Immediate Action Drill: Preparing Your Salesforce Data for the Future

Platforms like Salesforce Data 360 (formerly Data Cloud) and Agentforce are redefining what’s possible for organizations. But they share a common dependency: clean, well-structured, actionable data. Without that foundation, the most powerful tools in the ecosystem deliver underwhelming results.

The good news is that preparing doesn’t require a massive overhaul. It requires intentional action, taken now. Here’s a practical drill to get started.

1. Audit and Standardize Your Data

Start with the fields that matter most: names, emails, addresses, phone numbers. Are they populated? Are they consistent across records?

Common issues to look for:

• Duplicate records from multiple entry points
• Inconsistent formatting (different date formats, phone number patterns)
• Stale or inactive records inflating your counts

Tools like DemandTools can accelerate deduplication, but you need a clear picture of the problem before you can solve it. Also review your metadata (record types, industry codes, picklist values), since these feed the segmentation and AI logic that makes Data 360 useful.

2. Map Your External Data Sources

Data 360’s power comes from unifying data across systems, but you can’t unify what you haven’t inventoried. Map out the external systems that hold customer data: your ERP, marketing platform, loyalty program, service desk, whatever is relevant to your business. To see why that inventory matters, follow one customer across four systems as they map onto one data model.

For each system, evaluate:

• Does it contain data that would enrich the Salesforce picture?
• Is integration feasible (API, connector, export)?
• What’s the data quality like on that side?

Pick one system to model in detail. Going deep on one integration teaches you patterns that transfer to everything else.

3. Simplify Your Org

Technical debt compounds when you layer AI onto it. Before you do, reduce the surface area.

Practical steps:

• Deactivate fields that are no longer used (they still appear in exports and can confuse data mapping)
• Audit your automation (Workflows, Process Builders, Flows) and consolidate where possible
• Review user permissions and remove what’s unnecessary
• Streamline page layouts so the data entry experience supports data quality

A simpler org is easier to integrate, easier to maintain, and easier to extend with new capabilities.

4. Get Your Team On Board

Technical preparation is only half the work. Your team needs to understand why the changes are happening and what they’re for. When people understand the purpose behind a data quality initiative, they’re more likely to maintain the standards you’re building.

Practical steps:

• Explain the “why” in terms of business outcomes, not platform features
• Designate internal champions who can answer questions and keep things moving
• Use Trailhead for self-paced learning on Data 360 and Agentforce concepts
• Where possible, show the team demos using your own data, it lands differently than generic examples

The Point

Preparing for the future doesn’t require a massive overhaul. It requires honest assessment and focused action on the things that will actually matter when you’re ready to take the next step.

Start now. The foundation you build today is what AI will amplify tomorrow.

When you’re ready for the next step, planning for identity resolution in Data 360 picks up where this clean foundation leaves off. And once the data is in shape, here’s how I decide which AI use cases are worth building.