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Course Details

Salesforce data 360

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Duration

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₹ 36999
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Salesforce Data 360

Master Salesforce Data 360, Customer Data, AI & Real-Time Data Activation

Salesforce Data 360 is an enterprise data platform designed to connect, harmonize, unify, analyze, and activate data from Salesforce and external systems. Formerly known as Salesforce Data Cloud, Data 360 helps organizations create a trusted, unified view of customers and business data while making that information available for analytics, automation, personalization, and AI-powered experiences.

With Salesforce Data 360, organizations can bring together data from CRM systems, websites, applications, marketing platforms, commerce systems, databases, data warehouses, data lakes, and other enterprise sources.

The Data 360 Data Lifecycle

Connect → Ingest → Transform → Harmonize → Unify → Analyze → Segment → Activate → Automate → Govern

What You Will Learn in Salesforce Data 360

Our Salesforce Data 360 training covers the complete data lifecycle, from data ingestion and modeling to identity resolution, analytics, segmentation, activation, AI, and governance.

1. Data Sources & Data Integration

Learn how Salesforce Data 360 connects with different enterprise data sources.

You will understand:

  • Salesforce CRM data

  • Sales Cloud

  • Service Cloud

  • Marketing Cloud

  • Commerce data

  • Website and application data

  • ERP systems

  • Databases

  • Data warehouses

  • Data lakes

  • External applications

  • APIs

  • Batch data

  • Streaming data

  • Real-time data

  • Supported connectors

  • Zero-copy data integration

Learn how data can be brought together from multiple systems without requiring every system to become the primary system of record.

2. Data Streams & Data Ingestion

Understand how data enters Data 360 through different ingestion mechanisms.

Topics include:

  • Data Streams

  • Batch ingestion

  • Streaming ingestion

  • Real-time ingestion

  • API-based ingestion

  • Salesforce data ingestion

  • External data ingestion

  • Data synchronization

  • Data refresh concepts

  • Monitoring data ingestion

You will learn how source-system data moves into Data 360 and becomes available for downstream processing.

3. Data Lake Objects (DLO)

Learn the role of Data Lake Objects (DLOs) in Salesforce Data 360.

DLOs represent source-oriented data that has been brought into the platform.

You will learn:

  • DLO fundamentals

  • DLO structure

  • Source data representation

  • Data ingestion into DLOs

  • DLO relationships

  • DLO and DMO differences

  • Working with external data

  • Unstructured Data Lake Objects

DLO vs DMO

DLO: Represents incoming/source-oriented data.

DMO: Represents harmonized business data according to the Data 360 data model.

Understanding this distinction is fundamental to working with Data 360.

4. Data Model Objects (DMO)

Learn how Data 360 transforms data from different systems into a common business data model.

Topics include:

  • Standard Data Model Objects

  • Custom Data Model Objects

  • DMO relationships

  • Data modeling

  • Object relationships

  • Attributes and fields

  • Primary identifiers

  • Foreign relationships

  • Standard Salesforce data models

  • Custom data models

You will learn how different source systems can represent the same business entity using different field names and structures, and how Data 360 harmonizes that information.

5. Data Mapping

Data Mapping connects source-system fields to the Data 360 data model.

For example:

CRM: customer_email
Website: email_address
Marketing: subscriber_email

These fields can be mapped to the appropriate customer/contact-point data structure in Data 360.

You will learn:

  • Source-to-target mapping

  • Field mapping

  • Object mapping

  • Data model relationships

  • Mapping validation

  • Mapping best practices

6. Data Transformation

Learn how to prepare and transform data before using it for analytics, identity resolution, segmentation, and activation.

Topics include:

  • Data cleansing

  • Data transformation

  • Field standardization

  • Data preparation

  • Combining datasets

  • Filtering data

  • Creating transformed outputs

  • Preparing data for downstream processing

Example:

IND → India → IN → INDIA

can be standardized into a consistent country value.

7. Data Spaces

Learn how Data Spaces help organizations logically separate data, metadata, and processes.

Data Spaces can be useful for:

  • Different brands

  • Business units

  • Regions

  • Departments

  • Countries

  • Projects

  • Different data use cases

Example:

Data 360

→ India
→ USA
→ Europe
→ APAC

Learn how Data Spaces support data organization, access control, and operational separation.

8. Identity Resolution

Identity Resolution is one of the most important capabilities of Data 360.

A single customer may exist in multiple systems:

  • CRM

  • Website

  • Mobile application

  • Commerce

  • Marketing

  • Customer service

For example:

CRM: John Smith
Website: J Smith
Marketing: John Smith
Commerce: J. Smith

Data 360 can use configured matching and reconciliation logic to determine which records represent the same individual or account.

You will learn:

  • Identity Resolution

  • Match Rules

  • Reconciliation Rules

  • Matching strategies

  • Individual unification

  • Account unification

  • Contact-point unification

  • Lead-to-account matching

  • Household matching

  • Unified profiles

  • Source-to-unified relationships

9. Unified Customer Profiles

After identity resolution, Data 360 can provide a unified representation of customer information.

A unified customer profile can bring together:

  • Customer information

  • Contact information

  • Purchases

  • Website activity

  • Marketing engagement

  • Service interactions

  • Product interactions

  • Loyalty information

  • Other relevant business data

This creates a connected view of customer activity across multiple systems.

10. Unified Contact Points

Learn how Data 360 works with customer contact information such as:

  • Email addresses

  • Phone numbers

  • Physical addresses

  • Application identifiers

  • Other contact points

This allows organizations to maintain a connected view of customer contact information across different systems.

11. Data Explorer

Learn how to explore Data 360 data and understand the information available within the platform.

Topics include:

  • Exploring DLOs

  • Exploring DMOs

  • Exploring calculated insights

  • Inspecting records

  • Understanding relationships

  • Validating data

  • Data investigation

Data Explorer is useful for data validation and understanding the structure and contents of your Data 360 environment.

12. Calculated Insights

Calculated Insights allow organizations to calculate meaningful business metrics from Data 360 data.

Examples include:

  • Customer Lifetime Value

  • Total Revenue

  • Average Order Value

  • Purchase Frequency

  • Number of Orders

  • Customer Engagement

  • Product Performance

  • Revenue by Customer

  • Revenue by Segment

  • Campaign Engagement

Example:

Total Revenue ÷ Number of Orders = Average Order Value

You will learn how to create and use calculated business metrics for analytics, segmentation, and activation.

13. Data Graphs

Learn how Data Graphs can provide a connected view of related information.

A customer Data Graph may connect:

Customer → Account → Orders → Products → Website Activity → Service → Loyalty

Data Graphs are particularly useful when applications and AI experiences require related information in a contextual structure.

14. SQL & Data Analysis

Learn how SQL can be used for advanced Data 360 data analysis and calculations.

Topics include:

  • SELECT statements

  • Filtering

  • Aggregations

  • GROUP BY

  • JOIN concepts

  • Calculations

  • Customer-level metrics

  • Revenue analysis

  • Data exploration

  • Advanced data analysis

Example:

SELECT
    customer_id,
    SUM(order_amount) AS total_revenue
FROM sales_orders
GROUP BY customer_id;

15. Segmentation

Learn how to create targeted audiences using unified Data 360 data.

Segments can be created using:

  • Customer attributes

  • Demographics

  • Transactions

  • Website behavior

  • Marketing engagement

  • Product activity

  • Calculated insights

  • Customer relationships

  • Business conditions

Example:

Customers who purchased more than ₹50,000 + visited the website in the last 30 days + have not purchased a particular product.

These audiences can then be used for supported activation and marketing use cases.

16. Data Activation

Data activation turns unified data and segments into actionable business experiences.

Learn how to activate data to supported destinations such as:

  • Salesforce applications

  • Marketing systems

  • Advertising platforms

  • Customer engagement channels

  • External destinations

Typical flow:

Unified Data → Segment → Activation → Customer Experience

17. Data Actions & Automation

Learn how Data 360 can trigger actions based on data and events.

Example:

Customer becomes a high-value customer

→ Data 360 event
→ Data Action
→ Salesforce Flow
→ Create task
→ Sales representative receives follow-up action

Topics include:

  • Data Actions

  • Events

  • Salesforce Flow integration

  • Trigger-based automation

  • Real-time actions

  • Data-driven workflows

18. Real-Time Data & Customer Experiences

Modern customer experiences require timely data.

Learn how Data 360 can work with streaming and real-time data for use cases such as:

  • Personalization

  • Customer engagement

  • Recommendations

  • Service experiences

  • Commerce

  • Marketing

  • AI-powered experiences

Example:

Customer views a product

→ Website event
→ Data 360
→ Customer context updated
→ Appropriate segment or decision
→ Personalized experience

19. Unstructured Data

Data 360 can also work with unstructured information.

Examples include:

  • Documents

  • Text

  • Knowledge content

  • PDFs

  • Other unstructured business information

Topics include:

  • Unstructured Data Lake Objects

  • Unstructured Data Model Objects

  • Document processing

  • Data preparation

  • AI-ready information

20. Data Chunking & Semantic Search

Learn concepts used to make unstructured information useful for AI and search applications.

Chunking

Large documents can be divided into smaller meaningful sections.

Document → Chunks → Searchable information

Semantic Search

Semantic search focuses on the meaning and context of information rather than relying only on exact keyword matches.

These capabilities are important for AI-powered retrieval and enterprise knowledge experiences.

21. AI & Salesforce Data 360

Data 360 provides an important data foundation for Salesforce AI capabilities.

The basic architecture is:

Enterprise Data → Data 360 → Harmonized Data → Unified Context → AI

Learn how trusted enterprise data can support:

  • AI experiences

  • Einstein capabilities

  • Agentforce

  • Customer context

  • AI-powered recommendations

  • Intelligent automation

  • Enterprise search

  • AI-assisted customer service

22. Data 360 & Agentforce

Learn how Data 360 can provide relevant enterprise context to Agentforce.

For example, an AI agent may need to understand:

  • Who the customer is

  • Previous purchases

  • Current orders

  • Products

  • Service interactions

  • Marketing engagement

  • Customer preferences

  • Other relevant business information

Data 360 can help connect these data sources and make the appropriate context available for supported AI experiences.

23. Tableau & Analytics

Data 360 data can also support analytics and visualization workflows.

Learn how to use unified business data for:

  • Dashboards

  • Customer analytics

  • Sales analytics

  • Marketing analytics

  • Commerce analytics

  • Revenue analysis

  • Customer segmentation analysis

  • Business performance analysis

Typical architecture:

Data 360 → Unified Data → Calculated Insights → Tableau → Dashboard

24. Predictive Analytics & AI

Data 360 can support predictive and AI-driven business use cases.

Examples include:

  • Customer churn analysis

  • Propensity analysis

  • Customer scoring

  • Recommendations

  • Next-best-action scenarios

  • Customer behavior analysis

  • Predictive business insights

The exact capabilities available depend on the Salesforce products, editions, licenses, and integrations used by an organization.

25. Zero-Copy Data Integration

Learn how Data 360 can work with external data without requiring all information to be physically copied into Salesforce.

Supported zero-copy architectures can connect Data 360 with external data platforms.

Conceptually:

External Data Platform ↔ Data 360

This approach can help organizations access existing enterprise data while reducing unnecessary data duplication.

26. Data Governance & Security

Data 360 includes governance and security capabilities that help organizations control how data is accessed and used.

Topics include:

  • Data access

  • Permissions

  • Data Spaces

  • Data governance

  • Data privacy

  • Data classification

  • Data sharing

  • Consent

  • Data lineage

  • Security controls

27. Consent & Privacy

Learn how consent information can be modeled and used within customer data workflows.

Important concepts include:

  • Individual consent

  • Contact-point consent

  • Communication subscription consent

  • Authorization

  • Privacy-related information

  • Marketing preferences

Example:

Email Marketing → Allowed
SMS Marketing → Not Allowed

Consent and privacy information can be important when creating compliant customer engagement workflows.

28. Data Lineage

Data lineage helps organizations understand how information moves through the Data 360 environment.

Example:

ERP

↓

Data Lake Object

↓

Data Model Object

↓

Identity Resolution

↓

Unified Profile

↓

Calculated Insight

↓

Segment

↓

Activation

Understanding lineage is important for:

  • Data governance

  • Troubleshooting

  • Compliance

  • Data quality

  • Impact analysis

29. Data Sharing

Learn how Data 360 data can be shared and accessed in supported architectures.

Topics include:

  • Data sharing

  • Data Share

  • DLO sharing

  • DMO sharing

  • Calculated Insight sharing

  • External data access

  • Data collaboration

30. End-to-End Salesforce Data 360 Architecture

A complete Data 360 implementation can follow this architecture:

Salesforce + Website + ERP + Database + Data Warehouse

↓

Connectors / APIs / Data Streams

↓

Data Ingestion

↓

Data Lake Objects (DLO)

↓

Transformation & Data Preparation

↓

Data Mapping

↓

Data Model Objects (DMO)

↓

Identity Resolution

↓

Unified Customer / Account Profiles

↓

Calculated Insights & Data Graphs

↓

Segmentation

↓

Activation & Data Actions

↓

Marketing + Sales + Service + Commerce + Analytics + AI

↓

Agentforce & Personalized Experiences

Why Learn Salesforce Data 360?

Salesforce Data 360 brings together several critical areas of modern enterprise technology:

  • Customer Data Platforms

  • Data Integration

  • Data Engineering

  • Data Modeling

  • Customer 360

  • Identity Resolution

  • Data Analytics

  • Real-Time Data

  • Marketing Activation

  • AI

  • Agentforce

  • Data Governance

Learning Data 360 can therefore be valuable for professionals working with Salesforce, CRM, data engineering, analytics, marketing technology, AI, customer experience, and enterprise data platforms.

Who Should Learn Salesforce Data 360?

This training is suitable for:

  • Salesforce Administrators

  • Salesforce Developers

  • Salesforce Data Cloud / Data 360 Consultants

  • Data Engineers

  • Data Analysts

  • CRM Professionals

  • Marketing Technology Professionals

  • Solution Architects

  • Salesforce Architects

  • Business Analysts

  • AI & Data Professionals

  • Customer Data Platform Professionals

  • Professionals moving into Salesforce Data 360

Salesforce Data 360 Learning Path

Beginner

Salesforce & Data 360 Fundamentals

↓

Data Sources & Connectors

↓

Data Streams

↓

DLO & DMO

Intermediate

Data Mapping

↓

Data Transformation

↓

Data Spaces

↓

Identity Resolution

↓

Unified Profiles

Advanced

Calculated Insights

↓

SQL

↓

Data Graphs

↓

Segmentation

↓

Activation

↓

Data Actions

Expert

Real-Time Data

↓

Zero-Copy Architecture

↓

Unstructured Data

↓

Semantic Search

↓

AI & Agentforce

↓

Governance & Enterprise Architecture

Build the Future with Data 360

Salesforce Data 360 connects enterprise data with the applications, analytics, automation, and AI experiences that organizations use every day.

From data ingestion and data modeling to identity resolution, unified customer profiles, analytics, segmentation, activation, AI, Agentforce, and governance, Data 360 provides a comprehensive framework for turning fragmented enterprise information into connected and actionable business context.

Learn Salesforce Data 360. Understand Enterprise Data. Build AI-Ready Customer Experiences.