Google are excited that bidirectional data sharing between BigQuery and Salesforce Data Cloud is now generally available. This will make it easy for customers to enrich their data use cases by combining data across different platforms securely, without the additional cost of building or managing data infrastructure and complex ETL (Extract, Transform, Load) pipelines.
Responding to customer needs quickly is more critical than ever, with a growing number of touchpoints and devices to deliver in-the-moment customer experiences. But it’s becoming increasingly challenging to do so, as the amount of data created and captured continues to grow, and is spread across multiple SaaS applications and analytics platforms.
Last year, Google Cloud and Salesforce announced a partnership that makes it easy for customers to combine their data across BigQuery and Salesforce Data Cloud, and leverage the power of BigQuery and Vertex AI solutions to enrich and unlock new analytics and AI/ML scenarios.
Today, Google are launching general availability of these capabilities, enabling joint Google Cloud and Salesforce customers to securely access their data in the different platforms and different clouds. Customers will be able to access their Salesforce Data Cloud in BigQuery, without having to set up or manage infrastructure, as well as use their Google Cloud data to enrich Salesforce Customer 360 and other applications.
With this announcements, Google Cloud and Salesforce customers benefit from:
- A single pane of glass and serverless data access across platforms with zero ETL
- Governed and secure bi-directional access to their Salesforce Data Cloud data and BigQuery in near real time without needing to create data infrastructure and data pipelines.
- Use their Google Cloud data to enrich Salesforce Customer 360 and Salesforce Data Cloud. Also, the ability to enrich customer data with other relevant datasets, public datasets with minimal data movement.
- Leveraging differentiated Vertex AI and Cloud AI services for predictive analytics, churn modeling and flowing back to customer campaigns through Vertex AI and Einstein Copilot Studio’s integration.
Customers who want to look at their data holistically across Salesforce and Google platforms, spanning cloud boundaries, can do so by leveraging BigQuery Omni and Analytics Hub. This integration allows data and business users including data analysts, marketing analysts, and data scientists, to combine data across Salesforce and Google platforms to analyze, derive insights and run AI/ML pipelines, all in a self-service capacity, without the need to involve data engineering or infrastructure teams.
This integration is fully managed and governed, allowing customers to focus on analytics and insights and avoid several critical business challenges that are typical when integrating critical enterprise systems. These innovations enforce data access and governance policy set for the data by admins. Only datasets that are explicitly shared are available for access and only authorized users are able to share and explore the data. With data spanning multiple clouds and platforms, relevant data is pre-filtered with minimal copying from Salesforce Data Cloud to BigQuery, reducing both egress costs and data engineering overhead.
“Trying to get a handle on our customer data was a nightmare until we seamlessly connected Google Cloud and Salesforce Data Cloud. No more copying data between platforms, no more struggling with complex APIs. It’s revolutionized how we do segmentation, understand our customers, and drive better marketing and service actions.” – Large insurance company in NorthAM
“We faced several challenges in making the most of our customer data. Enriching leads between our first-party BigQuery data and Salesforce was a slow, manual process. It also made creating timely, data-driven lifecycle marketing journeys difficult due to batch data transfers. By seamlessly integrating BigQuery and Salesforce, we’ve transformed these processes. The integration fuels our automated marketing campaigns with real-time data triggers, significantly enhancing customer engagement. Best of all, this solution eliminated the manual overhead of batch data transfers, saving us valuable time and resources. It’s a win-win for our marketing team and our bottom line.” – Large retailer in NorthAM
Easy and secure access to Salesforce Data Cloud from Google Cloud
Customers want to access and combine their marketing, commerce and service data in Salesforce Data Cloud with loyalty and point-of-sale data in Google analytics platforms to derive actionable insights about their customer behavior such as propensity to buy, cross-sell/up-sell recommendations and run highly personalized promotional campaigns. They also want to leverage differentiated Google AI services to build machine learning models on top of combined Salesforce and Google Cloud data for training/predictions, enabling use cases such as churn modeling, customer funnel analysis, market-mix modeling, price elasticity, and A/B test experimentation.
With the launch, customers can get access to their Salesforce Data Cloud data seamlessly through differentiated BigQuery cross-cloud and data sharing capabilities. They can access all the relevant information needed to perform cross-platform analytics in a privacy safe manner with other Google assets, and power ad campaigns. Salesforce Data Cloud Admins can easily share data, directly with the relevant BigQuery users or groups. BigQuery users can easily subscribe to shared datasets through the Analytics Hub UI.
There are several different ways to share information with this platform integration:
- For smaller datasets and ad hoc access, for example to find the store that had the largest sales last year, you can leverage a single cross-cloud join of your Salesforce Data Cloud and Google Cloud datasets, with minimal movement or duplication of data.
- For larger data sets that are powering your executive update, weekly business review or marketing campaign dashboards, you can access the data using cross-cloud materialized views that are automatically and incrementally updated and only bring the incremental data periodically.
Enrich Salesforce Customer 360 with data stored on Google Cloud
Google also hear from customers — especially retailers — that they want to access and combine their data in Salesforce Data Cloud and behavioral data captured in Google Analytics from their websites and mobile apps to build a richer customer 360 profile, derive actionable insights, deliver personalized messaging with rich capabilities of Salesforce Data Cloud. Google are making it easier than ever to break down data silos and give customers seamless real-time access to Google Analytics data within Salesforce Data Cloud and build richer customer profiles, and personalized experiences.
Salesforce Data Cloud customers can use simple point-and-click to connect to their Google Cloud account, select relevant BigQuery datasets and make them available as External Data Lake Objects, providing live access to data. Once they are Data Lake Objects, they behave like native Data Cloud objects to enrich customer 360 data models, derive insights to power Customer 360 models for analytics and personalization. This integration eliminates the need to build and monitor ETL pipelines for data integration, eliminating operational overhead and latencies of the traditional ETL copy approach.
Breaking down walls between Salesforce and Google data
This Google Cloud and Salesforce Data Cloud platform integration empowers organizations to break down data silos, gain actionable insights, and deliver exceptional customer experiences. With seamless data sharing, unified access, and the power of Google AI, this partnership is transforming the way businesses leverage their data for success.
Through unique cross-cloud functionality of BigQuery Omni and data sharing capabilities of Analytics Hub, customers can directly access data stored in Salesforce Data Cloud and combine it with data in Google Cloud to enrich it further for business insights and activation. Customers are not only able to view their data across clouds but perform unparalleled cross-cloud analytics without the need to build custom ETL or move data.