If your weekly pipeline meeting still ends with “wait… whose number is right?” you don’t have a forecasting problem—you have a data decisioning problem. That’s exactly where Salesforce Data 360 for revenue operations comes in: it helps unify customer data, reduce manual reconciliation, and give Sales, Marketing, and CS one shared view they can actually run on.

Salesforce Data 360 (formerly Data Cloud) is Salesforce’s native data platform designed to unify customer data, make it usable in near real time, and activate it across teams so decisions are made from one version of the truth, not five spreadsheets and a Slack debate.

For Revenue Operations teams, the goal isn’t “get a CDP.” The goal is:

• fewer blind spots and less manual reconciliation
• faster handoffs across Sales/Marketing/CS
• cleaner attribution and lifecycle reporting
• a foundation for AgentForce and automation that won’t break under pressure

What Data 360 actually does (in RevOps language)

Think of Data 360 as your unification + activation layer inside Salesforce:

1. Ingest data from multiple sources (CRM + marketing + product + support, etc.)
2. Harmonize it into consistent models
3. Unify identities into a profile you can trust
4. Activate it into segments, automations, and experiences

Salesforce’s own Data 360 overview frames it simply: unify customer data and use real-time insights to personalize experiences.

Why RevOps should care

Because most RevOps pain is data pain wearing different hats:

• Duplicate Accounts splitting pipeline
• Contacts that don’t map cleanly to buying committees
• Leads with missing enrichment and broken routing
• Attribution that collapses the minute someone changes a campaign naming convention

Data 360 doesn’t magically fix governance—but it gives you the platform to fix it and then actually use the result everywhere.

“Turn data into decisions” = decisions you can take in Salesforce

RevOps is accountable for the action, not just the data. The most useful Data 360 outcomes look like this:

1) Better prioritization (Sales + SDR)

• “Which accounts look most likely to buy based on behavior + fit?”
• “Which opportunities are at risk based on engagement + stage history?”

2) Cleaner handoffs (Marketing → Sales → CS)

• SLAs based on signals (not guesswork)
• Consistent lifecycle rules, tracked and reportable

3) Smarter segmentation (without manual exports)

Salesforce’s marketing CDP page explicitly calls out real-time segmentation and activation using unified data—this matters when you’re trying to operationalize targeting across channels.

Data 360 in the real world: the RevOps use cases that matter

Here are a few high-impact patterns we see work in B2B revenue orgs:

Account scoring that doesn’t live in a spreadsheet

Instead of “the spreadsheet score,” you build a unified view that powers routing, sequences, and prioritization.

Lifecycle reporting you don’t have to defend every month

When identity + definitions are consistent, conversion rates stop being a philosophical debate.

Real-time signals that actually change behavior

Salesforce highlights “sub-second real-time” profile updates for use cases like real-time profile sharing—RevOps can translate that into “signal-driven routing and next best actions.”

Where teams go sideways (and how RevOps prevents it)

Mistake #1: “We connected data, so we’re done.”

Nope. If definitions aren’t aligned—ICP, lifecycle stages, source of truth fields—Data 360 just makes your inconsistency faster.

Mistake #2: “We tried to unify everything at once.”

The fastest implementations start with one business outcome (pipeline efficiency, churn risk, expansion targeting) and prove value in 30–60 days.

Mistake #3: “No one owns governance.”

RevOps has to be at the table with Marketing Ops, IT, and Security. If ownership is unclear, your unified profile becomes “the new battleground.”

If you want a practical approach, we laid out a straight-shooting guide and a 30-day plan here: The World’s Most Valuable Fuel in Salesforce Isn’t Oil — It’s Data (A Practical Guide to Data 360).

Data 360 + AgentForce: why this pairing is showing up everywhere

Agentic AI is only as useful as the data it can trust. When Data 360 unifies identity and context across systems, it becomes the substrate for better automation and more reliable agent behavior.

Trailhead even includes a directly relevant project: Connect Data 360 to Agentforce—a signal that Salesforce expects these to work together in real implementations.

How to get started (without boiling the ocean)

Here’s a RevOps-friendly rollout sequence:

1. Pick one outcome (e.g., improve speed-to-lead, identify expansion candidates, reduce pipeline slippage)
2. Choose the minimum data sources needed to support that outcome
3. Define identity rules (what makes a person/account “the same”)
4. Operationalize: push the insights into routing, dashboards, sequences, and plays
5. Measure: time-to-contact, conversion rates, stage velocity, forecast accuracy

If your team wants a step-by-step structure (frameworks, templates, governance), we built it: Why Every Business Needs a Data 360 Playbook — And Where to Get Yours.

And if you want help implementing Data 360 with RevOps outcomes in mind (not just “standing up the platform”), you can start with our main site and services.

Learn it (and train your team) with official Salesforce resources

If you’re building internal capability, these are the best “source of truth” starting points:

• Salesforce Data 360 overview
• Trailhead: Unlock Your Data with Data Cloud trail (setup → segmentation → activation)
• Trailhead Academy: Learn Data Cloud training options

The bottom line

Salesforce Data 360 is most valuable when it stops being a “data initiative” and becomes a decision engine: one trusted customer view, activated into the workflows your revenue teams actually use.

If you want the shortest path to value, start with one measurable RevOps outcome, unify only the data required, and operationalize it into routing, prioritization, and lifecycle reporting—then expand.