Salesforce Data 360 explained
Data 360, formerly Data Cloud, is Salesforce's unified data and context layer for CRM, analytics and AI agents.
Data 360 is Salesforce's unified, real-time data and context layer for CRM, analytics and AI agents. It was called Data Cloud until the October 2025 rebrand, and Salesforce's own FAQ describes Data 360 as "the official new name for what was previously called Data Cloud". Third-party sources also list earlier names such as Salesforce Genie and Salesforce CDP; only Data Cloud is confirmed by Salesforce as a former name.
What it is for
Most organisations hold customer data in many systems: CRM, ecommerce, data warehouses, support tools and marketing platforms. Data 360 brings that data together, resolves it into unified profiles, and makes it available for segmentation, analytics, automation and AI agents across Salesforce.
Core capabilities
| Capability | What it does | Status |
|---|---|---|
| Zero Copy integration | Works with data in Snowflake, Databricks, BigQuery and others without copying it | Available |
| Identity resolution | Matches and reconciles records into unified profiles | Available |
| Segmentation and activation | Builds audiences and sends them to other systems | Available |
| Agent Context Engine | Supplies context to AI agents | Listed by Salesforce; status not stated |
| Data 360 MCP Server | Exposes nearly 200 Data 360 APIs to agents | GA since August 2026 |
| Agentic Segmentation and Activation | Conversational audience building and an Activation Agent | Winter '27, status not stated |
| Agent Memory | Shared memory across agents, governed by Data 360 policies | Winter '27, status not stated |
| Salesforce Catalog | Plain-language business-context catalogue | Winter '27, status not stated |
How it connects with other Salesforce products
Data 360 sits under Marketing Cloud Next and Salesforce Personalization, and grounds Agentforce agents in customer data. Tableau Next runs on the Salesforce Platform and Data 360. In Salesforce's AIforce architecture, Data 360 is one of the three foundations alongside Customer 360 and Agentforce.
Who it suits
- Organisations with customer data in several systems that need one view.
- Teams planning AI agents that must answer from accurate, current data.
- Businesses with a data warehouse that want to use it in Salesforce without duplication.
Planning an implementation
- 01
Define use cases
Name the segments, insights or agent tasks that justify the work.
- 02
Inventory sources
Decide what to ingest and what to access through zero copy.
- 03
Check data quality
Clean identifiers such as email and phone before identity resolution.
- 04
Map to the data model
Map data lake objects to data model objects consistently.
- 05
Design match and reconciliation rules
Test them on samples before full runs.
- 06
Monitor
Track data stream refreshes, job failures and consumption.
Common mistakes
- Ingesting everything before agreeing use cases.
- Running identity resolution on poor-quality identifiers, which merges the wrong people.
- Confusing DLOs and DMOs; see DLO vs DMO.
- Treating Winter '27 features without a stated status as generally available.
How GeekMidst can help
Our Data Cloud consulting covers scoping, data modelling and setup. We also offer focused help with identity resolution and calculated insights and segmentation. Use our Data Cloud readiness checklist to prepare.
Questions we hear
Is Data 360 the same as Data Cloud?
Do I have to copy my warehouse data into Salesforce?
Do AI agents need Data 360?
How is Data 360 priced?
Related reading
Data Cloud vs a data warehouse
How they differ and work together
→How identity resolution match rules work
A practical explanation
→Unified customer profile blueprint
A reference architecture
→Data Cloud services FAQ
Common questions about Data 360 projects
→Sources
Planning Data 360?
Tell us about your data sources and goals, and we will help you scope a practical first phase.
