Data 360 (formerly Salesforce Data Cloud) Reviews & Product Details

What I like most about Data 360 is the ability to bring customer data from multiple sources together into a more unified view. It makes it easier to understand customer behavior, create meaningful audience segments, and activate that data across Salesforce. The integration with the broader Salesforce ecosystem is especially valuable. Review collected by and hosted on G2.com.

The biggest challenge is the learning curve and complexity of the initial setup. Connecting data sources, defining data models, and understanding how everything works together can take time and often requires technical expertise. Once configured properly, though, the platform becomes much easier to use. Review collected by and hosted on G2.com.

What I like best about Data 360 is its ability to unify customer data from multiple sources and make that data directly usable across Salesforce. It helps create a more complete customer view without requiring every source system to be redesigned, and the integration with CRM, analytics, automation, and Agentforce makes activation much easier. I especially like that data can move beyond reporting and be used directly in business processes and customer engagement. Review collected by and hosted on G2.com.

What I dislike most is the complexity of designing and operating Data 360 effectively. Data ingestion, identity resolution, data modeling, and activation all require careful architecture, and it can be difficult for teams to understand the overall design at first. Cost and consumption management can also be challenging, especially as data volumes and use cases grow. Review collected by and hosted on G2.com.

What I like best about Salesforce Data 360 is how it helps unify customer data from different sources and turn it into actionable insights across Salesforce. I especially like that it can support more personalized experiences, better segmentation, and smarter automation by making data more connected, trusted, and usable for business teams. Review collected by and hosted on G2.com.

What I dislike about Salesforce Data 360 is that it can feel complex to set up and understand at first, especially when working with data ingestion, identity resolution, permissions, and activation. The platform is very powerful, but the learning curve can be steep, and sometimes the documentation or setup steps could be clearer for new users. Review collected by and hosted on G2.com.

What I like most about Data 360 is that it addresses one of the most persistent problems I’ve encountered in Salesforce architecture: everyone wants a 360-degree view of the customer, but the data rarely lives in one place.

I’ve worked with environments where different Salesforce orgs and external systems each hold a piece of the customer journey. What I find particularly powerful about Data 360 is the ability to connect those pieces without forcing the entire enterprise to consolidate onto a single system.

As an architect, I also like that it goes beyond data consolidation. Once data is unified, I can think about how to activate it—feeding it into Sales, Service, Agentforce, Marketing, analytics, or downstream processes. That makes Data 360 feel less like another data platform and more like an intelligence layer connecting the enterprise to the Salesforce ecosystem.

That shift—from simply integrating data to making it contextual, actionable, and available at the point of decision—is what makes Data 360 particularly interesting to me. Review collected by and hosted on G2.com.

What I find most challenging about Data 360 is that the promise of a unified customer view can be much easier than achieving it in a real enterprise environment.

In the multi-org and integration architectures I’ve worked with, the complexity often sits upstream—data quality, identity resolution, conflicting customer identifiers, inconsistent data models, and real-time vs. batch requirements. Data 360 can bring the data together, but it doesn’t eliminate those underlying architecture problems; in some cases, it makes them more visible.

I also find the platform can have a steep learning curve. Understanding ingestion, data modeling, identity resolution, calculated insights, activation, governance, and consumption patterns requires a fairly broad skill set. I’d like to see more simplicity around end-to-end lineage, troubleshooting, cost/consumption visibility, and managing complex multi-org implementations.

For me, the biggest opportunity is making Data 360 easier to operate at enterprise scale without requiring architects and data teams to understand so many underlying platform concepts. Review collected by and hosted on G2.com.

What I like best about Data 360 is how it unifies customer data from sales, service, marketing, commerce, and external sources into a single, real-time profile that’s natively accessible across the Salesforce ecosystem. This gives me reliable identity resolution, zero-copy access to data warehouses (like Snowflake/Databricks), and drag-and-drop segmentation so I can activate personalized, AI-driven experiences without building complex ETL pipelines Review collected by and hosted on G2.com.

What I like best about Data 360 is how it unifies customer data from sales, service, marketing, commerce, and external sources into a single, real-time profile that’s natively accessible across the Salesforce ecosystem. This gives me reliable identity resolution, zero-copy access to data warehouses (like Snowflake/Databricks), and drag-and-drop segmentation so I can activate personalized, AI-driven experiences without building complex ETL pipelines Review collected by and hosted on G2.com.

The most helpful point is that we can see customer data from different systems in one place. Before, each team looked at a different source, so the same customer sometimes looked different depending on the system. Data 360 helps us connect those sources and use a more consistent view inside Salesforce. For our team, this is useful when we need to check history, contact information, or recent activity without opening many tools. After the basic connection started working, daily checking became easier than before.

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To be honest, the first setup is not simple. We need to think carefully about data sources, matching rules, and how the unified profile should look. If this part is not clear, the result can be confusing. There are many settings, and people who only used standard Salesforce objects need extra time. Documents exist, but some parts are still hard to follow when we try a real use case. It is not a blocker after the design is decided, but implementation and internal explanation take more effort than we first expected. Review collected by and hosted on G2.com.

Data 360 seamlessly brings together disparate data from across our entire tech stack into a single, standardized Customer 360 profile, helping break down legacy silos without requiring complex, manual ETL work. Its Zero Copy architecture allows us to access and analyze our external data warehouse, Snowflake, directly within Salesforce. As a result, we avoid data duplication, reduce storage costs, and keep our data pipelines lightweight and easier to manage. With both structured and unstructured data fully integrated inside Data 360, we get immediate contextual grounding for Agentforce and automated workflows, which supports more precise and reliable AI-driven customer interactions. Overall, it strengthens the data feeding into our Agentforce Service and Coworker processes. Review collected by and hosted on G2.com.

Configuring Data Spaces, identity resolution rules, and data model objects (DMOs) often requires significant specialized expertise. Administering and maintaining these mapping structures can also be more time-consuming than working with traditional core Salesforce objects. The consumption-based credit model may make quarterly and annual forecasting less predictable, since heavy data ingestion, frequent identity resolution runs, or complex calculated insights can burn through credits much faster than initially expected. Troubleshooting issues across data ingestion pipelines, identity resolution, or flow integrations can be difficult as well. Error messages and audit logs are sometimes opaque, so pinpointing the root cause of sync failures can become a labor-intensive process. Review collected by and hosted on G2.com.

What I like best about Data 360 is its ability to bring data from multiple sources together and create a more unified view of customers. It helps connect and harmonize data across systems, making it easier to access consistent information and use it for analytics, segmentation, and personalization. Review collected by and hosted on G2.com.

What I like best about Data 360 is its ability to bring data from multiple sources together and create a more unified view of customers. It helps connect and harmonize data across systems, making it easier to access consistent information and use it for analytics, segmentation, and personalization. Review collected by and hosted on G2.com.

Data 360 enables us to unify customer data from multiple systems and channels into a single customer profile. Its seamless integration with Salesforce products such as Service Cloud and Agentforce allows us to leverage customer insights in real time, improving personalization, operational efficiency, and customer engagement. The ability to activate unified data across different business processes is particularly valuable. Review collected by and hosted on G2.com.

While Data 360 provides powerful data unification capabilities, implementation and data modeling can be complex, especially when integrating multiple data sources. Organizations need to invest time in data governance, data quality management, and architecture design to maximize value. In addition, licensing and consumption-based pricing should be carefully evaluated to ensure a positive return on investment. Review collected by and hosted on G2.com.

I love the Data 360 interface because it resembles Salesforce, which makes it familiar to me. Review collected by and hosted on G2.com.

Deploying the data kits is a real headache; I've tried and nothing works properly. Each error forces you to delete everything and start over, which is really not normal. The documentation also needs improvement, as it doesn't clearly describe what the data cloud is. And the initial setup was horrible; having to perform actions manually because metadata never updates even when trying all possible deployment systems was frustrating. I think it's absolutely necessary to work on the documentation related to DevOps or otherwise to ensure that even manual tasks are not necessary. Review collected by and hosted on G2.com.