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Get to Know the Customer 360 Data Model

Learning Objectives

After completing this unit, you’ll be able to:

  • Describe data model challenges.
  • Recognize Customer 360 Data Model terminology.
  • Explain how Salesforce is using the Customer 360 Data Model.


If you'd like to listen to an audio recording of this module, please use the player below. When you’re finished listening to this recording, remember to come back to each unit, check out the resources, and complete the associated assessments. As this is a prototype, we’d really appreciate your feedback on the experience. You can provide it by completing this short survey.

Customer 360 Data Model

Successful missions often have hi-tech gadgets and technical support running in the background. For Data Cloud to manage and organize large amounts of data, we have support behind the scenes, too. We use the Customer 360 Data Model to standardize and connect data sources with different data structures and formats. It uses APIs (a developer tool that allows for systems to talk to each other) and other mappings to connect applications and data. Basically, the Customer 360 Data Model is Data Cloud's standard data model that provides a way to make integration seamless and scalable. This also reduces the barriers to cross-product integration. Standardization is important for a couple of reasons.

Data Models

In search of connected experiences and digital transformation, many companies adopt multiple systems. Remember our NTO example with team members using data from lots of different sources? That’s pretty typical. The larger the company, the more systems they work with. And each system comes with a unique data model. This makes it challenging for users to unify data across departments and systems. Companies often solve this issue by asking developers or consultants to create custom code and solutions to connect the dots, which can slow innovation and lead to brittle integrations.

Database Structure

Enterprise data is rarely standardized. The data is instead heavily customized for specific business requirements—and can even be found in its raw form with unlimited decimals or lengths. This can be messy, but it gets messier. Data can also be classified as structured or unstructured (meaning with or without a formal data model). And there are many varieties of types of databases that store data, from relational databases that use Structured Query Language (SQL) to ones that don't, called NoSQL databases. (For reference, Marketing Cloud Engagement stores data using a relational database structure.) Many organizations today store their data in a variety of databases. Having different types of data is fine, as long as it is easily retrievable—which isn’t always the case.


Learn more about data systems in the Trailhead module Strategies for Big Data Architecture.

A Marketer’s Guide to the Data Model

As a marketer, you know that data is what drives your business and personalized experiences for your customers. While you don’t need to be an expert on data models and architecture, it’s helpful to know how it impacts your work. Think of how a customer record is stored in a retail point of sale (POS) system and how that same customer is identified in Marketing Cloud Engagement. A POS might store a customer as a random number based on the time of the sale (like 1145). Marketing Cloud Engagement stores that same customer using a subscriber key (like Susan1145). So how can you integrate systems that have different ways of identifying the same customer, along with different data formats?

The Customer 360 Data Model solves this by creating standardized data models that can be used in common scenarios based on subject areas (like sales orders). This helps standardize your data to make data mapping easier. If data mapping is easier, identifying the same customer in multiple systems becomes easier.

Customer 360 Data Model Terminology

Let’s go over the basic components of the Customer 360 Data Model.

Basic diagram of subject areas, DMOs, and attributes

Subject Area

A subject area is a business concept or term used to group similar data objects to aid in data modeling, for example, sales orders, loyalty or engagement data. Each subject area contains one or more data model objects.

Data Stream

A data source brought into Data Cloud, for example, a Marketing Cloud Engagement customer data extension. These data streams can be based on batched data or real-time data streams. 

Data Model Object (DMO)

A data model object is a grouping of data (made up of attributes) that are created from data streams, insights, and other sources. DMOs can be standard or can be custom, based on business need. Common standard DMOs include sales orders, party identification, email engagement, and so on. 


An attribute, also called a field, is a specific piece of data found in a DMO, for example, a customer’s first name. This is similar to a data extension field in Marketing Cloud Engagement.

Foreign Key

A foreign key is a common link found between data sources that builds data relationships—for example, a customer ID number. 

Salesforce, the Data Model, and You

Why embrace the Customer 360 Data Model? It helps bring together and standardize data across various Salesforce apps, as well as from outside sources. By using the Customer 360 Data Model, your data becomes more usable across a variety of platforms. It allows us to deliver a self-service data management platform, for even the most complex data pipelines. 

For marketers, the Customer 360 Data Model is used in Data Cloud to make data modeling marketer-friendly using pre-built models based on common marketing use cases. These crafted data models for things like general customer engagement or tracking customer loyalty are called data bundles. While we won’t dive into specific information about data bundles in this module, the thing to know is that data bundles help you standardize your data without extensive manipulation or a PhD in data science.

Intrigued? Earn the Customer 360 Data Model for Data Cloud badge to learn even more about the standard data model. 


If the pre-built data use cases or standard options don’t work for your business, you also have the ability to customize your own data models.

Marketing Cloud Engagement Contact Model

Implementing Data Cloud won’t impact your current contact model in Marketing Cloud Engagement Contact Builder. Your contact model in Marketing Cloud Engagement is just one piece of the data model puzzle. It’s the job of Data Cloud to integrate and work across multiple channels and clouds. That being said, in the next unit, we cover some prep work you can do to prepare for Data Cloud.


Want more info about data strategy? Check out the Trailhead module Customer-Centric Data Strategies to learn more about customer data considerations.

Now that you have an overview of the products and how the data is managed behind the scenes, let's take a closer look at Data Cloud.


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