Salesforce Data 360 (formerly Salesforce Data Cloud) is changing how businesses use customer data. Instead of working with disconnected systems and slow reports, companies can now bring all their data together in one place and act on it instantly. This turns scattered information into near real-time insights that drive smarter decisions and better customer experiences.
In this article, we’ll break down how Salesforce Data 360 works, what makes it powerful, and why it’s becoming the foundation for AI-driven customer engagement. You’ll see how it solves the problem of data silos and how it connects with tools like Agentforce to create more personalized, efficient agentic interactions.
We’ll also explain why Data 360 matters for modern businesses, and how Gearset helps teams deploy, manage, and version-control Data 360 configurations safely and easily. With the right setup, teams can innovate faster without sacrificing trust or stability.
What is Salesforce Data 360?
Salesforce Data 360 is a hybrid data lakehouse platform that unifies customer data from multiple sources and activates it directly within Salesforce in near real time. It combines the best of two worlds — the flexible, large-scale storage of a data lake and the structured, query-ready performance of a data warehouse.
Data 360 operates on two levels. First, it serves as the foundational infrastructure connecting and accelerating data movement across all Salesforce Clouds. Second, it functions as a standalone product that organizations can license to ingest, unify, and activate their data from external systems — CRM, ERP, marketing platforms, or external data lakes.
Unlike traditional databases that merely store information, Data 360 transforms business data into Salesforce metadata-driven objects, bringing it natively into the Salesforce metadata model. Teams can access, analyze, and act on unified data through familiar Salesforce interfaces — no complex data translations required.
Behind the scenes, Data 360’s open architecture uses technologies like Apache Iceberg and Apache Parquet to store and organize data efficiently across cloud environments such as Amazon S3. This standards-based approach allows enterprises to scale to petabytes while maintaining flexibility and performance.
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How Data 360 differs from data lakes and warehouses
A data lake and a data warehouse aren’t the same thing, even though the terms are often used interchangeably.
- A data lake stores massive amounts of raw data in its original form.
- A data warehouse organizes data into structured, queryable formats for reporting and analytics.
- Salesforce Data 360 unifies customer data from various environments to power near real-time engagement.
Traditional data warehouses are Online Analytical Processing (OLAP) systems designed for historical analysis, typically updating in scheduled batches. But, modern cloud data warehouses — like Snowflake and Databricks — have evolved to support near real-time data ingestion and processing, enabling more responsive analytics. Despite these advancements, they’re still primarily designed for analytical workloads rather than operational engagement.
While warehouses are built for analysis, Data 360 extends that by making unified data usable in day-to-day Salesforce operations. It doesn’t replace warehouses; instead, through zero-copy architecture, it integrates with them, making warehouse data actionable in Salesforce without duplicating or moving the data. This bridges analytical depth with operational speed — turning static data into dynamic customer intelligence.
The evolution from CDP to an enterprise platform
Salesforce Data 360 began as the Salesforce Customer Data Platform (CDP) — a marketing-focused solution for unifying first-party customer data. Before that, it evolved from a Data Management Platform (DMP) designed to manage third-party advertising data.
In 2022, Salesforce rebranded the technology as Genie, highlighting its new capability to process data in real time. As the platform matured, the transition to Data Cloud reflected its expanded mission: serving as the data backbone for the entire Salesforce ecosystem, not just marketing.
This evolution was driven by Salesforce’s own challenges — integrating data across acquisitions like ExactTarget (now Marketing Engagement), Demandware (now Commerce Cloud), and Tableau — and by the rise of generative AI.
Today, Salesforce Data Cloud has been rebranded yet again to Data 360 and stands as a complete enterprise solution that delivers a single, actionable view of every customer, connecting hundreds of systems to power consistent, intelligent experiences across all channels.
How Salesforce Data 360 works
Salesforce Data 360 connects customer data from CRMs, marketing platforms, websites, commerce engines, ERPs and more. It ingests data from these sources, transforms it into Salesforce’s Customer 360 data model, and keeps it current through powerful identity resolution logic.
The Customer 360 data model
At the heart of Salesforce Data 360 is the Customer 360 Data Model — a standardized framework that helps bring customer records, transactions, and interactions from diverse sources into a consistent structure. It acts as a shared language for your data, making it easier for systems across your organization to understand and work together.
Administrators can map incoming fields from any source directly to this shared model. That means less time spent cleaning and aligning data, and more time acting on it. The model supports both structured data — like records and tables — and unstructured data such as documents or emails, so nothing important gets left behind.
Because it’s fully extensible, teams can add custom objects and fields to match their unique business needs, while still benefiting from Salesforce’s built-in consistency and reporting standards. The result is a reliable data foundation that powers accurate analytics, meaningful automation, and trusted AI across every Salesforce Cloud.
How Salesforce Data 360 handles data processing
Data 360 organizes incoming data through a structured processing pipeline made up of several object types:
- Data Stream / Data Source Objects (DSOs): temporary ingestion points where data from connected systems, live streams or files enters Data Cloud.
- Data Lake Objects (DLOs): storage containers that preserve the data in its native schema, enabling traceability and further processing.
- Unstructured Data Lake Objects (UDLOs): can store non-tabular content such as documents or logs.
- Data Model Objects (DMOs): virtual views that align incoming data to Salesforce’s standardized Customer 360 Data Model for consistent access and reporting.
- External Data Lake Objects (EDLOs): metadata links that connect Data 360 to external data warehouses or lakes without duplicating data, enabling true zero-copy integration.
These layers allow Salesforce Data 360 to continuously ingest, unify, and activate data at massive scale, enabling near real-time engagement across the Salesforce ecosystem.
Identity resolution engine
The identity resolution engine in Salesforce Data 360 turns scattered data points into a single, reliable customer profile. Configurable match rules identify which records belong to the same person — using exact, fuzzy, normalized, or compound logic across connected systems.
When different sources hold conflicting information, reconciliation rules decide which value to trust, based on factors like source reliability, recency, or completeness. The result is a single, “golden record” that combines every known interaction and attribute for each customer.
This unified profile becomes the foundation for personalized engagement, analytics and AI-driven actions across the Salesforce ecosystem.
Key features of Salesforce Data 360
Here are some of the core features that make Salesforce Data 360 such a powerful platform for managing customer data:
Data connectivity and ingestion
Salesforce Data 360 gives teams flexibility in how they connect and ingest data. With more than 200 connectors, it links seamlessly to Salesforce Clouds, major databases, cloud storage platforms, and popular SaaS tools. Real-time streaming APIs capture customer interactions as they happen — from web and mobile apps — while batch processing efficiently handles large historical data sets from systems like Snowflake or BigQuery.
For complex enterprise architectures, MuleSoft supports enhanced integration patterns, moving data reliably between on-premise and cloud systems. Developers can also use the Ingestion API to design custom pipelines that connect proprietary or industry-specific sources. Whether you’re streaming live event data or syncing bulk records, Data 360 keeps every system aligned and every dataset up to date.
Data transformation and preparation
Once data is ingested, Salesforce Data 360 makes it easy to shape and prepare that data for analysis or activation. Data Prep gives admins a no-code way to design transformations, merges, and filters without touching SQL. Formula fields handle inline calculations and conditional logic, enriching data in real time.
Built-in preparation tools standardize formats, normalize values, and run quality checks before data is unified — cutting down on manual cleanup. For streaming data, transformations apply instantly as events flow through the system, keeping insights current and consistent across every channel. Complex data engineering becomes a clear, visual process that both technical and business teams can manage confidently.
Segmentation and audience building
Salesforce Data 360 includes an intuitive segment builder that lets marketers and analysts define audiences without writing code. With drag-and-drop controls, teams can build and layer segments for precise targeting across campaigns.
Waterfall segmentation determines which audience a customer belongs to when overlaps occur, ensuring each profile appears in the most relevant group. And with Einstein Segment Creation, teams can describe the audience they want in plain language — and let the platform build it automatically. The result is faster, smarter targeting and fewer manual hand-offs between data and marketing teams.
Insights and intelligence
Data 360 turns unified data into near real-time intelligence. Teams can define calculated insights — metrics like customer lifetime value, purchase frequency, or engagement scores — directly within the platform. Streaming insights refresh these metrics continuously, using rolling time windows to reflect what’s happening right now.
For deeper analysis, Data 360 connects to predictive models through Einstein Studio or external Machine Learning services, and enables fast retrieval of insights for operational use. These capabilities power instant recommendations, personalization, and reporting across Salesforce.
Zero copy
One of Data 360’s most forward-thinking features is its zero-copy architecture, which changes how data is shared across systems. Rather than duplicating or migrating data, Data 360 enables direct access and querying of external data platforms so you can activate insights without moving every dataset.
This bidirectional integration means you can use your existing data investments without building redundant Extract, Transform, Load (ETL) pipelines. Minimizing duplication and keeping data current, governed, and cost-efficient.
Data governance tools built into Salesforce Data 360
Salesforce Data 360 includes a rich suite of built-in governance, privacy, and security tools — let’s unpack them.
Data protection capabilities
Data Spaces let organizations with multiple brands, regions, or business units manage everything in one place — without losing control. Teams can work independently while sharing the same underlying data architecture. Access is managed through a layered security model across objects, fields, and records, giving admins fine-grained control over who can see or edit specific data.
Data 360’s AI-powered data classification automatically identifies and tags sensitive fields, helping teams stay on top of compliance and keep regulated information in check.
Dynamic data masking can hide or obfuscate sensitive values based on user roles and policies. For data at rest, Shield Platform Encryption uses tenant-specific root keys, while Data 360’s Bring Your Own Key (BYOK) and External Key Management (EKM) options — including AWS KMS — give enterprises even greater control over encryption keys without having to maintain them in Salesforce.
Compliance and privacy
Salesforce Data 360 includes built-in tools to help organizations stay compliant with global privacy standards like GDPR and CCPA. Its consent management framework captures and enforces customer preferences around communication and data sharing across all of your connected systems.
To make meeting regulatory requirements easier, Data 360 automates key data-subject-rights workflows — including requests for access, deletion, export, and processing restrictions — all handled through the Salesforce Consent API.
Security and compliance teams can gain deeper visibility with Event Monitoring and related add-ons, which track user activity, flag anomalies, and generate audit reports.
For secure data exchange, Private Connect enables encrypted, point-to-point communication between Salesforce Data 360 and external data platforms without crossing the public internet. And with policy-based governance automation — generally available from September 2025 — admins can define and enforce consistent data-handling rules across all connected sources. Together, these features create a flexible governance framework built to scale securely and stay ahead of evolving compliance needs.
Real-world use cases of Salesforce Data 360
Salesforce Data 360 helps organizations turn disconnected systems into customer-ready insight. By bringing all data together, teams can act on information instantly — improving efficiency, personalization, and the customer experience, regardless of the industry they’re in.
Retail: Brands combine online browsing, in-store transactions, loyalty activity, and social engagement into one profile. When a shopper abandons a cart or posts feedback, automated Flows trigger timely responses — from personalized offers to VIP outreach — driving conversion and long-term loyalty.
Financial services: Banks and insurers unify data from accounts, investments, mortgages, and digital channels to identify buying signals. Insights trigger actions such as automated loan pre-approvals or proactive financial recommendations, improving satisfaction and cross-sell performance.
Healthcare: Providers connect Electronic Health Records (EHRs), patient portals, wearable data, and social insights in Salesforce Data 360 to build a single, trusted patient view. Near real-time updates power automated reminders and personalized care journeys — helping teams deliver better outcomes, reduce missed appointments, and strengthen patient engagement.
Manufacturing: Data 360 unifies IoT sensor readings, service contracts, and inventory systems to predict equipment issues before they happen. These predictive insights minimize downtime, shorten delivery cycles, and strengthen customer trust — all powered by data intelligence.
Powering Agentforce with Data 360
Data 360 provides the unified data foundation that helps Agentforce agents generate accurate, context-aware responses — turning AI from a generic assistant into an intelligent, trustworthy business partner.
Agentforce uses Salesforce Data 360 to access a unified customer context that spans sales, service, marketing, commerce, and even external data platforms through zero-copy federation. This feature allows near real-time queries across Snowflake, Databricks, BigQuery, and Redshift using advanced query pushdown techniques, so data can be accessed live without duplication or latency.
The foundation for intelligent AI
Salesforce Data 360 serves as the grounding layer that gives Agentforce factual, contextually accurate information. By providing a unified, customer view, Data 360 helps Agentforce reduce AI hallucinations and generate responses that align with actual customer history and business data. Without Data 360, Agentforce still functions using existing Salesforce CRM data and connected sources, but its ability to leverage a complete, multi-source context is limited.
Trust and security for AI
Trust and security remain central to Salesforce’s AI design. As of October 2025, zero data retention policies ensure that customer data used by Agentforce’s large language models is never stored or reused for model training. Permission enforcement ensures that Agentforce only accesses data authorized by Salesforce’s field-level security and role-based access controls.
Data masking is now disabled for Agentforce, this change was made to preserve contextual accuracy in planner and action workflows. Salesforce mitigates this by expanding its partnership with Anthropic: all Claude-based models now operate fully within Salesforce’s virtual private cloud, ensuring that even unmasked contextual data never leaves the trusted environment.
Every interaction with Agentforce is fully audited — including prompts, responses, toxicity scores, and user feedback — and stored securely in Data 360 for governance and compliance visibility.
How Gearset supports Salesforce Data 360
Salesforce Data 360 introduces an entirely new layer of complexity to CRM management — bringing in dozens of new metadata types, JSON-based configurations, and dynamic, near real-time components that evolve faster than most traditional Salesforce features. For DevOps teams, this makes deployment, version control, and governance more challenging than ever. Gearset bridges this gap, giving teams the tools they need to deploy, manage, and secure Data 360 configurations with confidence.
The deployment challenge
Unlike traditional Salesforce environments, Salesforce Data 360 adds more than 30 new metadata types, covering everything from data streams and model objects to segmentation rules and activation targets. These configurations are often defined in JSON, meaning even small syntax errors can block deployments or cause production failures.
Because metadata-only sandboxes can’t preview real data, teams often struggle to test identity resolution logic or segmentation behavior before pushing changes live. And with Data 360’s rapid release cadence, definitions for identity resolution rules, calculated insights, and data streams evolve frequently, making manual tracking difficult.
The stakes are high: a single misconfigured deployment could inadvertently deactivate live data flows or exclude thousands of customers from active journeys. As Data 360 becomes a critical driver of personalization and AI, ensuring that deployments are correct, consistent, and recoverable has become a top operational priority.
Gearset’s Data 360 capabilities
Gearset provides the most comprehensive DevOps support available today for Salesforce Data 360. It handles every Data 360 component through Salesforce’s Metadata API, including Data Streams, Identity Resolution Rules, Calculated Insights, Segments, Activation Targets, and more.
With Data Kit management, Gearset simplifies complex packaging requirements for bundled components such as Data Lake Objects (DLOs), Calculated Insights, and Search Indexes — ensuring that interdependent assets deploy cleanly and in the right order.
To prevent costly deployment failures, Gearset includes built-in JSON linting and validation, catching syntax or configuration issues before they ever reach production. Every change is automatically tracked through version control, so teams can monitor how Data 360 configurations evolve over time — even for metadata types not fully supported by other tools.
Gearset’s complete CI/CD pipeline solution makes it easy to automate deployments across environments, while rollback capabilities let teams revert changes instantly if something goes wrong. These features bring automation, safety, and visibility to a fast-moving platform that demands precision at scale.
The business case for Gearset
For organizations deploying Salesforce 360, Gearset delivers tangible business value. Deployment time drops dramatically — what once took hours of manual packaging and troubleshooting can now be done in minutes through automated validation and intelligent dependency management.
Deployment previews let teams see exactly what’s changing before it goes live, reducing risk and preventing errors that could affect live customer data or revenue-generating segments. Version control integration supports collaborative development, allowing multiple team members to work on Data 360 configurations simultaneously without overwriting each other’s changes.
Gearset also strengthens compliance and governance with full audit trails, documenting every modification, deployment, and rollback — essential for regulated industries. Because Gearset continuously updates its platform to align with Salesforce’s latest releases, it remains future-proof, ensuring ongoing compatibility as Salesforce expands capabilities.
With Gearset, teams can manage the complexity of Data 360 confidently — deploying faster, minimizing risk, and maintaining the trust and agility that enterprise-scale Salesforce DevOps demands.
Take the next step
Salesforce Data 360 is transforming how enterprises manage, unify, and activate customer data — but with its innovation comes new deployment complexity. Gearset gives DevOps teams the confidence and control they need to manage Data 360 configurations smoothly alongside their existing Salesforce operations.
Don’t let deployment hurdles slow your momentum. Join the thousands of Salesforce teams already using Gearset to deploy changes faster, safer, and more reliably — and unlock the full potential of Salesforce Data 360 with every release.
Start your 30-day free trial today to experience Gearset’s full Data 360 deployment capabilities with no commitment.
Or book a demo to see a personalized walkthrough showing how Gearset handles your specific Data 360 challenges.
