In today’s digital world, businesses across industries need to collect, combine, and share data to obtain new insights and collaborate with partners. This includes combining first-party data with external sources like third-party vendors or public data sources. However, sharing data across organisational boundaries can pose challenges, particularly concerning regulatory and privacy requirements.

To address these challenges, Google Cloud has just announced BigQuery data clean rooms, which will be available in Q3. The data clean rooms will be available in all BigQuery regions through Google Cloud’s Analytics Hub. This solution will help organisations create and manage secure environments for privacy-centric data sharing, analysis, and collaboration across organisations, without generally needing to move or copy data.

Privacy-first use cases of BigQuery Data Clean Rooms

With BigQuery data clean rooms, large enterprises can solve a plethora of use cases, including being able to optimise their marketing and promotional activities, and improve fraud detection, among others.

Clean rooms allow for granular control over which parts of the dataset are shared and how they can be used. The users and the data providers can determine the required conditions for the data blending to happen – that set of rules and conditions can be thought of as a data contract. 

Should we protect PII? Do we need to remove some columns, or should they simply be hashed or encrypted? Obfuscation and randomisation may also be used to partially retain the distribution of a dataset column but make it impossible to identify records on an individual level. 

Juliusz Abramczyk, Cloud, ML and Data Engineer for XPON highlights, “Controlling how data is used also plays a big role in protecting privacy. Enforcing only aggregated queries may safeguard individual users’ details in a database but will still allow for an increase in the accuracy of statistical analysis.”

Potential use cases from a digital marketing perspective may include:

  • Profile Enrichment: Profile enrichment refers to enhancing customer profiles by collecting, combining, and analysing data from multiple sources. This additional information helps businesses better understand their customers, enabling more targeted and personalised marketing campaigns, improved customer experiences, and optimised decision-making.
  • Audience Analysis: Audience analysis examines and segments an audience based on their characteristics, behaviours, and preferences. This process helps marketers identify specific target groups, create tailored messaging and content, and develop more effective marketing strategies that resonate with the intended audience.
  • Measurement and Attribution: Measurement and attribution involve tracking the performance of marketing efforts across different channels, as well as determining which channels, campaigns, or touchpoints are responsible for driving desired outcomes, such as conversions or sales. This information allows marketers to optimise their marketing strategies by allocating resources more effectively, improving return on investment (ROI), and gaining insights into the customer journey.

Creating and Configuring DataClean Rooms

Google intends for its customers to create and invite partners or other participants to contribute data to BigQuery Data Clean Rooms in minutes via Google Cloud console or APIs.

Data contributors can publish tables or views within a clean room and aggregate, anonymise, and help protect sensitive information. They can also configure analysis rules to restrict the queries that can be run against the data. Adding data to a clean room does not generally require creating a copy or moving the data; it can be shared in place and remain under the control of the data contributor. Finally, data subscriber customers can discover and subscribe to clean rooms to perform privacy-centric queries within their projects.

Shared data within a data clean room can be live and up-to-date — any changes to shared tables or views are immediately available to subscribers. Data contributors also receive aggregated logs and metrics to understand how their data is used within a clean room. There are no additional costs for BigQuery customers associated with using BigQuery data clean rooms. When collaborating with multiple partners, data contributors pay for the data storage, and subscribers of data clean rooms only pay for the queries.

Ash Rane, Global Director – Cloud & Martech for XPON, adds, “The impact of data clean rooms on the industry has been significant, as companies in various sectors, including healthcare, finance, and retail, have realised the benefits of secure data sharing. By leveraging these technologies, such as XPON’s Wondaris®, organisations can gain valuable insights from their data in a privacy-centric way. Data clean rooms have opened up new opportunities for collaboration and innovation while maintaining strict privacy and security controls, making them an invaluable tool for any organisation looking to unlock the power of their data.”

In Summary

BigQuery data clean rooms offer a secure and privacy-centric way for businesses to share and collaborate on data while respecting user privacy and data security. By enabling organisations to combine and analyse data from multiple sources, BigQuery data clean rooms can help businesses make more informed decisions and drive better outcomes.