Data Cloud Breaks $1B ARR Milestone in Q1 FY26: What Salesforce Leaders Need to Know

Salesforce made headlines for their Q1 results for a number of reasons, the standout being Data Cloud reaching a $1B revenue milestone, which reflects a 120% growth year-over-year for one of Salesforce’s most prominent services. With many in the ecosystem still having issues with Data Cloud, what’s really driving growth on the ground beyond the marketing buzz?

To discuss this further, I sat down with Francois Zimmerman, Field CTO for Data Cloud EMEA (Europe, the Middle East, and Africa) at Salesforce, to fully understand why Data Cloud is enjoying success in these regions, what businesses are getting right (and wrong) with their implementations, and how it ties directly to Salesforce’s flagship product, Agentforce.

The Long-Term Need for Unified Data

When you look past the current AI hype and product announcements, the surge in Data Cloud adoption can be traced back to a more foundational issue, which is the growing complexity of enterprise data.

As organizations invest in more SaaS applications across marketing, service, commerce, and sales, they often create fragmented data silos that are difficult to reconcile. Data Cloud is helping companies solve that problem by building a more unified, connected view of their customers and processes.

“One of the big problems of getting a SaaS application to work is first getting it to work with the enterprise’s data at scale,” Zimmerman explained. “If you buy lots of SaaS products across different channels, you tend to get fragmentation. One of the big problems of getting a SaaS application to work is first getting it to work with the enterprise’s data at scale, and if you buy lots of SaaS products across different channels, you tend to get fragmentation.”

It’s the fragmentation, as well as the need for cross-functional coordination, that is really driving adoption across EMEA at the moment. “People are trying to deploy agents across the front office,” Zimmermann noted, referencing the early adoption of Agentforce. “But to orchestrate workflow across front, middle, and back office, you need a unified view of your customer. That’s where Data Cloud comes in.”

This need for orchestration is especially pronounced in EMEA, where businesses often operate across multiple regions, languages, and systems. Data Cloud’s ability to pull together fragmented data into a single, actionable source of truth is proving to be a key differentiator, especially for companies looking to deploy AI agents across different departments.

Why Some Companies Struggle

There’s no questioning Data Cloud’s current revenue successes, but failed Data Cloud implementations are still an ongoing issue in the ecosystem.

Timo Kovala recently covered this topic for Salesforce Ben, speaking to global Salesforce industry leaders about the key sticking points when Data Cloud implementations fail, which included factors such as over-engineering segmentation logic, uncontrolled credit consumption, evolving release cadence, and planning missteps.

For many, Data Cloud is still a “gray area”, as Jack Searle from Capgemini mentions.

“It’s evolving so fast that while other SF products have a quarterly release, Data Cloud’s release is every few weeks,” Jack explained to Salesforce Ben. “There are constant changes and updates happening that make it difficult to keep up.”

So what separates successful Data Cloud implementations from ones that stall out? For Francois, having the right mindset and structure is the best approach to take to ensure successful implementation.

“The people who do it well are taking an incremental approach,” Francois explained. “They start with a persona in the front office – sales, service, marketing – and they ask four key questions: What do you need to know about your customer? What do you want to calculate? What do you want to automate? And what tasks could you share with an agent?”

He then contrasted this by explaining that some companies still view Data Cloud as a purely technical proof-of-concept, ultimately holding them back from a smooth implementation process.

“They’ll put it on an innovation budget and focus only on data ingestion… but the tech is proven. The real value comes when you tie data acquisition to business outcomes.”

He emphasized that success doesn’t come from trying to build everything at once. Instead, the best results happen when businesses think in six-week cycles, starting with specific use cases and gradually expanding. This helps teams avoid getting bogged down in a massive data project and instead keeps the focus on tangible business results.

Although this is, of course, great constructive advice for customers, effectively positioning Data Cloud, managing its consumption-based pricing model, and the requirement for meticulous planning and targeted use-case design underscore substantial challenges that still need mitigating for many.

These complexities prompt future questions about Data Cloud’s as the foundational technology powering Salesforce’s AI agents.

The Importance of an AI Connection

On the topic of agents, much of the excitement around Data Cloud is its role as a foundation for AI and agent-based experiences – but Zimmermann emphasized to me that AI isn’t magic. “The key thing is context,” he said. “The biggest barriers to external AI use are accuracy and trust. People are worried about giving the wrong answer or leaking data.”

Agentforce mitigates some of that through what Zimmermann calls “grounding checks,” ensuring the model only responds using approved, business-specific data. But readiness also goes deeper than this. “It starts by linking data to the customer, the account, the opportunity – that makes it AI-ready.”

This structure-first approach helps reduce risk while accelerating important outcomes. By tying data to the specific business context in which an agent operates, companies ensure that their AI tools are both relevant and reliable. This is especially critical for customer-facing use cases, where trust and data sensitivity are top of mind.

Structured, Unstructured, and Everything in Between

Francois was especially enthusiastic about how Salesforce is evolving their strategy around how they understand unstructured data, saying: “People used to just dump everything into a big bucket and hope for the best. Now, we’re using techniques like rag-and-rich chunking and embedding to improve knowledge retrieval.”

He also pointed to Salesforce’s retriever framework within Data Cloud as a differentiator. “You don’t want to rewrite all your upstream systems. Our approach gives you enough control to filter knowledge – say, by product category – before running a search. It improves accuracy without reengineering everything.”

This hybrid view that combines structured data like customer records with unstructured sources like knowledge articles allows companies to train smarter, more context-aware agents. It’s also a more flexible approach for global organizations managing huge volumes of documentation and variation across business units.

If you’re looking to learn how to ground search with unstructured data in Data Cloud, have a go at the module on Trailhead.

What the Informatica Deal Signals

Salesforce’s recent $8B acquisition of Informatica adds another layer of intrigue. While Francois couldn’t comment on specific product plans, he pointed to clear areas of synergy. “Data quality, trusted pipelines – those are strengths Informatica brings, and that’s where I expect to see impact.”

He acknowledged that it’s too early to speak about detailed roadmaps, but emphasized that the logic behind the acquisition aligns with Salesforce’s broader goals, which is making AI agent deployment powerful, trustworthy, and governable. In regulated markets like EMEA, this is very important.

But even so, a lot of eyebrows have been raised in the ecosystem following this acquisition. While it could potentially enhance data integration capabilities and ultimately extend their reach outside the Salesforce ecosystem, many are curious to see how this will blend in next to MuleSoft.

As the two tools bring very similar offerings – that is, helping businesses connect to different apps and data – could this bring users overlaps and headaches? It will be interesting to see how the two products coexist going forward.

Why Salesforce Pros Should Care

To wrap up our conversation, I asked Francois a final question: Why should Salesforce professionals who haven’t yet touched Data Cloud start paying attention in 2025?

“It’s becoming a batteries-included capability,” he said. “Every time you use an agent, Data Cloud is involved. Industry Clouds are also using it more natively. And most importantly, it helps reduce tech debt.”

He cited a striking internal example, saying: “We went from two million lines of code to 200,000 by moving harmonization into Data Cloud. That’s the way customers want to run Salesforce – leaner, more agile, and easier to manage.”

Technical debt is a massive issue across the Salesforce ecosystem, with many still trying to understand how to successfully tackle it. Fortunately, Data Cloud should offer a clear pathway forward. By centralizing data sources, standardizing data models, and replacing complex custom integrations with more manageable, declarative solutions, Data Cloud helps teams clean up their Salesforce environments and avoid common pitfalls.

This is especially important if you’re looking to also successfully implement Agentforce in the near future. Agents need clean and clear data, and you will likely need to utilize Data Cloud to make sure your agent experience runs smoothly.

In essence, Francois’ message is clear: if you’re still ignoring Data Cloud, you may already be behind. Whether you’re an admin, architect, or consultant, understanding how to make use of it is quickly becoming a core part of working in the Salesforce ecosystem.

Final Thoughts

Data Cloud is growing to become extremely important for Salesforce customers and is rapidly becoming the foundation for how businesses want to use Salesforce, as well as a key to unlocking real value from AI.

For professionals working in the ecosystem, understanding how to leverage it is quickly moving from nice-to-have to more of a non-negotiable.

But implementation issues still exist, regardless of the impressive growth figures. Many users still find the platform very complex, and this could impact the platform’s long-term growth if they can’t meet user expectations.