rooms work?

As mentioned before, a  data clean room is a controlled environment designed to facilitate secure data collaboration and analysis between different parties while ensuring privacy and compliance with data protection regulations. Here’s how a data clean room typically works:

  1. Data Integration and Segregation:
    • Data owners (different parties contributing data) retain control and ownership of their respective datasets
    • Raw data is not directly shared or transferred between parties. Instead, it stays with the original owners
  2. Data Transformation:
    • Before entering the clean room, data is subjected to privacy-preserving techniques to protect individual identities. These techniques may include:
      1. Anonymization: Removing or encrypting personally identifiable information (PII) from the data
      2. Aggregation: Combining data at an aggregate level (e.g., summing up values) to prevent identification of individual records
      3. Perturbation: Adding noise or randomization to the data to further protect privacy
      4. Tokenization or Hashing: Transforming data into non-reversible representations
  3. Secure Environment:
    • The clean room itself is a secure computing environment with controlled access. It can be a physical location with strict access controls or a virtual environment with robust security measures
  4. Analytical Tools:
    • Analysts from different parties can access the clean room to perform computations, analytics, and generate insights using the transformed data
    • The tools within the clean room allow for querying and processing of data without revealing individual identities
  5. Data Usage Policies:
    • Agreements regarding how the data can be used, what types of analysis can be performed, and the scope of insights that can be derived
  6. Auditing and Monitoring:
    • The clean room environment is monitored and audited to ensure compliance with privacy regulations and to detect any potential breaches or unauthorised access
  7. Results Extraction:
    • After analysis, only aggregated, anonymized results are extracted from the clean room. These results do not contain information that can identify specific individuals
  8. Compliance and Governance:
    • The clean room operates under strict compliance with data protection regulations such as GDPR, HIPAA, or other relevant laws

Data clean rooms are especially valuable in industries where sensitive data is involved, such as healthcare, finance, and marketing. They enable organisations to collaborate on data-driven initiatives while safeguarding individual privacy and adhering to legal and regulatory requirements.


What are the disadvantages of data clean rooms? 

Data clean rooms represent a significant investment, and while there are alternatives provided by major security service providers, the associated logistical and operational challenges can be a burden for all parties involved. 

The success of these rooms depends largely on the willingness to share data, but not all advertisers are willing to disclose detailed transactional data, largely due to unfounded concerns about potential privacy risks. For instance, data is more prevalent in healthcare compared to the automotive sector. Therefore, when data is shared on a limited basis, results are often incomplete, leading to approximate measurements at best. In addition, universal implementation standards have not yet been established, meaning that data collection and preparation, which vary in format across multiple sectors, may require a lengthy process.

It’s worth mentioning that in certain situations, individual user data remains accessible, such as on Android devices and for iOS users who provide consent, potentially mitigating the need for an immediate implementation of a data clean room solution. However, data clean rooms present a highly promising solution to the existing obstacles confronting the programmatic advertising sector in a manner that prioritises user privacy.


Do you have any questions regarding data clean rooms?

Do not hesitate to contact us or follow us on our LinkedIn! 

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