Knowledge is powerful only when it is whole. A customer researching a product online and then visiting your store, if not mapped correctly, creates fragments of insights.
Therefore, you need a platform that brings these fragments together, helping your organisation to understand and respond to your customers in real time.
Salesforce Data Cloud – Its Evolution and Capabilities
Over the last few years, Salesforce has quietly reshaped the way businesses use data. What started as an effort to bring customer information together has now grown into one of the most powerful data platforms available. Salesforce Data Cloud is the result of this journey and understanding how it evolved helps explain why it has become such an important part of the modern business toolkit.
The Evolution of Salesforce Data Cloud
The story begins in 2020 with Customer 360 Audiences. At the time, many businesses were struggling to make sense of the data scattered across different systems.
Customer 360 Audiences was Salesforce’s first customer insights hub that was built to unify customer data into a single profile and enable audience segmentation for personalised engagement. This helped companies to group customers by demographics, behaviour, or purchase history. For the first time, Salesforce users could look at the bigger picture of who their customers really were.
By 2021 and 2022, this vision expanded into what was known as the Customer Data Platform (CDP). Along with the features of Customer 360, it added tighter integration with Marketing Cloud, which made it easier to activate data directly within marketing campaigns. The CDP was all about moving from simply storing data to actually using it to drive personalised engagement.
In 2022, Salesforce introduced Genie. Genie brought the magic of real-time data processing. Businesses could now respond instantly to customer behaviour, whether it was a website visit, a purchase, or a service request. This was also the stage where AI-powered insights entered the picture, helping organisations predict needs and tailor their offerings with greater accuracy.
Finally, in 2023, Genie grew into what we now call Salesforce Data Cloud. The focus broadened beyond customer engagement to data management as a whole. With capabilities like data ingestion, governance, security, and quality controls, Data Cloud has become the central nervous system of Salesforce’s ecosystem. It also powers Salesforce’s Generative AI innovation, enabling businesses to make smarter decisions and deliver truly personalised experiences.
Capabilities of Salesforce Data Cloud
Unlike traditional repositories, Salesforce Data Cloud is a customer intelligence hub, built to consolidate data, generate meaningful insights, and enable hyper-personalised customer journeys.
Its core capabilities make it one of the most advanced solutions for businesses looking to maximise the value of their data across touchpoints. Let us explore these capabilities in detail:
- Data Collection: Centralising Customer Information
- It supports real-time and batch data ingestion
- There are connectors for Salesforce-native products (Sales Cloud, Service Cloud, Marketing Cloud) and external systems
- Secure storage is enabled within the Salesforce ecosystem, maintaining compliance and governance standards
- Data Transformation: Preparing Data for Action
- Merging & Joining: Combine multiple datasets to form a holistic view
- Filtering: Remove irrelevant or duplicate records
- Cleansing: Correct formatting errors, remove noise, and ensure data quality
- Normalisation: Standardise attributes like addresses, dates, or contact details for consistency
- Reshaping: Map datasets into the Salesforce data model to ensure compatibility across applications
- Identity Resolution: Building Unified Customer Profiles
- Consolidated profiles across online and offline interactions
- Resolution of duplicate identities into one master record
- Enriched context for each customer journey
- Segmentation & Audience Building: Creating Targeted Audiences
- Segment customers by purchase history, browsing behaviour, or lifecycle stage
- Build lookalike audiences to target new prospects
- Continuously refine audiences with dynamic updates as new data flows in
- Data Activation: Turning Insights into Action
- Marketing Cloud for personalised campaigns
- Sales Cloud for smarter prospecting and upselling
- Service Cloud for proactive and contextual customer support
- Commerce Cloud for tailored shopping experiences
The foundation of Salesforce Data Cloud lies in its ability to seamlessly collect and fetch data from multiple sources. Whether it is first-party data from CRM systems, transactional data from ecommerce platforms, behavioural data from websites, or third-party enrichment sources, Salesforce Data Cloud ensures that all streams merge into a single, scalable environment.
Imagine this: your marketing team sends a personalised email – “Hey Rahul, your running shoes are back in stock!†Or your sales team gets an alert, “High-intent shopper: Rahul just compared two products.â€
A global fashion retailer collects millions of customer interactions daily from mobile app purchases, online browsing, to in-store transactions. With Salesforce Data Cloud, all these separate data streams are unified, allowing the marketing team to understand each customer’s journey in a single, comprehensive view. This centralised approach improves personalisation, reduces operational complexity, and ensures consistent, secure management of sensitive customer data.
Raw data is often messy and inconsistent. Salesforce Data Cloud provides robust transformation capabilities to ensure that collected information is accurate, clean, and ready to use. The platform enables:
Demonstration – A leading financial institution was struggling with a common but critical problem where duplicate customer records were spread across multiple regions. The same person could appear under slightly different names, contact details, or account numbers in different systems. This not only made it hard to get a clear view of customers but also complicated compliance reporting and slowed down customer service.
By implementing Salesforce Data Cloud, the bank used its powerful data transformation tools to clean, merge, and standardise over five million customer records. What once looked like fragmented data was now a single, accurate, and unified customer view.
With this clarity, the institution could easily meet compliance requirements, run targeted marketing campaigns based on complete customer profiles, and deliver faster, more personalised support. The result was greater efficiency, improved trust, and a smoother experience for both customers and teams.
One of the standout features of Salesforce Data Cloud is its identity resolution engine. Modern customers interact with brands across multiple channels such as mobile apps, websites, social platforms, and in-store experiences. Without identity resolution, these touchpoints remain fragmented.
Salesforce Data Cloud uses various matching techniques – deterministic (connects records using exact identifiers like email or phone number) and probabilistic (links records using patterns and attributes such as location, device, or behaviour), to unify customer identifiers, creating a holistic view of each individual. This results in:
Case study – A travel company noticed something unusual: the same customer was showing up as three different people across its app, website, and loyalty program. Each profile held a piece of the puzzle: browsing history on one, past bookings on another, and reward points on the third.
Using Salesforce Data Cloud, the company brought all these scattered details together into a single, unified profile. Suddenly, they could see the full picture of that customer’s journey, such as where they travelled, what they liked, and how they engaged.
With this clarity, the marketing team created personalised offers and loyalty rewards that truly matched individual preferences. The impact was palpable, they witnessed happier travellers, stronger loyalty, and a noticeable rise in repeat bookings.
Once customer profiles are unified, Salesforce Data Cloud empowers organisations to build, segment, and manage audiences with precision. Using its powerful Audience Manager, businesses can define customer groups based on demographic attributes, behavioural traits, engagement patterns, or predictive scores.
Example – It started with a question that every automotive brand was asking: “Who among our loyal customers is ready to go electric?â€
For this global automaker, the shift from petrol to EVs was not just about launching a new model. It was about finding the right audience, those who were curious, engaged, and ready to take the next step.
Using Salesforce Data Cloud, the marketing team brought together data from service records, website visits, and dealership inquiries. Patterns began to emerge. Customers who frequently browsed EV pages, downloaded sustainability guides, or owned models nearing upgrade cycles were marked as “high-intent.â€
With Data Cloud’s segmentation tools, they built precise audience groups and even crafted lookalike audiences with people who shared similar traits and interests.
This led to tailored campaigns that matched each group’s interests. Within weeks, engagement rates soared, test drive bookings doubled, and what began as a data experiment turned into a strategic play for customer loyalty and innovation.
The true power of Salesforce Data Cloud lies in its ability to activate data across the Salesforce ecosystem and beyond. Once audiences and insights are built, they can be directly synced to applications like:
For instance, before Salesforce Data Cloud, the retailer’s marketing team was swimming in data but struggling to act on it. After connecting behavioural insights to Marketing Cloud, something changed.
Emails became smarter. Offers became personal.
Customers received messages like:
“Hi Priya, your favourite sneakers are waiting. Enjoy 15% off if you order tonight.â€
That simple, data-driven campaign turned hesitant browsers into buyers and proved that when insights move, so does revenue.
Key Features and Concepts of Salesforce Data Cloud
Salesforce Data Cloud comes with a rich set of features designed to help organisations collect, unify, transform, and activate customer data at scale. To maximise value, it is important to understand the core concepts and technical components that form the platform. Below is a detailed breakdown of the most important Salesforce Data Cloud features.
- Data Sources: Bringing Information into Salesforce
- Native connections: Out-of-the-box integrations with Salesforce products like Sales Cloud, Service Cloud, Marketing Cloud, and Commerce Cloud
- SDKs: Web and mobile SDKs for capturing event-level behavioural data such as clicks, page views, app sessions etc.
- Third-party integrations: Connectors to external CRM, ERP, and data warehouse platforms such as AWS, Snowflake, Google BigQuery, or Azure
- Data Streams: The Flow of Information
- Supports batch incorporations for large datasets and real-time streaming for event data
- Handles structured (example – relational databases), semi-structured (JSON, XML), and unstructured data
- Automatically updates as new records flow in, ensuring profiles remain up to date
- Salesforce Data Pipelines: Transforming Data Visually
- Includes a native function library for cleansing, filtering, joining, normalising, and reshaping data
- Designed with a drag-and-drop UI for ease of use for analysts, and also supports advanced SQL-like expressions for complex operations
- Ensures raw integrated data becomes clean, standardised, and analytics-ready
- Data Spaces: Secure and Organised Data Governance
- Separate data by brands, regions, or business units within the same Salesforce environment
- Control permissions, security, and compliance at a granular level
- Prevents cross-contamination of datasets while still enabling global-level reporting
- Data Lake Objects (DLOs): The Storage Layer
- Each DLO corresponds to a consolidated dataset, whether customer details, transactions, or behavioural data
- Acts as the raw storage layer before transformation
- Scalable to handle petabytes of structured and semi-structured data
- Customer 360 Data Model: The Unified Schema
- Defines standard objects, fields, metadata, and relationships across datasets
- Ensures consistency across all Salesforce applications
- Helps organisations create a unified view of each customer
- Data Model Objects (DMOs): The Building Blocks
- Examples include Individual (customer record), Engagement (interactions), Transaction (purchase), etc.
- Define the way customer information is structured and related
- Serve as the foundation for segmentation and activation
- Data Mapping: Bridging Ingested Data and the Model
- Ensures merged datasets align with Salesforce’s schema
- Enables segmentation, analytics, and activation without manual restructuring
- Reduces errors by enforcing consistency and integrity
- External Data Lake Objects: Federated Access to External Data
- Supports federation from external data sources (e.g. Snowflake, AWS Redshift, BigQuery)
- Allows Salesforce to query external data without copying it
- Maintains governance while avoiding unnecessary duplication
- Segments: Creating Custom Audiences
- Define audiences using drag-and-drop criteria or advanced rules
- Support dynamic updates as new data flows in
- Can be used for personalisation, campaigns, or lookalike audience creation
- Activation Targets: Sending Data Where It Matters
- Native endpoints: Salesforce Marketing Cloud, Sales Cloud, Service Cloud, and Commerce Cloud.
- External destinations: Ad networks, customer engagement platforms, and analytics tools.
- Supports batch and real-time data pushes.
- BYOL Data Federation: Virtualising Warehouse Data
- No redundancy and no need to copy data physically.
- Salesforce accesses your external data warehouse (Snowflake, Databricks, BigQuery, etc.) virtually.
- Reduces storage costs while keeping insights accessible.
- Zero-Copy Data Sharing: Salesforce to Warehouse
- Avoids creating extra copies of data.
- Allows BI and analytics teams to use their preferred tools.
- Provides a seamless bridge between Salesforce and enterprise data ecosystems.
- Accelerated Data Federation: Improving Performance
- Improves query speed by caching metadata and optimising requests.
- Reduces refresh intervals to bring near real-time access to federated data.
- Balances performance with governance for enterprise use cases.
Data Sources are the starting point for every Salesforce Data Cloud implementation. They define where your data originates and how it enters the system.
Once sources are defined, Data Streams represent the actual flow of data into Salesforce Data Cloud. Think of them as the live pipelines that continuously feed information into the system.
This capability is critical for businesses relying on real-time customer engagement, such as ecommerce or financial services.
Data Pipelines provide a visual, low-code interface for preparing and transforming your data before it maps to the Salesforce model.
This capability reduces the need for heavy IT involvement and quickens time-to-insight.
Data Spaces act as logical partitions within Salesforce Data Cloud that allow organisations to manage governance and user access at scale.
This feature is especially valuable for multinational enterprises or companies managing multiple product lines.
DLOs (Data Lake Objects) are the fundamental storage containers for data merged into Salesforce Data Cloud.
Think of DLOs as the raw building blocks upon which the Customer 360 model is built.
At the heart of Salesforce Data Cloud is the Customer 360 Data Model, a standardised schema that provides a logical hierarchy for customer data.
DMOs are the specific entities within the Customer 360 Data Model.
By mapping data into DMOs, businesses can unlock downstream insights in Marketing Cloud, Service Cloud, or Commerce Cloud.
Data Mapping is the process of linking raw integrated data (DLOs) to structured DMOs in the Customer 360 Data Model.
This step is critical to ensure your data becomes usable across the Salesforce ecosystem.
Not all data needs to live inside Salesforce Data Cloud. External Data Lake Objects (EDLOs) act as references to data stored externally.
This feature is perfect for companies managing large enterprise data lakes that already exist outside Salesforce.
Segments are groups of customers defined within the Audience Manager based on shared attributes, traits, or behaviours.
After creating segments, the next step is activation. Activation Targets determine where these audiences are sent for use across systems.
Bring Your Own Lake (BYOL) Federation allows businesses to virtualise warehouse data inside Salesforce Data Cloud.
Zero-Copy enables the reverse: instead of virtualising external data into Salesforce, it lets you query Salesforce Data Cloud data directly in your data warehouse.
This is the best approach for organisations running data-intensive operations outside Salesforce.
Large-scale queries across external warehouses can create latency. Salesforce solves this with Accelerated Data Federation.
Conclusion
Salesforce Data Cloud is more than a data platform. It consolidates scattered data sources, resolves customer identities, and enables real-time personalisation across touchpoints.
With capabilities such as Data Pipelines, Data Spaces, BYOL Federation, Zero-Copy Sharing, and Accelerated Data Federation, it gives enterprises the flexibility to manage complex data while activating insights immediately.
Embitel brings practical expertise to implementing Salesforce Data Cloud. We help organisations integrate their systems, structure their data efficiently, and deploy scalable solutions that turn insights into measurable outcomes.
Our strategic approach, along with Salesforce services, enables businesses to deliver smarter and more purposeful customer experiences.
FAQs
- How do Einstein Bots gather and assess customer information through conversations?
- Is Einstein AI easy to use?
- Is Salesforce Data Cloud like Snowflake?
- Is Salesforce Data Cloud an ETL tool?
- What is Snowflake in Salesforce Data Cloud?
Einstein Bots collect and qualify information through AI-powered conversations that mimic natural human interaction.
When a customer starts chatting, the bot uses predefined conversation flows and natural language processing (NLP) to understand intent. It then asks relevant questions like name, order ID, or issue type to gather key details.
As the conversation continues, the bot analyses responses in real time to determine what the customer needs and how best to respond. It can route the query based on the answers and update records or hand over to a live agent with all the collected contexts.
This approach helps businesses deliver faster, more accurate, and personalised support without overwhelming human teams.
Yes, Salesforce Einstein AI is designed to be easy and intuitive to use, even for teams without a deep background in data science.
Einstein Model Builder is one of the core tools that offers a user-friendly interface where you can create, train, and deploy AI models directly within your Salesforce environment.
It helps to build models from scratch using Salesforce data to predict outcomes such as lead conversion, churn, or sales forecasting.
You can connect pre-trained models from other platforms, so your business can leverage existing machine learning investments.
It enables you to integrate external AI models from other sources seamlessly with your CRM data through APIs.
What makes Einstein AI particularly easy to use is its low-code/no-code approach. Business users can configure AI features using guided workflows instead of writing complex code.
Not exactly. While both handle data, they serve different purposes. Snowflake is a cloud-based data warehouse used mainly for storing, organising, and analysing large amounts of data from many sources. Salesforce Data Cloud, on the other hand, is a customer data platform (CDP) that focuses on bringing together customer data from various systems to create a single, real-time view of each customer.
Snowflake helps you understand your data, while Salesforce Data Cloud helps you use that data to personalise customer experiences. Many businesses utilise both Snowflake for in-depth analytics and Salesforce Data Cloud for activating insights across sales, marketing, and service, transforming raw data into tangible business impact.
No, Salesforce Data Cloud is not a conventional ETL (Extract, Transform, Load) tool, but it includes many ETL-like capabilities within its customer data platform.
While some ETL tools focus on moving and cleaning data, Salesforce Data Cloud goes further as it not only collects and transforms data from multiple sources but also unifies customer identities and activates insights across Salesforce apps like Marketing Cloud, Service Cloud, and Sales Cloud.
In Salesforce Data Cloud, Snowflake serves as an integrated cloud data platform that allows businesses to extend and unify their data ecosystem. It acts as an external data warehouse where large volumes of structured and semi-structured data are stored and analysed.
By connecting Snowflake with Salesforce Data Cloud, organisations can securely access and query their Snowflake data in real time without duplicating or moving it. This is made possible through features like Zero-Copy Data Sharing and Bring Your Own Lake (BYOL) Federation.
Snowflake enhances Salesforce Data Cloud by enabling businesses to gain deeper insights, improve decision-making, and reduce storage and operational costs.
