Salesforce Spring ’26 introduced a wide range of updates, and some are already starting to shape how teams work with data day to day.
Data 360 sits at the center of that shift. As Salesforce continues pushing toward more connected, AI-driven operations, the way your data is structured, accessed, and activated is becoming harder to ignore.
After Agentforce, this is the next area to focus on. Formerly known as Data Cloud, Data 360 is more than a rebrand. It reflects a broader shift in how Salesforce approaches enterprise data, treating it as the foundation for AI, automation, and operations rather than something separate from them.
If you’re working toward a more agentic model, where AI agents and human teams rely on the same trusted information, start with these three Data 360 updates.
1. Data Cloud Is Now Data 360—and It’s Built to Connect More of Your Business
Salesforce has officially rebranded Data Cloud to Data 360, but the bigger story is what the platform is becoming.
One of the most practical updates is the direct Microsoft Power BI connector. For organizations that already rely on Power BI for reporting and visualization, this makes it much easier to connect operational Salesforce data to the dashboards business teams actually use. That means fewer workarounds, fewer manual exports, and a smoother path from CRM data to decision-making.
Salesforce is also improving data connectivity across home orgs and companion orgs through the Agentforce Data Library. That matters because one of the biggest obstacles to useful AI and reliable reporting is fragmented data living across disconnected environments.
In plain terms, Data 360 is becoming a more connected layer across systems, teams, and tools. And that is what makes it more usable in the real world.
2. Faster Ingestion and Custom Python Give Teams More Flexibility
Another important shift in Spring ’26 is how much easier it is to get data ready for use.
Salesforce now offers ready-to-use Data Lake Objects that come pre-mapped to standard CRM fields. That may not be the flashiest update in the release, but it solves a very real problem. Less manual mapping means less setup, less project delay, and less time spent doing foundational work before teams can actually start building.
At the same time, Code Extension (Beta) gives developers the ability to run custom Python logic directly within Data 360. That opens the door to more advanced transformation, enrichment, matching, and modeling use cases using familiar libraries.
The impact is pretty straightforward. Teams can move faster on the basics, while also having more flexibility when standard tools are not enough. That combination makes Data 360 feel much more practical for businesses that need both speed and sophistication.
3. Clean Rooms and Notebook AI Make Data More Useful—and More Trustworthy
Just like Agentforce is becoming more measurable and accountable, Data 360 is becoming more usable in environments where trust and governance matter.
Data 360 Clean Rooms are now generally available, giving organizations a way to collaborate with external partners on shared data without exposing sensitive underlying information. For companies balancing personalization, partnerships, and privacy requirements, that is a meaningful step forward.
Notebook AI is also now generally available. It creates a workspace where teams can pull together files, links, and enterprise data sources, then explore that information more naturally in one place. Instead of chasing context across scattered systems, teams can spend more time actually learning from the information they already have.
Together, these capabilities help solve two persistent data challenges: how to collaborate safely, and how to make enterprise knowledge easier to access and use. That makes Data 360 more than a backend improvement. It makes it a stronger foundation for everything built on top of it.
Where Palladin Comes In
Understanding these updates is one thing. Turning them into something that actually works for your business is another.
That’s where Palladin comes in. Whether you’re connecting your data ecosystem, identifying the right use cases for Data 360, implementing Clean Rooms, or building the data foundation your AI strategy depends on, the focus is always on making the technology practical.
It’s not about adding more tools for the sake of it. It’s about helping your team create a cleaner, more connected environment where analytics, automation, and AI can actually deliver results.
What This Means for Your Data Strategy
If Agentforce is the part of the Spring ’26 release that gets the most attention, Data 360 is the part that makes it sustainable.
Because better agents, better automation, and better customer experiences all depend on the same thing: trusted, connected, usable data.
If you’re just getting to this now, focus on what matters most. Make it easier to connect your data across systems. Reduce the friction involved in preparing it. And build a stronger foundation for AI, analytics, and collaboration moving forward.
The teams that come out ahead this year will not just be the ones experimenting with AI. They will be the ones pairing intelligent automation with the data foundation required to make it work.
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