Why You Need to Read This Completely
If you’re working with Salesforce Data Cloud or planning to leverage it, this is a must-read. Here’s why:
I will take you step by step through the entire data lifecycle from ingestion to activation using real-world sources and advanced techniques:
✔️ Data Ingestion: I bring in data from multiple sources, including Salesforce, Amazon S3, local files, and Snowflake Zero Copy Data using federated data access.
✔️ Profile Unification & Insights: I unify this data, resolve duplicates, and generate calculated insights and segments to unlock deep customer understanding.
✔️ Data Activation: Once the data is processed, I don’t just let it sit there I send it to a target organization using various methods:
- REST API calls for direct integrations
- Event-driven triggers for real-time actions
- Webhooks to push data to external systems
- Activations for publishing segments
This article is not just about theory it’s a hands-on, practical guide that covers real business use cases, showing you exactly how to move data, unify it, and make it actionable.
By the end, you’ll have a clear roadmap on how to master Salesforce Data Cloud and use it to drive business success.
Now, let’s dive in! 🔥
Salesforce Data Cloud Architecture
1. Introduction to Salesforce Data Cloud
What is Salesforce Data Cloud?
Salesforce Data Cloud is a powerful platform designed to unify and activate customer data from various sources, providing businesses with a 360-degree view of their customers. It enables organizations to ingest, model, unify, and act on data to drive personalization, automation, and smarter decision-making.
Why is it Important for Modern Businesses?
With businesses collecting data from multiple touchpoints websites, social media, emails, transactions, and other sources having a centralized data strategy is crucial. Salesforce Data Cloud eliminates data silos, ensures real-time insights, and enables organizations to make data-driven decisions that improve customer engagement and operational efficiency.
Key Benefits of Salesforce Data Cloud
✔️Near Real-time data ingestion and processing
✔️360-degree customer view for deeper insights
✔️ AI-powered segmentation and calculated insights
✔️Seamless integration with Salesforce ecosystem
✔️Actionable data through activation and automation
2. The Salesforce Data Cloud Lifecycle
Salesforce Data Cloud follows a structured lifecycle to ensure data is properly collected, processed, analyzed, and activated for meaningful use. The key stages include:
- Data Ingestion – Bringing in data from multiple sources
- Data Modeling – Structuring data for better usability
- Profile Unification – Merging customer data for a single view
- Calculated Insights – Creating real-time insights based on behaviors
- Segments – Grouping audiences for targeted engagement
- Data Graphs – Connecting and visualizing customer relationships
- Activation – Taking action with the processed data
Each of these steps is crucial to unlocking the full potential of Salesforce Data Cloud.
3. Data ingestion is the first step in the lifecycle, involving:
Data Sources Used in This Process
Here’s a breakdown of the different types of data sources we are ingesting:
1️⃣ Contact_Home (Salesforce CRM Data)
- This dataset consists of contact records ingested directly from Salesforce CRM.
- It provides core customer profile data, including names, emails, and phone numbers.
2️⃣ Website Interaction Data (Direct File Upload)
- This data is ingested using a direct file upload.
- It contains customer interaction data from the website, tracking visits, clicks, and engagement.
- Useful for behavioral analysis and personalization.
3️⃣ Snowflake Data (Zero Copy Data)
- This dataset is ingested via federated access from Snowflake.
- It includes customer purchase history at ice and juice shops.
- Zero-copy integration ensures data is used without duplicating or moving it, maintaining efficiency.
4️⃣ Reservation Data (Amazon S3 Ingestion)
- This dataset is ingested from Amazon S3 storage.
- It contains customer reservation details from a booking system.
- Helps in understanding customer preferences and booking trends.
5️⃣ Guest Data (Amazon S3 Ingestion)
- Another dataset sourced from Amazon S3.
- It contains details of customers who have booked reservations, but may not yet be in Salesforce as contacts.
- Useful for identifying new potential customers and expanding engagement.
4. Data Modeling
- Once data is ingested into Salesforce Data Cloud, the next critical step is data modeling and profile unification. This process ensures that data is structured properly and mapped to the Customer 360 Data Model, making it usable for insights, segmentation, and activation.
Understanding Data Storage & Mapping in Data Cloud
🔹 Data Streams & Data Lake Objects (DLOs)
- Data streams connect to ingested data sources.
- The ingested data is stored in Data Lake Objects (DLOs) before further processing.
🔹 Mapping Data to Data Model Objects (DMOs)
- Data in DLOs is mapped to Data Model Objects (DMOs) based on the Customer 360 Data Model.
- This mapping ensures data consistency and identity resolution across different sources.
Key DMOs in Salesforce Data Cloud
The core DMOs play different roles in structuring and unifying customer data:
1️⃣ Individual DMO (Essential for Identity Resolution)
- The primary entity representing a customer in the system.
- All profile-related data must map to this object.
2️⃣ Contact Point DMOs (Required for Engagement)
- Contact Point Phone DMO – Stores phone numbers for communication.
- Contact Point Email DMO – Stores email addresses.
- Contact Point Address DMO – Stores physical addresses.
- At least one contact point is required for customer engagement.
3️⃣ Party Identification DMO (Optional, Improves Accuracy)
- Stores additional identifiers like customer IDs or external system references.
- Helps improve identity matching across sources.
4️⃣ Party Field & Relationship Mapping
- The Party field in Contact Point and other DMOs links them to the Individual DMO using Party = Individual. Id.
- This creates essential relationships between customer records.
How I Mapped Data to DMOs
I mapped different data sources based on whether they contain customer profile information or transactional/interaction data:
✔️ Profile Data Sources (Mapped to Standard DMOs)
- Contact_Home, Guest, and Website Interaction Data → These datasets contain direct user profile information, making them eligible for identity resolution.
- They are mapped as follows:
Individual DMO → Profile fields
Contact Point Phone DMO → Phone numbers
Contact Point Email DMO → Email addresses
Contact Point Address DMO → Physical addresses
The Party field is used to create relationships between these DMOs and the Individual DMO.
✔️ Transactional & Interaction Data Sources (Mapped to Custom DMOs)
- Reservation Data (Amazon S3) & Snowflake Data (Purchase History) → These datasets do not fit into standard DMOs.
- Solution: We created custom DMOs to store this information.
- This ensures flexibility while maintaining linkages to Individual DMO for unified insights.
5. Identity Resolution & Profile Unification
Now that we have mapped all our Data Lake Objects (DLOs) to Data Model Objects (DMOs), we are ready to unify customer profiles using Identity Resolution.
What is Identity Resolution?
Identity resolution is the process of merging duplicate records and linking data across different sources to create a single, unified customer profile. It ensures that we don’t treat the same customer as multiple different individuals due to variations in their records.
Key Data Sources for Identity Resolution
Our profile data comes from multiple sources:
✔ Website Interaction Data
✔ Salesforce Contact_Home
✔ Guest Data from Amazon S3
All of these were mapped to:
✔ Individual DMO (to store customer identity)
✔ Contact Point DMOs (to store phone, email, and address)
However, in identity resolution, the Individual DMO and Contact Point DMOs play the main role in merging duplicate profiles.
Configuring Identity Resolution in Salesforce Data Cloud
There are two main rule sets we define to control the profile unification process:
1️⃣ Match Rules (How Records are Compared)
Match rules determine how Salesforce Data Cloud identifies similar records. We use different comparison methods:
- Exact Match – Used for highly unique fields like email or phone number.
- Fuzzy Match – Used for fields with minor variations like names (“Jon Doe” vs. “John Doe”).
- Probabilistic Match – Uses AI-based probability scoring to determine matches across multiple attributes.
Records that meet the criteria are considered the same customer and will be merged.
2️⃣ Reconciliation Rules (Which Data is Retained)
Reconciliation rules define how conflicting data is resolved during unification.
For example, if a customer’s email is present in Website Data, Salesforce, and Guest Data, we need to decide which one to keep. We can define rules such as:
✔ Most Frequent Value – Keeps the value that appears most often.
✔ Priority-Based Selection – Example: Salesforce data is considered more reliable than website data.
Once these rules are configured, we run the rule set, and Salesforce Data Cloud generates unified records.
Understanding the Unified Objects Created
After running the identity resolution rules, Salesforce Data Cloud generates four key objects that store the final unified profiles:
1️⃣ Unified Individual CCID (Rule Name) – The master customer profile that combines data from multiple sources.
2️⃣ Unified Contact Point Phone – The consolidated phone number for each customer.
3️⃣ Unified Contact Point Email – The unified email address.
4️⃣ Unified Contact Point Address – The final standardized mailing address.
5️⃣ Unified Link Individual CCID – The key object for tracking source records.
- This object links the unified profile back to its original data sources (Salesforce, Website, Snowflake, S3).
- It allows us to trace where each data point came from.
How Data is Quered Back From Unified Individual ID
✔ Querying Unified Profiles
Unified Individual ID is the primary key used to query unified profiles. Data Cloud stores these profiles in the Unified Individual object, which contains aggregated data from multiple source records.
✔ Tracing Back to Source Records (Unified Link Objects)
Unified Link Objects act as bridges between unified profiles and the original data streams. Each unified profile may be linked to multiple source records.
✔ Querying Contact Point Data (Emails, Phones, etc.)
Since contact point data is critical for engagement, you can retrieve it using the Party ID, which corresponds to the Unified Individual ID.
✔ Querying Engagement Data
If you need to query engagement data (e.g., clicks, purchases, etc.), you can use the Unified Individual ID to access relevant records in engagement objects.
✔ Reconstructing the Full Customer Journey
To get a comprehensive view of the customer’s interactions, join the unified individual Id and other data.
How Relationships Work Behind the Scenes
- PartyId in contact point objects (e.g., ContactPointEmail, ContactPointPhone) is a foreign key linking to UnifiedIndividualId in UnifiedIndividual.
- Unified Link Objects maintain the connection between UnifiedIndividual and source records.
- Engagement Events reference the UnifiedIndividualId through the PartyId, ensuring consistent linkage across engagement data.
Why Identity Resolution is Crucial
✔ Removes duplicate records to avoid fragmented customer views.
✔ Creates a golden customer record with the most accurate and reliable data.
✔ Improves personalization by ensuring engagement is based on a unified profile.
✔ Enhances reporting and analytics by providing a single source of truth.
With profile unification complete, we can now move on to calculated insights and segmentation to derive actionable intelligence from our data.
6. Calculated Insights & Segmentation
Now that we have a unified customer profile, the next step is to derive actionable intelligence from this data using calculated insights and segmentation.
What Are Calculated Insights?
Calculated Insights in Salesforce Data Cloud help in:
✔ Aggregating customer data across multiple interactions.
✔ Deriving meaningful business metrics for decision-making.
✔ Enhancing segmentation and personalization efforts.
Key Calculated Insights Created
We created two key calculated insights to measure customer engagement and spending patterns:
1️⃣ Web Key Metrics (Website Engagement Tracking)
This insight helps track customer behavior on the website:
- Total Time Spent on Website: Aggregates the total duration a customer spent across different sessions.
- Total Web Sessions: Counts the number of times a customer has visited the website.
- Why It Matters: Helps identify highly engaged users who interact frequently with the website.
2️⃣ Spend Profile By Guest (Customer Spending Analysis)
This insight tracks customer reservations and spending patterns:
- Total Reservations: The number of times a customer has made a reservation.
- Total Amount Spent: The total amount a customer has spent till now.
- Why It Matters: Helps identify high-value customers based on their spending behavior.
Creating a High-Value Customer Segment
Using the calculated insights, we created a segment to identify our top-tier customers:
Segment: High_Profile_Customer (VVIP Customers)
✔ Criteria: Customers who have spent more than $10,000 in total.
✔ Purpose: These customers are considered VVIP and require special attention and personalized engagement strategies.
✔ Business Impact:
- Ensure priority service for high-value customers.
- Design exclusive offers, loyalty perks, and premium support.
- Optimize marketing and sales strategies to retain them.
Why This Step is Critical?
✔ Data-Driven Decision Making – Helps identify key customers and optimize engagement. ✔ Better Customer Retention – High-value customers receive personalized experiences. ✔ Improved Business Growth – Ensures resources are focused on the most profitable customers.
Now that we have segmented our high-value customers, the next step is to activate this data and use it across different channels for marketing, automation, and personalization.
7.Data Graphs in Salesforce Data Cloud
Now that we have ingested, unified, and enriched data, it’s essential to define relationships between different data entities. This is where Data Graphs come into play in Salesforce Data Cloud.
1️⃣ What is a Data Graph?
A Data Graph in Salesforce Data Cloud is a logical representation of how different data objects relate to each other. It enables us to define relationships between different data sources and use connected customer data for analytics, and activations.
2️⃣ Creating a Data Graph in Data Cloud
In our use case, we have multiple data sources:
- Contact_Home (Salesforce Contact Data)
- Guest (Customer booking details from Amazon S3)
- Website Interaction Data (Uploaded file data)
- Snowflake Purchase Data (Zero Copy data)
- Reservation Data (Reservation details from Amazon S3)
To connect all these data points, we define a Data Graph that links Individual Profiles with their Engagements, Purchases, and Reservations.
📌 Steps to Create a Data Graph
1️⃣ Navigate to Salesforce Data Cloud → Click on Data Graphs.
2️⃣ Create a New Data Graph → Provide a meaningful name.
3️⃣ Add DMOs (Data Model Objects) – Select the Individual DMO as the core entity.
4️⃣ Establish Relationships:
5️⃣ Validate the Graph – Ensure all relationships are correctly mapped.
6️⃣ Save & Publish the Graph – The Data Graph is now ready.
8. Acting on Data: Activations & Automation
Now comes the most crucial step acting on the data. All our efforts in data ingestion, profile unification, calculated insights, and segmentation would be useless if we don’t put this data to work.
Salesforce Data Cloud provides several ways to activate and operationalize data, but in this blog, we will focus on four key activation methods:
In this use case, we will send high-value customer data to the following destinations:
✔ Home Org
✔ Other Target Orgs (Financial Services Cloud & Sales Cloud)
✔ Custom Webhook (To send real-time updates to an external system)
1️⃣ Data Cloud Enrichments (Enhancing CRM Data)
✔ Objective: Update customer records in Salesforce CRM (Home Org) with enriched insights from Data Cloud.
✔ Example Use Case:
- The High_Profile_Customer segment (customers spending over $10K) gets enriched with calculated insights (Web Key Metrics, Spend Profile).
- This data is written back into Salesforce CRM to provide sales and support teams with real-time intelligence.
2️⃣ Activation Targets
✔ Objective: Publish high-value customer segments to org.
✔ Example Use Case:
- The High_Profile_Customer segment is sent to Financial Services Cloud (FSC) for wealth management teams to handle high-net-worth customers.
- The same segment is sent to Sales Cloud for targeted VIP engagement strategies.
- Marketing Cloud can use this data to create personalized campaigns for premium customers.
3️⃣ Using Data Action Platform Event to send data and trigger Flow in Connected FSC Org(Real-Time Actions)
✔ Objective: Automate data processing and distribute insights using custom logic.
✔ Example Use Case:
- Flow: When a new VVIP customer is identified, a Flow automatically triggers a task for the Fsc team.
4️⃣ Sending Data Cloud Data Using Webhooks(Real-Time Actions)
In many real-world scenarios, businesses need to send Salesforce Data Cloud data to external systems in real-time. Webhooks provide a powerful way to achieve this by sending HTTP requests to an endpoint whenever specific events or data changes occur.
What is a Webhook?
A webhook is a real-time API callback mechanism where Data Cloud automatically pushes data to an external system when a condition is met. Unlike traditional APIs, which require pull-based requests, webhooks allow event-driven communication between systems.
✔ Why Use Webhooks?
- Real-time data synchronization with external applications.
- No need for manual API polling—data is pushed automatically.
- Seamless integration with third-party platforms, marketing tools, or custom applications.
Why This Step is Critical?
✔ Ensures data is not just stored but actively used. ✔ Automates real-time decision-making for better customer engagement. ✔ Allows businesses to personalize customer interactions at scale.
5️⃣ Sending Data Cloud Data Using Apex and REST API & Showing Data in Other Orgs Using LWC
Now that we have unified and segmented our data, the next step is sharing this valuable data with other Salesforce orgs or external systems. We will achieve this using:
- Apex Callout & REST API – To send data from Data Cloud to another Salesforce org.
- REST API in Target Org – To receive and store the data.
- LWC (Lightning Web Component) – To display the received data in the target org’s UI.
With these activation strategies in place, we have successfully closed the data loop, ensuring that insights lead to actions that improve customer engagement, marketing efficiency, and business outcomes.
Other Activation Options in Salesforce Data Cloud
Salesforce Data Cloud provides multiple ways to activate and distribute data beyond the ones we covered. Here are some additional methods:
1️⃣ Data Cloud One (Unified Data Across All Salesforce Applications)
2️⃣ Data Cloud Data Share (Sharing Data Securely with External Partners)
3️⃣ Salesforce Connect (Live Access to External Data Without Storing It)
4️⃣ Change Data Capture (CDC) (Track Changes in Data in Real-Time)
Conclusion: Data is Useless Without Action
🔹 We ingested data from Salesforce, Amazon S3, Snowflake, and local files.
🔹 We unified customer profiles using Identity Resolution.
🔹 We created Calculated Insights & Segments to identify high-value customers.
🔹 We activated data using enrichments, activations, APIs, and events.
The power of Data Cloud lies in turning raw data into business actions. Without activation, data is just numbers sitting in a database.
