Salesforce Data Cloud Modelling: A Comprehensive Guide 2024

Salesforce Data Cloud offers a robust framework for data modeling that ensures seamless integration, efficient data management, and powerful analytical capabilities. In today’s data-driven landscape, organizations are inundated with vast amounts of data from various sources. 

According to IDC, the global data sphere is expected to reach 175 zettabytes by 2025, highlighting the critical need for efficient data management strategies. Salesforce Data Cloud solves this challenge by enabling companies to create a unified and comprehensive view of their data.

Data modeling in Salesforce Data Cloud involves designing a logical structure of data elements and their relationships. This is crucial for enabling effective data integration, ensuring consistency, and enhancing query performance. With over 150,000 companies using Salesforce and its revenue surpassing $34.86 billion by May 2024, the platform’s data cloud capabilities are pivotal for businesses aiming to harness their data for competitive advantage.

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This guide will explore the core principles and best practices of Salesforce Data Cloud modeling, providing a detailed roadmap for data architects and administrators. Organizations can significantly enhance their data management practices by understanding and implementing these concepts, leading to better decision-making and operational efficiency.

What is Data Cloud Modelling?

Data modeling in Salesforce Data Cloud involves designing a logical structure of data elements and their relationships. It enables effective data integration, consistency, and query performance. This guide will cover the following key aspects of Salesforce Data Cloud modeling:

• Normalized vs. Denormalized Data
• Primary Keys, Foreign Keys, and Cardinality
• Harmonization to C360 Data Model
• Standard vs. Custom Data Models

Normalized vs. Denormalized Data

Normalization is the process of structuring data to minimize redundancy and dependency. It involves dividing large tables into smaller ones and defining relationships among them. This approach improves data integrity and reduces data anomalies.

Benefits:

• Improved data integrity
• Efficient data storage management

Drawbacks:

• Increased complexity in querying due to the need for joins or inner queries

Example: Salesforce’s customer 360 data model employs a normalized data model, storing individual information and contact point details in separate objects.

Conversely, denormalization involves merging data from related tables into a single table, which can enhance query performance by reducing the need for complex joins.

Benefits:

• Improved query performance

Drawbacks:

• Increased data redundancy
• Higher storage costs and risk of data inconsistency

Example: The Contact object in Salesforce’s Sales/Service Cloud is denormalized, storing multiple phone numbers, email addresses, and addresses within the same object.

Primary Keys, Foreign Keys, and Cardinality

Primary Keys are unique identifiers for records in a table, crucial for ensuring each record can be uniquely accessed.

Foreign Keys are fields that link two tables, facilitating data integrity and navigation across related data.

Cardinality refers to the nature of relationships between tables, including:

• 1:1: One record in a table is related to one record in another table.
• 1: One record in a table relates to multiple records in another.
• N:1: Multiple records in a table are related to one record in another table.

Fully Qualified Keys (FQK) are used to avoid key conflicts when integrating data from multiple sources, ensuring harmonized data representation in the Data Cloud model.

Also Read – Salesforce Data Cloud Implementation Approach

Harmonization to C360 Data Model

Harmonization involves mapping disparate data sources to a common data model, enabling a unified view of customer data. This process ensures consistency, identity resolution, and cross-data source analytics.

Benefits:

• Provides a single view of the customer
• Enables cross-data source calculated insights
• Supports business intelligence analysis

Standard vs. Custom Data Models

Standard Data Models align with Salesforce’s predefined structures and are essential for identity resolution and interoperability. They provide a consistent framework for data integration and are supported by various AppExchange offerings.

Benefits:

• Speed time to value
• Consistent data structures across applications

Custom Data Models are tailored to fit specific business requirements, offering flexibility in defining unique objects, fields, and relationships.

Benefits:

• Match existing data structures for easier integration
• Provide flexibility for specialized data needs

Data Modelling Best Practices

1. Create a Data Dictionary: Maintain an inventory of each source system’s data elements, documenting field-level details and mappings to Data Model Objects (DMOs).
2. Map Source Data to DMOs: Ensure accurate field mappings, considering how each source field relates to the Individual object or other relevant DMOs.
3. Consider Downstream Use Cases: Map data with a view to its end use, ensuring it supports desired analytical and reporting outcomes.
4. Transform and Normalize Data: Align source data with the standard data model, applying necessary transformations to achieve consistency.
5. Visualize Data Relationships: Use tools like the Data Model tab’s graph view to holistically inspect and validate object relationships.

Implementation Activities

Implementing Salesforce Data Cloud modeling involves design and execution phases, each critical for ensuring a robust and effective data model. Here’s a detailed breakdown of these activities:

Design Phase

Define Data Requirements:

• Identify the key data elements needed for your business operations and analytics.
• Conduct interviews with stakeholders to understand data usage scenarios and requirements.

Data Source Analysis:

• Catalog all data sources, including CRM systems, external databases, and third-party applications.
• Evaluate the quality, structure, and relevance of data from each source.

Data Dictionary Creation:

• Develop a comprehensive data dictionary documenting each data element, source, and intended use.
• Include metadata such as data type, format, and any transformation rules.

Logical Data Model Design:

• Create a logical data model outlining entities, attributes, and relationships.
• Use tools like ER diagrams to visualize the model and meet all business requirements.

Normalization and Denormalization Strategy:

• Decide which parts of the data model will be normalized or denormalized based on query performance needs and data integrity requirements.
• For example, customer master data might be normalized for accuracy, while sales data might be denormalized for faster reporting.

Primary and Foreign Key Identification:

• Identify primary keys for each entity to ensure unique identification of records.
• Define foreign keys to establish relationships between entities and enforce referential integrity.

Also Read – Data Cloud Strategy and Topology: A Comprehensive Guide

Execution Phase

Data Ingestion:

• Implement pipelines to ingest data from various sources into the Salesforce Data Cloud.
• To automate data extraction, transformation, and loading, use tools like Salesforce Data Loader, MuleSoft, or custom ETL scripts.

Data Mapping and Transformation:

• Map source data to the corresponding fields in the target Data Model Objects (DMOs).
• Apply necessary data transformations, such as format conversions, data cleansing, and enrichment, to align with the target model.

Validation and Testing:

Conduct thorough testing to ensure data accuracy and integrity. This includes:

• Unit tests for individual data mappings.
• Integration tests to validate end-to-end data flow.
• User acceptance tests (UAT) with business users to ensure the data meets their needs.

Deployment and Monitoring:

• Deploy the data model to the production environment following best change management and version control practices.
• Set up monitoring and alerting mechanisms to promptly detect and address data quality issues.

Continuous Improvement:

• Regularly review and refine the data model based on feedback and changing business requirements.
• Implement data governance policies to maintain data quality and compliance.

Practical Application: Hands-On Exercise

A hands-on exercise is crucial for reinforcing the concepts discussed. Practitioners should:

• Map AWS S3 customer data to Contact and Contact Point phone DMOs
• Map Email Address List to Contact Point email DMO
• Validate mappings using tools like Data Explorer and Query Editor

Explore our Salesforce Data Cloud series on YouTube for expert guidance and tips on leveraging Salesforce Data Cloud effectively.

Conclusion

Salesforce Data Cloud modeling is foundational for seamless data integration, robust data management, and powerful analytics. Data architects can build efficient and scalable data models that drive business value by adhering to best practices and understanding the nuances of normalized vs. denormalized data, primary and foreign keys, and the harmonization process.

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Frequently Asked Questions (FAQs)

1. What is Salesforce Data Cloud Modelling?

It’s the process of structuring data within Salesforce Data Cloud to ensure effective integration and management.

2. What is the difference between normalized and denormalized data?

Normalized data reduces redundancy by storing data in separate tables. Denormalized data stores all data in one table for faster queries.

3. Why are primary keys important?

Primary keys uniquely identify each record in a table, ensuring data integrity.

4. What is the purpose of foreign keys?

Foreign keys link records across different tables, maintaining relationships between data sets.

5. What is data harmonization?

Harmonization maps disparate data sources to a common model, providing a unified view of customer data.