Salesforce has renamed this product several times, and the latest change happened at Dreamforce 2025, when Data Cloud became Data 360 as part of the wider Agentforce 360 platform.
The name has changed more often than most architects would like. But the problem it solves has stayed exactly the same, and it is one of the oldest problems in enterprise technology:
How do we see one customer, when their data is spread across dozens of systems?
What Data 360 Does
Data 360 is Salesforce's data platform for bringing customer data together, making sense of it and putting it to work across Salesforce and beyond.
Connect
Data comes in from Salesforce clouds, websites, mobile apps, other applications and data warehouses. A notable capability is zero-copy access to platforms such as Snowflake, Databricks, Google BigQuery and AWS, which lets data be used without physically copying all of it into Salesforce.
Harmonize
Data from different sources is mapped to a common data model, so a "customer" from the website and a "contact" from the CRM can be understood consistently.
Unify
Identity resolution rules match records that belong to the same person or company and create unified profiles.
Act
Unified data and insights power segments, personalization, analytics, triggered automation and, increasingly, the context that Agentforce AI agents use to answer and act.
Why It Matters for AI
Salesforce now positions Data 360 as the data foundation for Agentforce. That makes sense architecturally: an AI agent answering a customer is only as good as the data it can see. Unified, governed customer context is what turns a generic assistant into a useful one.
Architecture Advice
Start from use cases
The most common mistake I see is trying to load every possible data source first and figure out the value later. Start with two or three concrete use cases, such as a unified service view or a churn-risk segment, and bring in only the data they need.
Respect the data model
Map to the standard data model wherever possible. Building your own structures on top defeats much of the purpose and makes future features harder to adopt.
Design identity resolution carefully
Matching rules that are too loose merge different people. Rules that are too strict leave duplicates. Involve the business in deciding what "the same customer" means.
Govern consent and access
Customer data platforms concentrate sensitive data. Consent, classification and access policies must be designed from day one, not added later.
Watch consumption
Data 360 uses consumption-based pricing. Ingestion, processing and queries all have a cost. Monitor usage and design pipelines efficiently.
Know where it fits
Data 360 is not automatically a replacement for your enterprise data warehouse or lakehouse. In most enterprises it works alongside them, focused on activating customer data in Salesforce and AI experiences.
Final Thought
Product names will keep changing. Salesforce may well rename it again.
What matters is the architectural idea: one trusted, governed view of the customer that applications and AI agents can act on in real time. Get that right, and the name on the box matters much less.