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<div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">The rise of data collaboration and use of external data sources highlights the need for robust privacy and compliance measures. In this evolving data ecosystem, businesses are turning to clean rooms to share data in  low-trust environments. Clean rooms enable secure analysis of sensitive data assets, allowing organizations to unlock insights without compromising on privacy.</span></p>
<p><span style="vertical-align: baseline;">To facilitate this type of data collaboration, we launched the </span><a href="https://cloud.google.com/blog/products/data-analytics/bigquery-data-clean-rooms-now-available-in-public-preview"><span style="text-decoration: underline; vertical-align: baseline;">preview of data clean rooms</span></a><span style="vertical-align: baseline;"> last year. Today, we are excited to announce that BigQuery data clean rooms is now </span><strong style="vertical-align: baseline;">generally available.</strong></p>
<p><span style="vertical-align: baseline;">Backed by BigQuery, customers can now share data in place with </span><a href="https://cloud.google.com/bigquery/docs/analysis-rules"><span style="text-decoration: underline; vertical-align: baseline;">analysis rules</span></a><span style="vertical-align: baseline;"> to protect the underlying data. This launch includes a streamlined data contributor and subscriber experience in the Google Cloud console, as well as highly requested capabilities such as:</span></p>
<ul>
<li role="presentation"><strong style="vertical-align: baseline;">Join restrictions: </strong><span style="vertical-align: baseline;">Limits the joins that can be on specific columns for data shared in a clean room, preventing unintended or unauthorized connections between data. </span></li>
<li role="presentation"><strong style="vertical-align: baseline;">Differential privacy analysis rule: </strong><span style="vertical-align: baseline;">Enforces that all queries on your shared data use </span><a href="https://cloud.google.com/bigquery/docs/differential-privacy"><span style="text-decoration: underline; vertical-align: baseline;">differential privacy</span></a><span style="vertical-align: baseline;"> with the parameters that you specify. The privacy budget that you specify also prevents further queries on that data when the budget is exhausted.</span></li>
<li role="presentation"><strong style="vertical-align: baseline;">List overlap analysis rule: </strong><span style="vertical-align: baseline;">Restricts the output to only display the intersecting rows between two or more views joined in a query.</span></li>
<li><strong style="vertical-align: baseline;">Usage metrics on views: </strong><span style="vertical-align: baseline;">Data owners or contributors see aggregated metrics on the views and tables shared in a clean room.</span></li>
</ul></div>
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</div>
    </div>

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<div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">Using data clean rooms in BigQuery does not require creating copies of or moving sensitive data. Instead, the data can be shared directly from your BigQuery project and you remain in full control. Any updates you make to your shared data are reflected in the clean room in real-time, ensuring everyone is working with the most current data.</span></p>
<h3><strong style="vertical-align: baseline;">Create and deploy clean rooms in BigQuery</strong></h3>
<p><span style="vertical-align: baseline;">BigQuery data clean rooms are available in all BigQuery regions. You can set up a clean room environment using the Google Cloud console or using APIs. During this process, you set permissions and invite collaborators within or outside organizational boundaries to contribute or subscribe to the data.</span></p></div>
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</div>
    </div>

</div>
<div class="block-paragraph_advanced"><h3><strong style="vertical-align: baseline;">Enforce analysis rules to protect underlying data</strong></h3>
<p><span style="vertical-align: baseline;">When sharing data into a clean room, you can configure </span><a href="https://cloud.google.com/bigquery/docs/analysis-rules"><span style="text-decoration: underline; vertical-align: baseline;">analysis rules</span></a><span style="vertical-align: baseline;"> to protect the underlying data and determine how the data can be analyzed. BigQuery data clean rooms support multiple analysis rules including aggregation, differential privacy, list overlap, and join restrictions. The new user experience within Cloud console lets data contributors configure these rules without needing to use SQL. </span></p></div>
<div class="block-image_full_width">

</div>
    </div>

</div>
<div class="block-paragraph_advanced"><p><span style="vertical-align: baseline;">Lastly, by default, a clean room employs restricted egress to prevent subscribers from exporting or copying the underlying data. However, data contributors can choose to allow the export and copying of query results for specific use cases, such as activation.</span></p>
<h3><strong style="vertical-align: baseline;">Monitor usage and stay in control of your data</strong></h3>
<p><span style="vertical-align: baseline;">The data owner or contributor is always in control of their respective data in a clean room. At any time, a data contributor can revoke access to their data. Additionally, as the clean room owner, you can adjust access using subscription management or privacy budgets to prevent subscribers from performing further analysis. Additionally, data contributors receive aggregated logs and metrics, giving them insights into how their data is being used within the clean room. This promotes both transparency and a clearer understanding of the collaborative process. </span></p>
<h3><strong style="vertical-align: baseline;">What BigQuery data clean room customers are saying </strong></h3>
<p><span style="vertical-align: baseline;">Customers across all industries are already seeing tremendous success with BigQuery data clean rooms. Here’s what some of our early adopters and partners had to say:</span></p>
<p style="padding-left: 40px;"><span style="font-style: italic; vertical-align: baseline;">“With BigQuery data clean rooms, we are now able to share and monetize more impactful data with our partners while maintaining our customers' and strategic data protection.”</span><span style="vertical-align: baseline;"> -  </span><strong style="vertical-align: baseline;">Guillaume Blaquiere, Group Data Architect, Carrefour</strong></p>
<p style="padding-left: 40px;"><span style="font-style: italic; vertical-align: baseline;">“Data clean rooms in BigQuery is a real accelerator for L'Oréal to be able to share, consume, and manage data in a secure and sustainable way with our partners.”</span><span style="vertical-align: baseline;"> -  </span><strong style="vertical-align: baseline;">Antoine Castex, Enterprise Data Architect, L’Oréal</strong></p>
<p style="padding-left: 40px;"><span style="font-style: italic; vertical-align: baseline;">“BigQuery data clean rooms equip marketing teams with a powerful tool for advancing privacy-focused data collaboration and advanced analytics in the face of growing signal loss. LiveRamp and Habu, which independently were each early partners of BigQuery data clean rooms, are excited to build on top of this foundation with our combined interoperable solutions: a powerful application layer, powered by Habu, accelerates the speed to value for technical and business users alike, while cloud-native identity, powered by RampID in Google Cloud, maximizes data fidelity and ecosystem connectivity for all collaborators. With BigQuery data clean rooms, enterprises will be empowered to drive more strategic decisions with actionable, data-driven insights.”</span><span style="vertical-align: baseline;"> - </span><strong style="vertical-align: baseline;">Roopak Gupta, VP of Engineering, LiveRamp</strong></p>
<p style="padding-left: 40px;"><span style="vertical-align: baseline;">“In today’s marketing landscape, where resources are limited and the ecosystem is fragmented, solutions like the data clean room we are building with Google Cloud can help reduce friction for our clients. This collaborative clean room ensures privacy and security while allowing Stagwell to integrate our proprietary data to create custom audiences across our product and service offerings in the Stagwell Marketing Cloud. With the continued partnership of Google Cloud, we can offer our clients integrated Media Studio solutions that connect brands with relevant audiences, improving customer journeys and making media spend more efficient.” - </span><strong style="vertical-align: baseline;">Mansoor Basha, Chief Technology Officer, Stagwell Marketing Cloud</strong></p>
<p style="padding-left: 40px;"><span style="vertical-align: baseline;">“We are extremely excited about the General Availability announcement of BigQuery data clean rooms. It's been great collaborating with Google Cloud on this initiative and it is great to see it come to market.. This release enables production-grade secure data collaboration for the media and advertising industry, unlocking more interoperable planning, activation and measurement use cases for our ecosystem.” - </span><strong style="vertical-align: baseline;">Bosko Milekic, Chief Product Officer, Optable</strong></p>
<h3><strong style="vertical-align: baseline;">Next steps</strong></h3>
<p><span style="vertical-align: baseline;">Whether you're an advertiser trying to optimize your advertising effectiveness with a publisher, or a retailer improving your promotional strategy with a CPG, BigQuery data clean rooms can help. Get started today by using this </span><a href="https://cloud.google.com/bigquery/docs/data-clean-rooms"><span style="text-decoration: underline; vertical-align: baseline;">guide</span></a><span style="vertical-align: baseline;">, starting a </span><a href="https://cloud.google.com/bigquery/docs/sandbox"><span style="text-decoration: underline; vertical-align: baseline;">free trial with BigQuery</span></a><span style="vertical-align: baseline;">, or contacting the </span><a href="https://cloud.google.com/contact"><span style="text-decoration: underline; vertical-align: baseline;">Google Cloud sales team</span></a><span style="vertical-align: baseline;">. </span></p></div>

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--- !ruby/object:Feedjira::Parser::RSSEntry
published: 2024-04-05 16:00:00.000000000 Z
carlessian_info:
  news_filer_version: 2
  newspaper: Google Cloud Blog
  macro_region: Technology
entry_id: !ruby/object:Feedjira::Parser::GloballyUniqueIdentifier
  guid: https://cloud.google.com/blog/products/data-analytics/bigquery-data-clean-rooms-now-generally-available/
title: Privacy-preserving data sharing now generally available with BigQuery data
  clean rooms
categories:
- Data Analytics
summary: "<div class=\"block-paragraph_advanced\"><p><span style=\"vertical-align:
  baseline;\">The rise of data collaboration and use of external data sources highlights
  the need for robust privacy and compliance measures. In this evolving data ecosystem,
  businesses are turning to clean rooms to share data in  low-trust environments.
  Clean rooms enable secure analysis of sensitive data assets, allowing organizations
  to unlock insights without compromising on privacy.</span></p>\n<p><span style=\"vertical-align:
  baseline;\">To facilitate this type of data collaboration, we launched the </span><a
  href=\"https://cloud.google.com/blog/products/data-analytics/bigquery-data-clean-rooms-now-available-in-public-preview\"><span
  style=\"text-decoration: underline; vertical-align: baseline;\">preview of data
  clean rooms</span></a><span style=\"vertical-align: baseline;\"> last year. Today,
  we are excited to announce that BigQuery data clean rooms is now </span><strong
  style=\"vertical-align: baseline;\">generally available.</strong></p>\n<p><span
  style=\"vertical-align: baseline;\">Backed by BigQuery, customers can now share
  data in place with </span><a href=\"https://cloud.google.com/bigquery/docs/analysis-rules\"><span
  style=\"text-decoration: underline; vertical-align: baseline;\">analysis rules</span></a><span
  style=\"vertical-align: baseline;\"> to protect the underlying data. This launch
  includes a streamlined data contributor and subscriber experience in the Google
  Cloud console, as well as highly requested capabilities such as:</span></p>\n<ul>\n<li
  role=\"presentation\"><strong style=\"vertical-align: baseline;\">Join restrictions:
  </strong><span style=\"vertical-align: baseline;\">Limits the joins that can be
  on specific columns for data shared in a clean room, preventing unintended or unauthorized
  connections between data. </span></li>\n<li role=\"presentation\"><strong style=\"vertical-align:
  baseline;\">Differential privacy analysis rule: </strong><span style=\"vertical-align:
  baseline;\">Enforces that all queries on your shared data use </span><a href=\"https://cloud.google.com/bigquery/docs/differential-privacy\"><span
  style=\"text-decoration: underline; vertical-align: baseline;\">differential privacy</span></a><span
  style=\"vertical-align: baseline;\"> with the parameters that you specify. The privacy
  budget that you specify also prevents further queries on that data when the budget
  is exhausted.</span></li>\n<li role=\"presentation\"><strong style=\"vertical-align:
  baseline;\">List overlap analysis rule: </strong><span style=\"vertical-align: baseline;\">Restricts
  the output to only display the intersecting rows between two or more views joined
  in a query.</span></li>\n<li><strong style=\"vertical-align: baseline;\">Usage metrics
  on views: </strong><span style=\"vertical-align: baseline;\">Data owners or contributors
  see aggregated metrics on the views and tables shared in a clean room.</span></li>\n</ul></div>\n<div
  class=\"block-image_full_width\">\n\n\n\n\n\n\n  \n    <div class=\"article-module
  h-c-page\">\n      <div class=\"h-c-grid\">\n  \n\n    <figure class=\"article-image--large\n
  \     \n      \n        h-c-grid__col\n        h-c-grid__col--6 h-c-grid__col--offset-3\n
  \       \n        \n      \"\n      >\n\n      \n      \n        \n        <img\n
  \           src=\"https://storage.googleapis.com/gweb-cloudblog-publish/images/1_RqH5w9g.max-1000x1000.png\"\n
  \       \n          alt=\"1\">\n        \n        </a>\n      \n    </figure>\n\n
  \ \n      </div>\n    </div>\n  \n\n\n\n\n</div>\n<div class=\"block-paragraph_advanced\"><p><span
  style=\"vertical-align: baseline;\">Using data clean rooms in BigQuery does not
  require creating copies of or moving sensitive data. Instead, the data can be shared
  directly from your BigQuery project and you remain in full control. Any updates
  you make to your shared data are reflected in the clean room in real-time, ensuring
  everyone is working with the most current data.</span></p>\n<h3><strong style=\"vertical-align:
  baseline;\">Create and deploy clean rooms in BigQuery</strong></h3>\n<p><span style=\"vertical-align:
  baseline;\">BigQuery data clean rooms are available in all BigQuery regions. You
  can set up a clean room environment using the Google Cloud console or using APIs.
  During this process, you set permissions and invite collaborators within or outside
  organizational boundaries to contribute or subscribe to the data.</span></p></div>\n<div
  class=\"block-image_full_width\">\n\n\n\n\n\n\n  \n    <div class=\"article-module
  h-c-page\">\n      <div class=\"h-c-grid\">\n  \n\n    <figure class=\"article-image--large\n
  \     \n      \n        h-c-grid__col\n        h-c-grid__col--6 h-c-grid__col--offset-3\n
  \       \n        \n      \"\n      >\n\n      \n      \n        \n        <img\n
  \           src=\"https://storage.googleapis.com/gweb-cloudblog-publish/images/2_icFyF0J.max-1000x1000.png\"\n
  \       \n          alt=\"2\">\n        \n        </a>\n      \n    </figure>\n\n
  \ \n      </div>\n    </div>\n  \n\n\n\n\n</div>\n<div class=\"block-paragraph_advanced\"><h3><strong
  style=\"vertical-align: baseline;\">Enforce analysis rules to protect underlying
  data</strong></h3>\n<p><span style=\"vertical-align: baseline;\">When sharing data
  into a clean room, you can configure </span><a href=\"https://cloud.google.com/bigquery/docs/analysis-rules\"><span
  style=\"text-decoration: underline; vertical-align: baseline;\">analysis rules</span></a><span
  style=\"vertical-align: baseline;\"> to protect the underlying data and determine
  how the data can be analyzed. BigQuery data clean rooms support multiple analysis
  rules including aggregation, differential privacy, list overlap, and join restrictions.
  The new user experience within Cloud console lets data contributors configure these
  rules without needing to use SQL. </span></p></div>\n<div class=\"block-image_full_width\">\n\n\n\n\n\n\n
  \ \n    <div class=\"article-module h-c-page\">\n      <div class=\"h-c-grid\">\n
  \ \n\n    <figure class=\"article-image--large\n      \n      \n        h-c-grid__col\n
  \       h-c-grid__col--6 h-c-grid__col--offset-3\n        \n        \n      \"\n
  \     >\n\n      \n      \n        \n        <img\n            src=\"https://storage.googleapis.com/gweb-cloudblog-publish/images/3_LOrq102.max-1000x1000.png\"\n
  \       \n          alt=\"3\">\n        \n        </a>\n      \n    </figure>\n\n
  \ \n      </div>\n    </div>\n  \n\n\n\n\n</div>\n<div class=\"block-paragraph_advanced\"><p><span
  style=\"vertical-align: baseline;\">Lastly, by default, a clean room employs restricted
  egress to prevent subscribers from exporting or copying the underlying data. However,
  data contributors can choose to allow the export and copying of query results for
  specific use cases, such as activation.</span></p>\n<h3><strong style=\"vertical-align:
  baseline;\">Monitor usage and stay in control of your data</strong></h3>\n<p><span
  style=\"vertical-align: baseline;\">The data owner or contributor is always in control
  of their respective data in a clean room. At any time, a data contributor can revoke
  access to their data. Additionally, as the clean room owner, you can adjust access
  using subscription management or privacy budgets to prevent subscribers from performing
  further analysis. Additionally, data contributors receive aggregated logs and metrics,
  giving them insights into how their data is being used within the clean room. This
  promotes both transparency and a clearer understanding of the collaborative process. </span></p>\n<h3><strong
  style=\"vertical-align: baseline;\">What BigQuery data clean room customers are
  saying </strong></h3>\n<p><span style=\"vertical-align: baseline;\">Customers across
  all industries are already seeing tremendous success with BigQuery data clean rooms.
  Here’s what some of our early adopters and partners had to say:</span></p>\n<p style=\"padding-left:
  40px;\"><span style=\"font-style: italic; vertical-align: baseline;\">“With BigQuery
  data clean rooms, we are now able to share and monetize more impactful data with
  our partners while maintaining our customers' and strategic data protection.”</span><span
  style=\"vertical-align: baseline;\"> -  </span><strong style=\"vertical-align: baseline;\">Guillaume
  Blaquiere, Group Data Architect, Carrefour</strong></p>\n<p style=\"padding-left:
  40px;\"><span style=\"font-style: italic; vertical-align: baseline;\">“Data clean
  rooms in BigQuery is a real accelerator for L'Oréal to be able to share, consume,
  and manage data in a secure and sustainable way with our partners.”</span><span
  style=\"vertical-align: baseline;\"> -  </span><strong style=\"vertical-align: baseline;\">Antoine
  Castex, Enterprise Data Architect, L’Oréal</strong></p>\n<p style=\"padding-left:
  40px;\"><span style=\"font-style: italic; vertical-align: baseline;\">“BigQuery
  data clean rooms equip marketing teams with a powerful tool for advancing privacy-focused
  data collaboration and advanced analytics in the face of growing signal loss. LiveRamp
  and Habu, which independently were each early partners of BigQuery data clean rooms,
  are excited to build on top of this foundation with our combined interoperable solutions:
  a powerful application layer, powered by Habu, accelerates the speed to value for
  technical and business users alike, while cloud-native identity, powered by RampID
  in Google Cloud, maximizes data fidelity and ecosystem connectivity for all collaborators.
  With BigQuery data clean rooms, enterprises will be empowered to drive more strategic
  decisions with actionable, data-driven insights.”</span><span style=\"vertical-align:
  baseline;\"> - </span><strong style=\"vertical-align: baseline;\">Roopak Gupta,
  VP of Engineering, LiveRamp</strong></p>\n<p style=\"padding-left: 40px;\"><span
  style=\"vertical-align: baseline;\">“In today’s marketing landscape, where resources
  are limited and the ecosystem is fragmented, solutions like the data clean room
  we are building with Google Cloud can help reduce friction for our clients. This
  collaborative clean room ensures privacy and security while allowing Stagwell to
  integrate our proprietary data to create custom audiences across our product and
  service offerings in the Stagwell Marketing Cloud. With the continued partnership
  of Google Cloud, we can offer our clients integrated Media Studio solutions that
  connect brands with relevant audiences, improving customer journeys and making media
  spend more efficient.” - </span><strong style=\"vertical-align: baseline;\">Mansoor
  Basha, Chief Technology Officer, Stagwell Marketing Cloud</strong></p>\n<p style=\"padding-left:
  40px;\"><span style=\"vertical-align: baseline;\">“We are extremely excited about
  the General Availability announcement of BigQuery data clean rooms. It's been great
  collaborating with Google Cloud on this initiative and it is great to see it come
  to market.. This release enables production-grade secure data collaboration for
  the media and advertising industry, unlocking more interoperable planning, activation
  and measurement use cases for our ecosystem.” - </span><strong style=\"vertical-align:
  baseline;\">Bosko Milekic, Chief Product Officer, Optable</strong></p>\n<h3><strong
  style=\"vertical-align: baseline;\">Next steps</strong></h3>\n<p><span style=\"vertical-align:
  baseline;\">Whether you're an advertiser trying to optimize your advertising effectiveness
  with a publisher, or a retailer improving your promotional strategy with a CPG,
  BigQuery data clean rooms can help. Get started today by using this </span><a href=\"https://cloud.google.com/bigquery/docs/data-clean-rooms\"><span
  style=\"text-decoration: underline; vertical-align: baseline;\">guide</span></a><span
  style=\"vertical-align: baseline;\">, starting a </span><a href=\"https://cloud.google.com/bigquery/docs/sandbox\"><span
  style=\"text-decoration: underline; vertical-align: baseline;\">free trial with
  BigQuery</span></a><span style=\"vertical-align: baseline;\">, or contacting the
  </span><a href=\"https://cloud.google.com/contact\"><span style=\"text-decoration:
  underline; vertical-align: baseline;\">Google Cloud sales team</span></a><span style=\"vertical-align:
  baseline;\">. </span></p></div>"
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url: https://cloud.google.com/blog/products/data-analytics/bigquery-data-clean-rooms-now-generally-available/
author: Nikhil Gaekwad

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