Businesses across industries are increasingly combining their data with external sources or collaborating directly with partners to gain new insights and unlock business value. As more data is being shared within and across organizational boundaries, the security and privacy of data — especially sensitive data — is more important than ever.
To help organizations solve these challenges, we announced that data clean rooms were coming to BigQuery to help organizations create and manage secure environments for privacy-centric data sharing and analysis. BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with the data needs of the business, with built-in AI/ML and business intelligence for insights. With enterprise-grade protection and mission-critical workloads, BigQuery data clean rooms can offer advanced security and privacy controls to help ensure that your teams can conduct meaningful analyses while protecting the underlying data.
Today, BigQuery data clean rooms are now available in preview across all Analytics Hub regions, the data sharing platform within BigQuery that enables data sharing. With this capability, you can now:
Create and deploy clean rooms in a few clicks, collaborating across organizational boundaries, without needing to move or copy the underlying data in BigQuery
Perform n-way joins, including enriching your existing data with thousands of public and commercial datasets available in Analytics Hub
Use analysis rules and privacy policies to help protect your sensitive data
Leverage the same analysis rules as leaders in the data clean room space, such as aggregation thresholding to help protect sensitive data
Prevent your data and query results from being copied or exported
Manage, track, and govern usage of shared data in clean rooms in a single pane of glass
Data clean rooms have many use cases across various industries. In the financial sector, sensitive data from various sources can be merged within clean rooms, leading to improved fraud detection and the creation of accurate credit risk scores. For retailers, point-of-sale data can be combined with marketing information, enabling more effective optimization of promotional strategies and data sharing with Consumer Packaged Goods suppliers. For publishers, data clean rooms help make it easier to securely share valuable data with advertisers that can be used to optimize advertising effectiveness.
Google Cloud customers like L’Oréal are excited to start using BigQuery data clean rooms. “We are intensively using Google Cloud for data analytics globally and are excited to be using BigQuery to build data clean rooms. In addition to saving time, money, and a highly reduced footprint because of data movement reduction and data sharing, we’re excited to get more value from the retailers that work with L’Oréal for a secure and streamlined data sharing experience that preserves user privacy.” – Antoine Castex, Data Platform Architect, L’Oréal
As clean rooms gain rapid traction across diverse verticals, marketers emerge as early and enthusiastic adopters, leveraging clean rooms for tasks such as campaign assessment. But as marketing sophistication increases, data can become difficult to use if it’s not in a clean room, due to the need to join various data sources — both owned sources and through partnerships — securely and with privacy protections.
Ads Data Hub for Marketers in BigQuery Analytics Hub
Existing customers of Ads Data Hub for Marketers are already able to analyze their first-party data in BigQuery and campaign data to understand their end-customer’s path to purchase. Early next year, customers will also be able to connect with Ads Data Hub for Marketers through BigQuery Analytics Hub, alongside other BigQuery data clean rooms.
Ads Data Hub for Marketers will retain its rigorous privacy checks that help protect the personal data of users online while enabling comprehensive analytics.
Partners enabling data clean rooms with BigQuery
Habu, a data collaboration platform for privacy-centric data orchestration, is launch partner for BigQuery data clean rooms. Customers like Disney Advertising use Google Cloud and Habu to help securely share data and surface insights to business users.
“Our partnership with Google Cloud exemplifies our commitment to deliver frictionless collaboration across an open technology ecosystem. Democratizing clean room access and delivering the privacy-centric tools that brands demand is opening up new avenues for growth for our shared customers.” – Matt Kilmartin, Co-founder and CEO, Habu
LiveRamp on Google Cloud can enable privacy-centric identity resolution within BigQuery to drive more effective data partnerships. LiveRamp’s solution in BigQuery unlocks the value of a customer’s first-party data and establishes a privacy-centric identity data model that can accurately:
Improve consolidation of both offline and online records for a more accurate and holistic view of customer audience profiles
Activate audiences securely with improved match rates for greater reach and measurement quality
Connect customer and prospect data with online media reports and partner data elements to help improve customer journeys and attribution insights using ML models
“LiveRamp has developed deep support for accurate and privacy-centric data connectivity throughout the Google ecosystem. As one of the first clean room providers on Google Cloud with our Data Collaboration Platform, we have been very excited to watch the evolution of Analytics Hub into a powerful, native clean room solution for the entire BigQuery ecosystem. We’re now helping global clients more easily collaborate in Analytics Hub with an ‘enterprise ID,’ an optimal key for connecting data and improving analytic accuracy between clean room partners. A purpose-built, first-party configurable identity graph — powered by LiveRamp’s proprietary third-party graph — drives greater connectivity, more flexible analytics for both individual and household levels of customer modeling, and ensures that sensitive customer data processing on BigQuery is performed safely.” – Max Parris, Head of Identity Resolution Products, LiveRamp
We are partnering with TransUnion to bring their TruAudience transfer-less Identity Resolution Service in BigQuery. TransUnion customers can execute identity resolution without data leaving the client’s BigQuery environment. The new integration can enable marketers to improve their data quality, get better insights, and collaborate in BigQuery data clean rooms in a privacy-first way. BigQuery’s powerful interoperability features help enable TransUnion to integrate their existing powerful machine-learning based entity resolution service with few code changes.
“A robust view of identity forms the basis for effective marketing analytics. We’re excited to build on BigQuery so that clients can enhance the performance of their advanced data analytics through identity resolution, enrichment, and deduplication. Our partnership can help marketers access data and collaborate with partners without sharing sensitive customer information.” – Ryan Engle VP of Identity Solutions, Credit Marketing, and Platform Integrations, TransUnion
Tumult Labs helps BigQuery customers implement differential privacy in clean rooms while minimizing compliance and privacy risk.
“We are thrilled to help BigQuery data clean rooms customers unlock new and valuable insights from their data without compromising on privacy. Our proven differential privacy platform provides rock-solid output controls in clean rooms, ensuring that aggregated data can be shared with rigorous guarantees of privacy protection. We look forward to building on our strategic partnership with BigQuery to provide even more customers easier access to world-class privacy technology.” – Gerome Miklau, Co-founder and CEO, Tumult Labs
Want to learn more about BigQuery data clean rooms? Sign up for the public preview today. For marketers who are looking to use a clean room to better understand Google and YouTube campaign performance and leverage their first party data in a privacy-centric way, consider Google’s advertising measurement solution Ads Data Hub for Marketers, built on BigQuery.
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