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The decision on the table is whether Data Cloud becomes the customer data foundation of your Salesforce estate, or whether that role stays with a platform you already own. It is a decision with a long tail. The data model, identity rules and consent logic you set up become the base that every segment, journey and AI agent draws on. Changing them later means rebuilding what sits on top.
Data Cloud turns scattered records into one usable profile
Data Cloud collects data from Salesforce clouds and outside systems, maps it to one data model and resolves identities. The web visitor, the loyalty member and the customer in a service case become one profile. Identity resolution, the set of rules that decides when two records belong to the same person, is where most of the value and most of the design effort sit. Set the rules too loose and you merge strangers. Set them too strict and one customer stays three profiles.
Where Data Cloud ends and your CRM begins is a common source of confusion, and the line between a CDP, a CRM and a DMP is worth drawing before the scope is set. Data Cloud sits inside our marketing automation practice, next to campaigns that reach two million recipients. We design profiles for the journeys that will actually use them.
Data Cloud is built for activation
Data Cloud stores data, and its real job is to act on it. A data lake or warehouse holds data for analysis and reporting. Data Cloud connects that data to segments, triggers and actions across Salesforce and advertising channels, within minutes of a customer doing something when the data arrives as a stream. The same profile gives Agentforce, Salesforce's AI agents, the customer context they need to answer questions with facts from your own systems.
For marketing teams, segmentation is usually the first use case. Data Cloud segmentation for marketers now goes well beyond attribute filters, with prioritised segments, lookalike audiences and segments drafted from a plain-language prompt.
Zero copy changes the build-or-connect question
If you already run a warehouse such as Snowflake, Databricks, Google BigQuery or Amazon Redshift, Data Cloud can read from it without copying the data across. Salesforce calls this zero copy integration. It weakens the old objection that a customer data platform means a second copy of everything you own.
Which warehouses work in which direction depends on the current Salesforce release and your contract, so check the list before you design around it. Query patterns matter too. Whether federating beats copying for your data depends on how often you query it and how fresh it must be, which is the core trade-off in how zero copy integration works.
