{"id":8251,"date":"2023-04-19T06:58:58","date_gmt":"2023-04-19T06:58:58","guid":{"rendered":"https:\/\/xpon.ai\/?p=8251"},"modified":"2023-08-09T05:30:38","modified_gmt":"2023-08-09T05:30:38","slug":"google-cloud-bigquery-data-clean-rooms","status":"publish","type":"post","link":"https:\/\/xpon.ai\/gb\/resources\/google-cloud-bigquery-data-clean-rooms\/","title":{"rendered":"Secure Data Sharing with BigQuery’s Data Clean Room"},"content":{"rendered":"

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In today\u2019s 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.<\/span><\/p>\n

To address these challenges, Google Cloud <\/span>has just announced BigQuery data clean rooms<\/a><\/strong>, which will be available in Q3. The data clean rooms will be available in all BigQuery regions through Google Cloud\u2019s <\/span>Analytics Hub<\/a><\/strong>. 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.<\/span><\/p>\n

Privacy-first use cases of BigQuery Data Clean Rooms<\/strong><\/h4>\n

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.<\/span><\/p>\n

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.\u00a0<\/span><\/p>\n

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.\u00a0<\/span><\/p>\n

Juliusz Abramczyk, Cloud, ML and Data Engineer for XPON<\/b> highlights, \u201cControlling how data is used also plays a big role in protecting privacy. Enforcing only aggregated queries may safeguard individual users\u2019 details in a database but will still allow for an increase in the accuracy of statistical analysis.\u201d<\/span><\/p>\n

Potential use cases from a digital marketing perspective may include:<\/span><\/p>\n