Salesforce Data Cloud Roadmap

While a lot of emphasis was placed on Einstein GPT at this week’s TrailblazerDX ’23, it was nice to see a number of core updates being delivered for Data Cloud with the Winter and Summer ā€™23 releases. The picture of Data Cloud as the lynchpin in Salesforce’s data strategy came more into focus. At Dreamforce 2023 many of the announcements about Data Cloud were somewhat aspirational and far off, but now the features and functionality are actually being delivered. There is still a lot of work to do in building out core functionality and explaining these innovations to customers, but Iā€™m looking forward to the year ahead. It feels like opportunity.

Data Cloud Roadmap

Genie Data in Flow (Winter ’23) – Create Genie-triggered workflows to update and act on unified data. The ability to trigger workflows is key to enabling use cases across the Customer 360.

Bring Your Own Lake (Summer ’23) – Securely share Genie data with your existing data lake without copying data. Starts with Snowflake. Clients and partners are excited about this one. The way it is worded makes it sound like this may only be the ability to share data out to Snowflake. Of course, we are all very eager to also have the ability to share data inbound from Snowflake and other data platforms!

Bring Your Own AI Model (Summer ’23) – Securely share Genie data with your AI platform of choice starting with Sagemaker. This is exciting and will be critical when Einstein GPT becomes available.

Near Real Time Identity Resolution (Winter ’23) – Resolve identities quickly to deliver personalized engagement as streaming engagement data changes. It’s good to see the speed of operations across the Data Cloud getting more real-time. This likely comes with a lot of caveats as identity resolution is also dependent on the overall speed of batch ingestion processes from other systems.

Data Spaces (Summer ’23) – Partition your data, metadata, and processes by business unit. The Data Cloud security model is different from core Salesforce. A lot more fine grained control of data and data security is coming with Data Spaces. I’m looking to understand how the data sharing and security models will ultimately align across the C360.

Data Prep Studio (Summer ’23) – Designed for B2C scale, run Data Prep over data ingested via the Genie Connector Framework. The ability to manipulate data as it is ingested is currently limited. It seems there is going to be much greater control of data transformations as data is ingested into Data Cloud.

Native Connectors – More native connectors have arrived for Tableau and GCP with Azure and other platform integrations coming.

Mulesoft Anypoint Exchange – Mulesoft allows for connections to any array of platforms that don’t yet have native connectors to Data Cloud.

The Salesforce Data Cloud is rapidly maturing with new features and functionality. The key will be explaining all of this innovation to customers and developing the core use cases that demonstrate value. Data Cloud’s new features are impressive and it makes sense that running an organization’s systems of engagement off of a common data layer will reduce complexity and wasted effort, however, there is still a lot of confusion among customers about what Data Cloud is and it’s unique value proposition. I look forward to continuing to add clarity to the conversation around Data Cloud and data strategy. Check out the links below, look out for upcoming posts, and feel free to reach out with questions.