Learn How Data Cloud Works

Learning Objectives

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

Explain key features of Salesforce Data Cloud.
Understand how data is used across Einstein 1 products.

What Is Salesforce Data Cloud?

Data Cloud is a data platform that combines the power of the Salesforce Platform with the scalability of an infrastructure that allows for processing data in near–real-time. Salesforce Data Cloud offers a bridge to harness data split across many orgs, Marketing Cloud, web engagement, across warehouses and lakehouses—to be used for AI, analytics, and automation. It can also handle tremendous scale—and we mean tremendous. Data Cloud can process trillions of records, petabytes of data, and thousands of requests per second per customer. For reference, one petabyte is equal to 1,000 terabytes. And 1 terabyte is equal to 1,000 gigabytes. How many gigabytes did your first computer have?

Data Cloud expands Salesforce capabilities by using the best pieces of the developer-friendly Salesforce Platform and adding a highly scalable infrastructure. Data Cloud is an evolution of Customer Data Platform—which was originally designed for marketers but now caters to broader use cases beyond marketing.

In this badge, we demystify Salesforce Data Cloud through the view of a product expert. This module is a video-based module with EVP of Software Engineering Muralidhar Krishnaprasad ready to help you understand Data Cloud capabilities.

How It Works

So how does Data Cloud really work?

Data Cloud data and functionality diagram.

Connect all your data sources, whether batch or streaming real-time data.
Prepare your data through transformation and data governance features.
Harmonize your data to a standard data model.
Unify data with identity resolution rulesets.
Query and analyze data using insights.
Use AI to predict behavior.
Analyze, expand, and act on your data in any channel.
Segment audiences and create personalized experiences.
Output data to multiple sources to act on data based on your business needs.
Continue to review, measure, and optimize data.

Connect and Ingest Data

It all starts with bringing data into Data Cloud.

Data Cloud connects to various Salesforce and external data sources, including:

Sales, Service, Commerce, and Marketing Cloud Engagement connectors
Amazon S3, Google storage connectors
Ingestion API and Salesforce Interaction SDK
Web and Mobile connectors
MuleSoft connector
Snowflake
And more!

Overall, Data Cloud makes it easier to bring all your data, whether streaming or batch, together into your Salesforce applications.

Learn more about ingestion in the Ingestion and Modeling in Data Cloud module.

Transform and Model Data

If you’ve ever spelled your name wrong in a form field, you might understand why data needs to be transformed. The good news is Data Cloud allows customers to prepare, cleanse, and transform data before it is used. Data is diverse and can look different in various sources—for instance, a product order, a contact in Sales Cloud, or an anonymous web browser. With Data Cloud, harmonize data from those different sources into a standard data model—the Customer 360 Data Model.

Explore data mapping in the Customer 360 Data Model for Data Cloud module.

Unify, Enhance, and Analyze Your Data

Once you have data in Data Cloud, you can unify customer data into one profile, enhance the data using insights and AI, create segments, and analyze it in analytics tools, such as Tableau.

Unify Data

To identify who is who, we need to match and reconcile these records. In Data Cloud, all person-related data (like phone number or deviceIDs) is mapped to the Individual DMO in the Customer 360 Data Model. Once mapped, rulesets are created in the Identity Resolution (IR) feature to identify how to find matches. For example, a rule might specify that all records of individuals with the same email address and name should be combined into one profile. Data Cloud allows you to choose and reconcile which information is used in the unified profile for that customer.

The Data and Identity in Data Cloud module focuses on unifying data using Identity Resolution.

Enhance Data with Insights and AI

With a unified, normalized, and harmonized view of a customer’s information, you can enhance that data with Calculated Insights (CI). Create powerful metrics and key performance indicators (KPIs) based on batch or streaming data. Batch calculated insights create metrics such as “total customer value” or “products over $500.” Streaming insights are created based on a rolling time window. For example, you can identify the click-through rate of all the products in an online storefront in the past 30 minutes. You can also connect custom machine learning (ML) models with Data Cloud to explore data and train models for better predictions and personalized insights.

Find more information about building insights in the Data Cloud Insights module.

Segment and Analyze Data

Not only can you create powerful metrics with insights, but you can also segment your data and then analyze it using a variety of analytics tools. Since Tableau is integrated with Data Cloud, all the standard data model objects and relationships are viewable in Tableau. By using the Direct Query functionality, you can analyze insights or any other data with a single click in CRM analytics.

Act on Data

The magic of Data Cloud is in creating experiences that wow customers. Data Cloud offers many ways for users to act on data.

Marketers create audience segments in Data Cloud that are used for personalized marketing campaigns within Journey Builder. Data Cloud segments can also be activated to a rich ecosystem of advertising partners, including Facebook (Meta) and Google.

Learn how to build segments in Data Cloud in the Segmentation and Activation module.

Beyond marketing, Data Cloud data can be used to create experiences in Sales Cloud, Service Cloud, Commerce Cloud, Marketing Cloud Personalization, and more. With Data Cloud, streaming events drive actions in various locations or targets. Data actions then use events, streaming insights, and data changes to trigger flows. For example, an automotive company uses a data action to trigger an alert to create an automatic service call, when a customer’s vehicle crosses the 75,000-mile mark.

In the next unit, we go further into use cases and share a demo.

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