Data 360 (formerly Data Cloud)

Data 360 (which Salesforce previously called Data Cloud) is the native data platform of Salesforce that ingests, unifies and activates customer information coming from many sources (CRM, e-commerce, web, apps, external systems) to build a unified, queryable profile of each person or account. It is not a CRM or a dashboard: it is the data layer that sits underneath and fuels everything else, from marketing segmentation to AI agents. It is the foundation on which consultancies like Vantegrate build data projects in the Salesforce ecosystem.

Its technical differentiator is that it works with zero-copy and near real-time processing: instead of duplicating tables into its own repository, it connects to your sources (including data warehouses such as Snowflake or BigQuery) and works on top of them through a common data model. That is how it resolves identity (it reconciles that "Juan Pérez" from checkout and "jperez@..." from the form are the same person) and delivers a single source of truth ready to activate.

In practice, Data 360 is the engine that makes a real Customer 360 possible and, increasingly, the grounding in first-party data that Salesforce's generative AI needs to answer with business context instead of hallucinating.

What Data 360 is for

The problem it solves is old and expensive: customer data lives scattered across silos. A typical company has orders in the ERP, tickets in support, browsing behavior on the website, campaigns in marketing and opportunities in the CRM, and none of them talk to each other. Data 360 unifies all of that into a single profile, keeps it fresh in near real time and makes it available for segmenting, analyzing, automating and, above all, feeding AI with reliable context. It is, in essence, the cross-cutting data layer of the Salesforce ecosystem.

The conceptual flow has four clear stages:

• Ingestion: sources are connected through native connectors, APIs, streaming or zero-copy to an external data warehouse, without necessarily moving the data.
• Harmonization: everything is mapped to a common data model so that fields from different systems mean the same thing.
• Identity resolution: duplicate records are reconciled and the unified profile of each customer is built.
• Activation: that enriched profile is used for segments, automations, analytics and AI grounding inside and outside Salesforce.

Why the name changed to Data 360

In 2025, Salesforce announced the evolution of the brand to Data 360, aligning it with the platform's "360" family (the idea of a 360-degree view of the customer). The product and its capabilities are the same: the commercial name changed, not the concept. In documentation and projects you will find both terms side by side, and it is worth recognizing them as synonyms.

Data 360 is not a data warehouse (the most common confusion)

This is the most frequent conceptual mistake. A data warehouse (such as Snowflake, BigQuery or Redshift) is an analytical repository where large volumes of historical data are stored for reporting. Data 360 can connect to one and even read it without copying it, but its focus is different: it is oriented toward an actionable, near real-time customer profile, not mass storage for pure BI.

The healthy way to see it: the data warehouse is usually the source, and Data 360 is the layer that makes it actionable around the customer. They do not compete; they complement each other.

A concrete Latin American example

Think of a consumer goods company in Argentina that sells through modern trade, distributors and its own e-commerce store. Without Data 360, the marketing team does not know that the e-commerce buyer is the same purchasing manager at a supermarket, and sends them generic promotions. With Data 360, data from the e-commerce store, the CRM (on which Sellium runs) and the order system are unified into a single profile; segmentation stops being a static list and starts updating itself when behavior changes. When that profile feeds an AI agent, the agent answers "your last order was X, it arrives Tuesday" with real business data, not made-up data.

Common implementation mistakes

• Treating it as a pure IT project: without a clear map of business use cases, it ends up as an expensive, unused data lake.
• Neglecting source data quality: Data 360 unifies, it does not magically clean; garbage in is garbage unified.
• Underestimating identity resolution: poorly defined matching rules create duplicate profiles or, worse, merge different people.
• Forgetting governance: consent, privacy and permissions must be defined from day one, not afterward.

Its growing role in AI

The use case driving its adoption the most is AI. Salesforce's agents and generative AI need up-to-date first-party data to give useful answers; Data 360 is precisely the layer that provides that reliable grounding. Without a unified, fresh data foundation, enterprise AI gives generic answers or hallucinates. That is why Data 360 stopped being "just another piece" and became the foundation on which much of the data and AI strategy in the ecosystem rests.