Salesforce Data Cloud helps businesses break down data silos, gain a deeper understanding of their customers, and leverage AI to build smarter applications. Understanding its capabilities is key for developers creating more data-driven applications that deliver better customer experiences.
In this blog post, we’ll walk you through a clear and concise diagram of Data Cloud, highlight its core capabilities, and provide links to further reading for each capability along the way.
Access your key data sources
The foundation of any powerful data platform is the ability to access data from disparate systems in your enterprise. For Data Cloud, accessing your data is the critical first step. Data streams are capabilities that make this as efficient as possible.
Data streams
Data streams are connections that continuously ingest data from various enterprise data sources. Connectors are available to many common applications, and data can be streamed, scheduled, or made available using zero-copy capabilities.
Each ingestion mechanism fits a broad number of use cases for web, mobile, and common enterprise applications and scales to huge volumes of data. Zero-copy capabilities allows Data Cloud to access data stored in external platforms without physically copying or moving the data itself.
Connect unstructured data
Connectors allow you to connect unstructured data files from your blob storage systems. Data Cloud doesn’t copy the files, but instead extracts their metadata to use when making their contents searchable across a vector database.
Represent data in a consistent format
Data sources have differing schemas. Structured data will have table and column names that differ but need to be represented in the same way. Data mapping is the capability that maps your source data to a common data model. For example, Contacts, Guests, Customers, and People are all individuals with the same attributes and map to the Individual object in Data Cloud. Businesses and Companies map to the Account object.
Unstructured data, such as emails, chat transcripts, PDFs, and knowledge articles, are a rich source of data that is hard to search using keywords. Indexing and embeddings help to convert your unstructured data to a format that is searchable and are key enablers for generative AI use cases.
Data source objects
Data source objects (DSOs) in Salesforce Data Cloud act as containers for the initial data brought into the system. They hold the information in its raw, original format before any transformations occur.
Data lake objects
Data lake objects (DLOs) are automatically created when data is ingested into Data Cloud. You can also create them manually. DLOs store structured data that are typically from sources like databases or CRM systems. Formulas and transforms can be added to augment the data when needed.
Unstructured data lake objects (UDLOs) are designed to make unstructured data, such as text files, emails, and chat transcripts, searchable. UDLOs are created manually and reference the unstructured data location. Each row within the objects corresponds to a single file on the external blob store.
Data model objects
Data model objects (DMOs) are typically mapped to data model objects (DMOs) which define a common structure for the data. There are standard data model objects provided or you can create custom objects when necessary.
Unstructured data model objects (UDMOs) are objects that form the basis for using embedding models to create vectors from your unstructured data.
Data lake objects and data model objects form the backbone of data used to drive insights and they provide data to predictive and generative AI services.
Unify data sources for the same person and business
Data about your customers and businesses is typically stored across a number of systems. Each source likely has some overlap but also contains unique details, and it is sometimes difficult to know if records across these systems relate to the same person without a common identifier. Identity resolution helps connect these dots, creating a single, comprehensive profile for each customer or company.
Identity resolution
Built-in matching and reconciliation rules can identify data that is likely to be related to the same person or business. Matching rules allow you to add criteria based on data in the common data model with match methods, such as fuzzy matching (where entries are approximately similar but not identical), exact matching, and normalized exact matching (where entries are transformed to address issues like trailing spaces, inconsistent formatting, special characters, etc).
Data Cloud can also reconcile data based on the best value to save to a unified profile. LAST UPDATED, MOST FREQUENT, and SOURCE PRIORITY gives administrators the ability to ensure that data is consistent and of high quality.
Drive insights by enriching your data
Accessing data, mapping it to a common model and unifying data are important to enabling a consistent way to drive insights. Data Cloud capabilities help identify interests, buying habits, and online behavior, allowing you to make data-driven decisions for marketing, sales, operations, and customer service.
Vector search
By bringing unstructured data into Data Cloud, you can ground your generative AI, analytics, and automation search results with business-specific data that delivers deeper insights for your users and customers. Unstructured information, such as free-form text, chat transcripts, PDFs, and emails can be brought into Data Cloud, broken into meaningful chunks, and converted into machine-readable vector embeddings. These vector embeddings are then added to a search index, which can be used to perform vector search queries from apps like Prompt Builder, Agentforce, or Tableau.
Data graphs
A data graph combines and transforms normalized table data from data model objects (DMOs) into new, materialized views of your data. Because the data is pre-calculated, you can make fewer calls, and queries respond in near real-time.
Segments
You can use segmentation to break down your data into useful segments to understand, target, and analyze your customers. You can create segments on objects from your data model, and then publish them on a chosen schedule or as needed.
Insights
Data Cloud offers multiple ways to gain insights from your data, including calculated, streaming, and real-time insights. Insights help analyze datasets for historical trends, and long and short-term customer behavior.
Model Builder
You can create and deploy generative and predictive AI models. Build models with a user-friendly interface and guided steps, or Bring Your Own Model with providers like Databricks, Amazon Sagemaker, Google Vertex AI, and OpenAI. This enables you to enrich your data with predictive insights and power generative AI capabilities with custom large language models (LLMs).
Einstein audit trail
The Einstein audit trail in Data Cloud is all about tracking how Salesforce uses generative AI. Specifically, it focuses on the Einstein Trust Layer, a set of features designed to ensure the safety and accuracy of these AI-generated responses. Data Cloud collects and stores two types of data for Einstein Generative AI: generative AI audit data and feedback data. Pre-built Data Cloud reports and dashboards can be used to analyze the usage and share insights, such as user trends, acceptance rates, and response feedback
Taking action on data changing in your enterprise
A key capability of Data Cloud is its ability to put insights into the hands of customers, employees, and partners who need it to drive tangible business outcomes.
Data shares
Data shares provide easy access for third-party partners to use data in Data Cloud. With access to data lake objects (DLOs), data model objects (DMOs), and calculated insight objects (CIOs), you can build powerful analytical insights and machine learning models. Data Cloud objects are shared without replicating the data into a partner ecosystem. With this zero-copy data sharing, you get near real-time access to data.
APIs
You can programmatically extract your data from Data Cloud using APIs. Retrieve metadata and query key data points using well-defined REST APIs. Check out the Postman collection for details. For Python developers, there’s a connector that uses the Query API and extracts data from Data Cloud.
The Models API (Beta) provides Apex classes and REST endpoints that connect your application to large language models (LLMs) from Salesforce partners, including Anthropic, Google, and OpenAI. The capabilities of the Models API are expressed as Apex methods or REST endpoints. Generate chat, embeddings, and text, and submit feedback on any generated text.
Data actions
Data Cloud enables you to monitor data changes in data model objects and calculated insight objects, and send the change data events to a Salesforce Platform Event, Marketing Cloud, or a webhook.
A data action can enable different types of event-driven integrations and orchestrations. For example, you can:
- Orchestrate Salesforce CRM workflows with insights and data events from Data Cloud
- Integrate data actions in Mulesoft Anypoint by sharing aggregated event data with external partners based on criteria
- Integrate with SaaS applications with signals from Data Cloud
- Trigger serverless functions that work with a webhook based on insights in Data Cloud
- Push unfiltered insights and engagements to your data lake for analysis and storage
Activations
Activation is the process that publishes a segment to activation platforms. An activation target is used to store authentication and authorization information for a given activation platform. You can publish your segments, include contact points, and add additional attributes to the activation targets. Activation targets can be file-based storage, ad services or marketing and B2C commerce clouds.
Enrichments
Data Cloud enrichments makes data from Data Cloud available in standard Salesforce components that you can add to specific record pages. For example:
- Add related lists to key objects in Salesforce to show related information directly from Data Cloud with related list enrichments
- And fields with data from Data Cloud to your Contact, Account and Lead record pages with copy field enrichments
Automation
You can build automation workflows based on data changing in Data Cloud, get records, and start administration jobs using Flow Builder to orchestrate changes within the context of Salesforce data. For example:
- Trigger a flow based on data changes in a data model object
- Get records from a data model object
- Get predictions from a model created in Model Builder
- Start administration jobs
Analytics
Data Cloud allows you to use various tools and products to analyze your data with direct data connectors for Salesforce, CRM Analytics, and Tableau. Developers can also use the APIs provided for programmatic access, or use the Java Database Connectivity (JDBC) driver.
Conclusion
Salesforce Data Cloud helps developers unify and leverage their customer data across the enterprise. You can then build data-driven applications that unlock AI-powered insights and deliver new customer experiences with insights that were previously unattainable.
To reinforce your understanding of Data Cloud, watch our video series on Data Cloud Fundamentals, where we walk you through a tangible use case and explain each step in detail.
Resources
- Video Playlist: Data Cloud Fundamentals: A Guided Tour
- Documentation: Get Started Using Data Cloud
- Documentation: Customer 360 Data Model
- Trailhead: Data Cloud for Admins
- Trailhead: Get Hands-on with Data Cloud
- Learn more about Salesforce Data Cloud
- Learn more about Salesforce AI
About the author
Dave Norris is a Developer Advocate at Salesforce. He’s passionate about making technical subjects broadly accessible to a diverse audience. Dave has been with Salesforce for over a decade, has over 35 Salesforce and MuleSoft certifications, and became a Salesforce Certified Technical Architect in 2013.