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

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

I use Salesforce Data 360 to bring in CRM Analytics datasets as DLO, which power the semantic model for Analytics Agent. It enables us to have an intelligent experience in data analytics, allowing users to ask questions directly to the agent and get insights rather than just viewing charts and tables. I really like the power of unification and reconciliation that Salesforce Data 360 offers. Additionally, transforming data segmentation and activation to Marketing Cloud works especially well. The initial setup was extremely easy. Review collected by and hosted on G2.com.

I find it challenging when using Salesforce Data 360 that it doesn't suggest which DMO to use for a particular DLO out of the box. Sometimes it gets difficult to map my DLO to an existing DMO, and having an option to get suggestions for the right DMO would be really helpful. Review collected by and hosted on G2.com.

I really like the segmentation feature in Salesforce Data 360. Once the Data Model Objects (DMOs) are mapped cleanly, building and refreshing our six customer segments is fast and reliable. The identity resolution feature is another highlight as it helped us create a unified customer view from scattered CRM data, which was our main goal. I appreciate the Calculated Insights too, even with its quirks. It allows me to derive insights like preferred product categories directly in the platform, saving me from a lot of back-and-forth. The ability for segments like VIPs, sleeping customers, and category-specific buyers to stay live without manual list-pulling for every campaign saves significant time and improves targeting. We can act on actual purchase behavior rather than guesswork, making re-engagement easier. Review collected by and hosted on G2.com.

The calculated insights SQL engine has some limitations - no subqueries, window functions, or DATEDIFF, and the workarounds like chaining three CIs together aren't documented anywhere. The initial setup isn't easy; there's a real learning curve, especially with mapping DLOs to DMOs and configuring identity resolution rules. It took a fair bit of trial and error, and these SQL engine restrictions are only discovered by hitting errors. This phase requires patience and hands-on debugging. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

I like that the initial setup of Data 360 is simple, allowing you to get started easily and then keep upgrading as needed. Review collected by and hosted on G2.com.

I find that managing Data 360 can get quite overwhelming when segmentation becomes too detailed. It becomes difficult to debug, especially when the numbers don't match up with the data. The UI isn't very intuitive, which makes navigation and troubleshooting more challenging. I think an easier, more intuitive interface would help a lot with managing these issues. Review collected by and hosted on G2.com.

What do you like best about Data 360 (formerly Salesforce Data Cloud)?* Review collected by and hosted on G2.com.

The biggest challenge is the complexity of implementation and troubleshooting. Data 360 is powerful, but understanding how data is unified, transformed, and connected can require significant technical knowledge. Better transparency, simpler configuration, and easier troubleshooting would make it much more accessible to admins. Review collected by and hosted on G2.com.

What I like most about Data 360 is its ability to bring data from different sources together into a unified customer view. It makes it easier to access, organize, and use customer data across Salesforce, while supporting real-time insights and more personalized business interactions. Review collected by and hosted on G2.com.

What I dislike about Data 360 is that it can take some time to understand and configure, especially when working with complex data models, integrations, and data mappings. Review collected by and hosted on G2.com.

What I like best about Data 360 is its ability to bring together and make enterprise information more accessible and useful for AI-driven experiences. We are currently using it with Salesforce Knowledge articles and exploring web search capabilities through a proof of concept (POC). This helps us evaluate how trusted internal knowledge and relevant web content can be unified to provide more accurate, contextual, and useful responses to users. Review collected by and hosted on G2.com.

One challenge with Data 360 is that the initial configuration and integration can be complex, especially when connecting and indexing multiple data sources. Understanding data ingestion, search configuration, permissions, and consumption-based usage also has a learning curve, and troubleshooting issues can sometimes require significant technical expertise and support. Review collected by and hosted on G2.com.

What I like most about Data 360 is how it brings data from multiple sources together to create a more unified view of customers and business information within Salesforce. I particularly appreciate that it can connect data across different systems, make that data easier to access for analytics and AI, and add stronger context for tools like Agentforce. Overall, this helps reduce data silos, improves the quality of insights, and enables teams to make more informed decisions based on a more complete view of their data. Review collected by and hosted on G2.com.

The biggest challenge with Data 360 is the complexity involved in setting it up and managing data across multiple sources. Data mapping, identity resolution, data quality, and governance can require significant planning and technical expertise. I would also like to see more intuitive administration and troubleshooting tools, especially for quickly identifying data issues and understanding how data flows through the platform. The overall learning curve and implementation effort can be higher than expected for teams that are new to the platform. Review collected by and hosted on G2.com.

The biggest strengths of Data 360 (formerly Salesforce Data Cloud) are unifying our data and real-time activation. It has a strong, tight integration with Salesforce and AI, and it can be activated across sales, service, marketing, and commerce. It also helps with identity resolution by creating a unified customer profile from disparate data sources, giving us a single source of truth so we can communicate with people where they are using data from our sales, service center, and marketing communications in one place. Review collected by and hosted on G2.com.

Data 360's implementation can be complex, and it requires some advanced data modeling. The setup also calls for strong data governance, which posed some challenges. Additionally, predicting costs seems to be tricky, which might complicate budgeting. Review collected by and hosted on G2.com.