Is Data Cloud the same as Data 360?

Same platform, newer name. If a proposal treats them as two products, ask why.

Do we need it before Agentforce?

You need grounded, permissioned context. Sometimes that is Data Cloud. Sometimes it is clean Knowledge and a sharing model that already works. We check the org before quoting.

Does it replace the warehouse?

No. It resolves and serves the profile. The warehouse stays the analytical store, and we front it rather than move it.

Can it be the only memory layer?

For profile and similarity, often yes. For multi-hop relationships and decision history, put a graph next to it.

Salesforce MVP Hall of Fame · Certified Partner since 2010

The profile the agent grounds on.

The first week is in the org, reading what already exists. Data Cloud resolves the customer across the systems that each think they own them, then serves that profile to journeys, analytics, and agents. It is context, not agency. The honest no: if the work is a software factory with a named delivery date, we are the wrong partner.

Send the brief

In the org

Four pieces that matter to an agent.

Data Cloud is also sold as Data 360. Same platform, same four pieces we care about when an agent has to be right.

01
Data model objects
Canonical shapes for customer, account, case, product
02
Identity resolution
One profile · Match rules · Survivorship
03
Data spaces
Tenant isolation · Team scoping · Row-level governance
04
Hybrid retrieval
Structured fields · Vector search on unstructured text
05
Consumers
Agentforce · Marketing journeys · Analytics

Data Cloud for agents. Sources land in data model objects, identity resolution produces the profile, data spaces keep tenants and teams apart, and retrieval serves both structured fields and semantic search.

Mermaid source

flowchart TD
  A["Sources: CRM, ERP, web, documents"] --> B["Data model objects: canonical shapes"]
  B --> C["Identity resolution: one profile"]
  C --> D["Data spaces: tenant and team isolation"]
  D --> E["Retrieval: fields plus vector search"]
  E --> F["Agentforce, journeys, analytics"]

Why it matters

An agent without a profile guesses.

These are the four failures we find when an agent pilot is “almost right”.

01Three versions of one customerThe agent answers about whichever record it found first. Identity resolution is the fix, not a better prompt.

02Context without permissionGrounding that ignores the sharing model. Data spaces and row-level rules have to survive retrieval.

03Unstructured text strandedDocuments sit outside the profile, so the agent cites nothing. Vector search next to the record closes that gap.

04Streaming assumptionsIngestion and usage limits are real. We design for them instead of discovering them in production.

In an org

A bank, and one customer with four records.

01Bring the sourcesCore banking, the CRM, the servicing portal, and the complaints inbox land as data model objects rather than four bespoke syncs.

02Resolve the personOne profile, with the match rules written down and reviewable. The branch and the agent finally see the same customer.

03Keep the wallsData spaces separate business units, so an agent for one desk cannot browse another.

04Serve the agentAgentforce grounds on the profile. A code agent reaches the same context through the gateway, not a direct database credential.

How Mindcat helps

Context first. Then the agent.

We will tell you when clean Knowledge and a working sharing model are enough, and Data Cloud is not the first cheque to write.

Questions

What we actually say.

Is Data Cloud the same as Data 360?: Same platform, newer name. If a proposal treats them as two products, ask why.
Do we need it before Agentforce?: You need grounded, permissioned context. Sometimes that is Data Cloud. Sometimes it is clean Knowledge and a sharing model that already works. We check the org before quoting.
Does it replace the warehouse?: No. It resolves and serves the profile. The warehouse stays the analytical store, and we front it rather than move it.
Can it be the only memory layer?: For profile and similarity, often yes. For multi-hop relationships and decision history, put a graph next to it.

What to read next.

The brief

Name the systems that disagree about a customer.

We will tell you whether Data Cloud is the fix or a bigger bill.

The profile the agent grounds on.

flowchart TD A["Sources: CRM, ERP, web, documents"] --> B["Data model objects: canonical shapes"] B --> C["Identity resolution: one profile"] C --> D["Data spaces: tenant and team isolation"] D --> E["Retrieval: fields plus vector search"] E --> F["Agentforce, journeys, analytics"]