Introduction

Salesforce Data Cloud is a powerful platform designed to centralize customer data from various sources, including AI, analytics, and CRM. Organizations are able to deliver outstanding customer experiences in sales, service, marketing, and commerce by creating a single source of truth that allows them to obtain actionable insights instantly.

In 2020, Data Cloud and Salesforce launched Salesforce Customer 360 Audience. It has experienced a number of creative changes since then. After changing names to Salesforce Customer Data Platform in 2021, Marketing Cloud Customer Data Platform & Salesforce Genie followed in 2022. Ultimately, Salesforce Data Cloud became its official name in 2023.

Customer data from a variety of sources, such as websites, mobile apps, and finished goods, within Salesforce organizations, and clouds like Sales or Service, is combined via Data Cloud. It improves the Customer 360 experience overall and raises the functionality and potential of all Salesforce clouds as part of the Salesforce Platform.

Data Cloud incorporates AI and automation, just as Flow and Einstein have added intelligence and automation to many Salesforce ecosystem components. This enables businesses to provide individualized experiences across a range of business processes and make data-driven decisions.

What makes using Salesforce Data Cloud for your company necessary?

1. Data Catalog

See your whole customer database from social media, marketing automation platforms, and Salesforce in a 360-degree perspective. You can quit scouring compartmentalized systems using Salesforce Data Cloud! To help you find and use the information you need fast, this revolutionary application indexes and logically organizes your data resources. To provide customized promotions, a retail corporation use the Data Catalog to determine which client segments make frequent purchases.

2. Versioning of Data

Similar to Google History's history capabilities, it preserves comprehensive recordings of the stuff you have looked up or viewed on the search engine. Jobs like process auditing, identifying the source of data points, and keeping a lineage trail are made a lot easier by this capability. It offers organizations an easier way to comply with compliance standards like the Sarbanes-Oxley Act.

3. Quality of Data

With Salesforce's cloud, your data is always clean, fresh, and of the highest caliber—much like fruits in a premium market! It provides you with excellent accuracy and dependability because it has built-in tools for data enrichment, authentication, and cleansing. By using these technologies, a financial institution can lower risk by, for instance, confirming the veracity of the loan applicant's information.

4. Data Pipelines that are Automated

Think of the information in your data as a component of a busy industrial machine. Without requiring laborious manual intervention, Salesforce Data Cloud seamlessly automates data intake and transformation. In an e-commerce setting, this facilitates quick data processing, enabling the analysis of client purchasing trends.

5. Compliance and Data Privacy

Salesforce Data Cloud provides capabilities that help comply with laws like GDPR and CCPA, reducing the likelihood of data leaks in an era where data privacy is crucial. For example, a healthcare practitioner doesn't have to worry about breaking data privacy rules when processing patient data.

6. Support for Multiple Clouds

Salesforce Data Cloud is accessible wherever you need it, just like your go-to restaurant! By spreading out the deployment of this solution over several cloud providers, you may minimize reliance on one supplier and maximize cost-effectiveness. It's similar to having Netflix accessible on Apple TV, Amazon Fire Stick, and additional streaming devices.

7. Lifecycle Management of Data

Salesforce Data Cloud's automated data lifecycle management is like having a personal assistant for data that can handle things like archiving, managing, and removing data. This keeps your company's data house in order by lowering storage costs and maintaining data governance, similar to a data recycling system.

Step-by-Step Procedure for Implementing Salesforce Data Cloud

1. Establish the Data Cloud Organization

Access User Setup:

Navigate to Setup -> User -> Select the user.

Enable permission sets in Data Cloud Admin. Optionally, enable Data Cloud Marketing Admin and Data Cloud Legacy Permission Sets.

Build the Data Cloud Org UI:

Click Setup, then select Data Cloud Setup.

Click Get Started to install Data Model Managed Packages.

2. Identify Sources of Data

Assemble all intended data sources, such as Salesforce CRM, Salesforce B2C Commerce, Salesforce Marketing Cloud, Einstein Analytics, AWS S3, Google Cloud Storage, Azure Storage, SFTP, MuleSoft Any point Exchange, websites, mobile apps, and API for ingestion.

3. Connect Data Sources

Example: Connecting Salesforce CRM with Data Cloud:

Go to Data Cloud Setup > Salesforce CRM.

The home CRM org will already be connected. To connect another org, click New.

Select Connect on the Connect Another Org option.

Enter your org login credentials. After successful login, a new org record will be visible in connectors.

Create a data stream for CRM by going to the Data Stream tab in the Data Cloud app > New.

Select Salesforce CRM > Next > Select Org.

Choose Data Bundles (Sales or Service) or All Objects, select Category > Next.

Select Data Space > Deploy.

4. Harmonize Data

Go to the Data Stream > Select Start in Data Mapping.

Select Objects.

Map necessary fields with Data Model Entities fields > Save.

If fields are not available in standard Customer 360 data model objects, create new custom fields or objects.

5. Segmentation

Go to the Segments tab in the Data Cloud app > Create New Segment.

Select Data Space, Segment on, and provide Segment Name or Description.

Select Publish type and schedule.

Save > Add criteria to include and exclude data in the segment > Save.

Save > Done (after adding criteria).

6. Unify Data

Go to Identity Resolution in the Data Cloud app.

Create a new ruleset by selecting Data Space, Primary Data Model Object, and entering ruleset ID > Next.

Enter ruleset name > Next.

Configure Match Rules and Reconciliation Rules.

Create a custom rule or select and modify standard rules.

7. Analysis

Einstein Studio for AI Predictions:

Go to the Einstein Studio tab in the Data Cloud app > Create New Model.

Add Endpoints, Variables, Outputs > Activate the model.

Calculated Insights for Analytics and Insights:

Use BI tools like Tableau to analyze the data.

8. Act

Data Actions:

Create Data Action Targets by going to the Data Action Targets tab in the Data Cloud app > Create New Data Action Target.

Provide a name and type (Salesforce Platform Event or Webhook).

Create Data Actions by going to the Data Actions tab in the Data Cloud app > Create New Data Action.

Select Data Action Target > Next.

Select Data Space, Object Type, Primary Object or Related Object > Next.

Select Attributes > Next.

Select Event Rules and Action Rules > Next.

Provide Action Name > Save and Publish.

Activations:

Create Activation Targets by going to the Activation Target tab > Create New Target.

Select Salesforce Apps in the org or other external activation platforms like AWS S3 > Next.

Provide Activation Target Name > Save.

Create Activations by going to the Activations tab in the Data Cloud app > Create New Activation.

Select Segment, Activation Targets, Activation Membership > Continue.

To create an activation, create a data cloud object.

Add Attributes > Next > Save.

Conclusion:

The key to leveraging data to increase productivity and return on investment for your company is Salesforce Data Cloud. Your decision-making, customer experience, and sales, service, marketing, and commerce can all be optimized with Data Cloud's centralized customer data, AI, and automation.