Data Clean Rooms: Centralized, Decentralized, and Hybrid Approaches
In today’s world where data is crucial, companies are always looking for ways to use data to get better insights, improve how they work, and make smarter decisions. But, worries about keeping data private and secure can get in the way of working together and sharing data. Data clean rooms have become a key answer to these problems. They offer a secure way for companies to work with data together, helping them to make the most of data while making sure sensitive information stays safe. There are various frameworks of Data Clean Rooms and I wanted to elaborate on each in this article:
Centralized Data Clean Rooms – A Collaborative Hub for Data-Driven Insights
Centralized data clean rooms provide a centralized environment where organizations can securely store, analyze, and collaborate on data while maintaining control over their data assets. Data is typically stored in a secure location, such as a cloud-based data warehouse, and participants access and analyze the data through controlled mechanisms, ensuring that data is shared and analyzed only for authorized purposes.
This centralized approach offers several advantages, including:
- Streamlined Data Sharing: Centralized data clean rooms streamline data sharing by bringing data together in a single location. This eliminates the need for data transfers between organizations
- Enhanced Data Analysis and Insights: By combining data from multiple sources in a centralized location, organizations can gain deeper insights and identify patterns that would be difficult to detect from individual datasets.
- Simplified Data Governance and Compliance: Centralized data governance ensures consistent data quality, security, and compliance across all participants.
Decentralized Data Clean Rooms – Preserving Privacy while Enabling Collaboration
This approach offers several benefits, particularly for organizations that handle sensitive data:
- Enhanced data privacy and control. Data remains under the control of its respective owners, minimizing the risk of data breaches and unauthorized access.
- Reduced data movement and exposure. By keeping data decentralized, organizations minimize the need for data transfer, reducing the risk of data exposure and unauthorized access.
- Increased trust and collaboration. The decentralized approach fosters trust among participants, encouraging collaboration and data sharing.
Hybrid Data Clean Rooms: Striking a Balance for Flexible Collaboration
Hybrid data clean rooms use a balanced approach, taking advantage of centralized elements for data governance, infrastructure, and security while incorporating decentralized techniques for data processing and collaboration. This hybrid approach provides flexibility and adaptability to address diverse data collaboration needs while maintaining data privacy and security.
Hybrid data clean rooms offer several advantages:
- Tailored solutions: Hybrid data clean rooms can be tailored to meet the specific needs of the organizations involved. This flexibility allows organizations to find the right balance between data privacy and data collaboration.
- Scalability for growing data volumes: Hybrid data clean rooms can scale to accommodate increasing volumes of data and participants. This scalability is important for organizations that expect their data collaboration needs to grow in the future.
Selecting the Right Approach for Data Collaboration
The best approach for data collaboration depends on the specific requirements and priorities of the organizations involved. When choosing an approach for data collaboration, it is important to consider the following factors:
- Data sensitivity: The level of sensitivity of the data involved will impact the security requirements for the data clean room.
- Regulatory environment: The regulatory environment in which the organizations operate will also impact the security requirements.
- Technical capabilities: The technical capabilities of the organizations involved will impact the scalability and cost-effectiveness of the data clean room.
- Collaboration goals: The goals of the collaboration will also impact the best approach to take.
By carefully considering these factors, organizations can select the best approach for data collaboration that meets their specific needs.
Here is a table that I created that highlights the differences
Image credit- Surya Kunju’s LinkedIn
This article is written by Surya Kunju, Marketing Cloud & Retail Media Lead
Surya Kunju
Generative AI | Retail Media | Marketing | Machine Learning | Customer Experience | Commerce | MarTech | AdTech
Surya has over 20 years of experience of global enterprise software experience and a proven track record of leading strategic sales, delivering data-driven marketing solutions, and spearheading business development initiatives. He is passionate about leveraging the power of AI to enrich customer experiences and optimize marketing and brand performance
TransUnion Collaborates with Snowflake for Data Clean Room Solution
TransUnion has announced a collaboration with Samooha by Snowflake, a native data clean room solution on Snowflake, and its marketing solutions line, TruAudience. The collaboration opens the door to an advertising sector driven by data and privacy in the future. TransUnion’s goal of achieving interoperability worldwide has advanced significantly with this move, as its industry-leading consumer data and identity resolution capabilities are now accessible throughout the marketing technology ecosystem. Through Samooha, users will have access to TruAudience’s marketing identity graph, facilitating teamwork and the matching and sharing of customer data without disclosing sensitive information.
User-friendly data clean room
Through Snowflake’s user-friendly clean room environment, clients can now access TransUnion’s marketing identity graph natively within Samooha. This allows them to connect, collaborate, and share offline and online customer data more securely without compromising data integrity or disclosing sensitive information to outside parties. The Snowflake native app Samooha is accessible through the Snowflake Marketplace.
What is Samooha, by Snowflake
In December 2023, Snowflake acquired Samooha. It is a start-up that aims to provide marketers with access to data clean room functionality without the need for assistance from data scientists. Samooha was developed as a native application on the Snowflake data cloud. It meant that no data migration was required for it to function with data in the data warehouse.
What is TransUnion’s TruAudience
TruAudience provides identity resolution, audience building, and targeting capabilities, which were acquired in part through the 2021 Neustar acquisition.
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Why is this noteworthy?
As it works to convince the industry that working with cross-enterprise data in the data warehouse is a better approach than migrating data from specific sources to a platform used for limited (marketing, sales, support, etc.) purposes, Snowflake’s influence in the customer data and advertising ecosystem only grows. It appears that integrating its data clean room solution with the enormous identity-centric dataset from TruAudience is a trend indicator.
TruAudience Marketing Solutions
TruAudience marketing solutions is a comprehensive and interoperable suite of privacy-first marketing solutions. It combines the vast consumer data, sophisticated identity resolution, audience building, and targeting capabilities of TransUnion and Neustar. The product suite also includes credit-informed marketing solutions and closed-loop marketing measurement and attribution. These solutions are powered by TruAudience and are used by top publishers, agencies, brands, data owners, and tech companies.
Here’s what they said
Michael Schoen, EVP, and Head of TruAudience Marketing Solutions at TransUnion said,
“Our partnership with Snowflake, through the Samooha solution, addresses the challenges faced by those who have begun leveraging data clean rooms, but are struggling to unlock their full value due to a lack of identity translation. This native identity management and collaboration solution deepens our integration into the Snowflake platform and Media Data Cloud, building on our transfer-less identity resolution capabilities. Marketers can now improve their collaborations with the same identity used to deduplicate and enrich their 1st party data.”
Kamakshi Sivaramakrishnan, Co-Founder, Samooha added,
“TransUnion’s leading identity data and enrichment augment our data clean room and enable seamless data collaboration across marketing channels, without the need to directly share sensitive customer data. This innovation empowers clients to establish enduring data partnerships, gain invaluable customer insights, and maintain stringent consumer privacy controls.”
