Data 360

What do you like best about the product?

What I like best about Salesforce Data 360 is that it helps bring customer data from different systems into a more unified and actionable view inside the Salesforce ecosystem. Instead of having customer information spread across CRM records, marketing tools, service interactions, commerce data, and external sources, Data 360 makes it easier to connect those signals and use them in a more structured way.

I especially like the idea of creating a more complete customer profile that can be used across sales, service, marketing, and analytics. This is valuable because many organizations already have the data they need, but it is fragmented across different platforms and teams.

Another strong point is that Data 360 is designed to make customer data more usable for segmentation, personalization, reporting, and automation. When implemented well, it can help teams move from isolated data points to more context-aware customer engagement.

What do you dislike about the product?

What I dislike about Salesforce Data 360 is that it can be complex to understand, implement, and maintain if the organization does not already have a clear data strategy. The platform is powerful, but getting real value from it requires clean source data, well-defined identity resolution rules, thoughtful data mapping, and alignment between business and technical teams.

Another challenge is that the terminology and architecture can feel difficult at first, especially for teams that are used to working only with standard Salesforce CRM objects. Concepts like data streams, data model objects, identity resolution, calculated insights, and activation require time to learn and configure correctly.

I also think the success of Data 360 depends heavily on governance. If the connected data sources are inconsistent, duplicated, or poorly documented, the platform can expose those issues rather than solve them automatically. It is not a quick plug-and-play solution; it needs careful planning, ownership, and ongoing data quality management.

What problems is the product solving and how is that benefiting you?

Salesforce Data 360 helps solve the problem of fragmented customer data across different systems and teams. Many organizations have useful customer information in CRM, marketing platforms, service tools, commerce systems, analytics tools, and external databases, but that information is often disconnected. Data 360 helps bring those signals together so teams can work from a more complete customer view.

The main benefit is that customer data becomes more actionable. Instead of looking at isolated records or manually combining information from different tools, teams can use unified profiles, segments, calculated insights, and activations to support better sales, service, marketing, and reporting decisions.

It also helps improve personalization and prioritization. For example, teams can better understand customer behavior, engagement history, product usage, service interactions, and potential opportunities or risks. That makes it easier to target the right customers, personalize communication, and make decisions based on broader context.

Overall, Salesforce Data 360 benefits me by reducing data silos, improving visibility, and making customer data more useful for business processes, analytics, and customer engagement.

What do you like best about the product?

What I like best about Salesforce Data 360 (formerly Data Cloud) is its ability to unify customer data from multiple systems into a single real-time customer profile. It helps organizations make smarter decisions by connecting sales, service, marketing, and analytics data in one place.

I also appreciate the real-time insights, identity resolution, and seamless integration with the Salesforce ecosystem, which enables personalized customer experiences and better automation across teams. The platform’s scalability and AI-driven capabilities make it very powerful for modern customer engagement and data-driven strategies.

What do you dislike about the product?

One challenge with Salesforce Data 360 (formerly Data Cloud) is that the initial setup and implementation can be complex, especially when integrating multiple data sources and defining identity resolution rules. It also has a learning curve for new users who are not familiar with data modeling and segmentation concepts.

Additionally, licensing and storage costs can become expensive for organizations managing large volumes of data, and some advanced features require significant technical expertise to fully utilize.

What problems is the product solving and how is that benefiting you?

Salesforce Data 360 (formerly Data Cloud) helps solve the problem of fragmented customer data that exists across multiple systems and platforms. By bringing data together into a unified customer profile, it provides a complete and real-time view of customers, which improves decision-making and customer engagement.

This benefits me by enabling better personalization, faster access to insights, improved reporting accuracy, and more efficient collaboration between sales, service, and marketing teams. It also helps streamline data management processes and supports scalable, data-driven business strategies.

What do you like best about the product?

real time data from other sources. one place to view instead of having to go login to multiple systems. saves time and money

What do you dislike about the product?

Setup took some time. also required tweaking the different parts to get everything we wanted. Also expensive so we have limited licenses. more users at my company can use this but not in budget.

What problems is the product solving and how is that benefiting you?

Bringing in all customer data into one system.

What do you like best about the product?

What I like most about Data Cloud is its ability to unify customer data from multiple sources into a single, reliable profile. In practice, this makes a big difference when building audience segments, since I no longer have to deal with fragmented or inconsistent data.

The pre-built connectors are another highlight. They significantly reduce the effort required to integrate new data sources — what used to take days can now be done much faster, which helps me focus more on campaign strategy instead of technical setup.

Real-time data is also a game changer. It allows us to react almost instantly to customer behavior, enabling more relevant and timely communications, especially in automated journeys.

What do you dislike about the product?

Overall, I find Data Cloud to be a very complete platform that meets most of my needs. However, the initial implementation can be quite complex, especially when dealing with data modeling and identity resolution across multiple sources.

This complexity can slow down the onboarding process and require more technical involvement than expected at the beginning. Over time, it becomes easier to manage, but the learning curve is definitely something to consider.

Improving the onboarding experience with more guided setups, clearer documentation, or predefined templates for common use cases would help reduce this initial barrier and accelerate time to value.

What problems is the product solving and how is that benefiting you?

Before using Data Cloud, we struggled with fragmented customer data spread across multiple systems, which led to duplication, inconsistencies, and a lack of a clear customer view. This made it difficult to build reliable audience segments and impacted the effectiveness of our campaigns.

With Data Cloud, we were able to centralize and unify this data into a single, more reliable customer profile. Identity resolution was a key factor, allowing us to connect interactions across multiple channels and significantly improve data consistency.

As a result, we gained much more agility in building segments and activating campaigns. What previously required heavy support from technical teams can now be done more independently by marketing, reducing turnaround time.

Overall, this has improved our decision-making confidence and led to better engagement and conversion rates.

What do you like best about the product?

Data 360 offers no‑code segment builders, streaming insights, and Einstein‑powered capabilities—such as Einstein Segment Creation and vector search—that enable business users and analysts to work with data effectively, without relying heavily on data‑engineering support.

What do you dislike about the product?

Pricing is high.Also configuration is bit complex.

What problems is the product solving and how is that benefiting you?

Data cloud mainly solving the problem of fragmented, “siloed” customer data by creating a single, real‑time Customer 360 that can be used across Sales, Service, Marketing, and AI workflows—without you having to build and maintain complex custom pipelines.