Data 360

What do you like best about the product?

Helps aggregate and clean our data before we run analytics on it. Allows us to centralize data from multiple sources

What do you dislike about the product?

Setting up data streams with outside sites can be challenging, especially for data from our websites.

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

Cleaning outside data so SF AI and other tools can combine outside data with Salesforce data.

What do you like best about the product?

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.

What do you dislike about the product?

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.

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

Siloed Data Across Systems
Problem: Customer data was fragmented across separate platforms, which led to incomplete customer records and duplicated effort across teams.
Benefit: Unifies cross-platform data into a single source of truth within Salesforce, giving teams immediate visibility into complete, up-to-date customer profiles.

Slow, Resource-Heavy Integrations
Problem: Moving data between warehouses and the CRM required custom ETL pipelines that were slow to build, brittle to change, and time-consuming to maintain.
Benefit: Zero Copy architecture and direct data connectors reduce heavy data replication, saving engineering hours and lowering ongoing storage overhead.

Inaccurate or Un-grounded AI Responses
Problem: AI agents and subagents can hallucinate or miss key context when they can’t securely access real-time enterprise data.
Benefit: Grounds Agentforce and automated flows in unified, real-time context, enabling more reliable service automation and more personalized customer interactions.

What do you like best about the product?

I like that it lets me consume large amounts of data from different systems. I like the advanced tools it gives me for mapping data.

What do you dislike about the product?

There’s no native way to support migration from other systems, such as CRM Analytics, which makes adoption harder. On top of that, the required skill set is different, and this has made it difficult for some legacy CRM Analytics users to migrate smoothly.

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

It allows us to map data from different systems into a single system of record. So far, adoption has been low, with cost being a factor. It’s a great tool with vector search, but we haven’t seen any benefits yet.

What do you like best about the product?

Salesforce Data 360 excels at unifying enterprise data and driving real time intelligence across systems.

Integrations: Smooth zero copy data sharing with warehouses like Snowflake and native links across Salesforce applications remove complex data pipeline work.

UI / UX: Drag and drop mapping tools let both technical and business teams build segments quickly without writing code.

Performance: Rapid processing of large data streams ensures identity resolution and customer profiles update instantly.

AI / Intelligence: Built in Einstein AI leverages real time unified data to automate actions and power predictive insights.

Pricing / ROI: Strong return on investment by cutting storage overhead, reducing maintenance, and boosting conversion rates.

Support / Onboarding: Detailed Trailhead modules and structured guidance help teams deploy and adopt the platform smoothly.

What do you dislike about the product?

Documentation and Implementation Guidance: Official documentation can be abstract and difficult to navigate. There is a need for clearer, simpler step by step setup guides, real world reference architectures, and easier implementation cookbooks for admins rather than high level theoretical guides.

Pricing Predictability and Credit Consumption: The consumption based credit model can be hard to estimate and track. Unexpected usage spikes during data ingestion or identity resolution processing can make monthly billing and ROI forecasts unpredictable.

Steep Learning Curve and Setup Complexity: Getting started takes significant time and technical expertise. Setting up data model objects, identity resolution rules, and calculated insights requires specialized training, often forcing teams to rely on expensive external consultants.

Heavy Dependency on Upstream Data Quality: The platform exposes bad legacy data quickly. If duplicate records or messy field mappings exist in connected sources, matching rules will struggle and consume excessive credits trying to resolve bad data.

Debugging and Error Reporting Limitations: Troubleshooting failed data transformations or stream ingest errors can be tedious. The diagnostic log details inside the user interface could be much clearer when data syncs fail.

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

From a consulting perspective, Data 360 addresses the mess of fragmented customer data across enterprise tech stacks. Most client architectures are a patchwork of separate CRMs, legacy databases, data warehouses, and marketing tools. Connecting them used to mean building custom ETL pipelines, heavy middleware integrations, and manual SQL matching rules that constantly broke, hit API limits, and left teams operating on delayed data.

The biggest benefit to my work is how drastically it speeds up delivery and reduces long term overhead. With built in zero copy connectors for platforms like Snowflake and BigQuery, I can connect a client warehouse to Salesforce in days rather than spending weeks coding ingestion pipelines. The native identity resolution engine handles record deduplication out of the box, which frees up my team to focus on business logic rather than writing complex matching scripts. Once data is unified, it activates instantly across Core Salesforce tools like Flows, Apex, and Agentforce. Ultimately, this means clients spend less budget on fixing broken data pipelines and far more time getting real value from their data and AI investments.

What do you like best about the product?

What I like most about Salesforce Data 360 (formerly Data Cloud) is how it brings customer data from multiple sources together into a single, real-time customer profile. It also integrates smoothly with Salesforce products like Sales Cloud, Service Cloud, Marketing Cloud, and Agentforce, which helps enable more personalized customer experiences and supports better decision-making. Features such as identity resolution, segmentation, calculated insights, and AI-powered capabilities make it easier to create targeted audiences and automate actions. Overall, the platform feels scalable and secure, and it has significantly improved customer engagement while reducing the amount of manual data processing needed.

What do you dislike about the product?

The platform is feature-rich, but it can be complex to implement and learn. The initial setup, data modeling, and identity resolution often require experienced resources to get right. Pricing may be high for smaller businesses, and some integrations still need additional configuration before they work smoothly. More guided onboarding, a simpler setup process, and stronger low-code capabilities would improve the overall experience.

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

Salesforce Data 360 helps eliminate data silos by creating a unified customer view across multiple systems. This has improved our customer segmentation, enabled more personalized engagement, increased reporting accuracy, and strengthened cross-team collaboration. With real-time insights and AI-powered capabilities, we can make faster, data-driven decisions while reducing manual effort and improving overall operational efficiency.