What you should know about Salesforce Data 360

For a moment Data 360 looked like it took a back seat in the Salesforce portfolio. Among the AI hype and Agentforce it was easy to forget about the data that fuels our algorithms. Salesforce didn’t forget – in 2026 Data 360 became the context layer for every agent, so this is a good moment to look at what it actually is… and what it is not.

What is Data 360

There are a lot of definitions of Data 360. Due to the flexibility of this tool, it can be defined differently depending on how you use it. First let’s look at the official Salesforce definition, and later in the article I will try to categorize this tool in a more scientific way.

Data 360 definition

The official definition changed together with the product. In the Data Cloud era, Salesforce described the core feature like this:

Data Cloud is a data platform that unifies all of your company’s data on Salesforce Platform.

But why should we unify this data? The answer was in the next part of the sentence:

Data Cloud is a data platform that unifies all of your company’s data on Salesforce Platform, giving every team a 360-degree view of the customer to drive automation and analytics, personalize engagement, and power trusted AI.

Today the Data 360 page calls it an activation engine native to Salesforce, built to activate trusted data everywhere – without moving it, thanks to Zero Copy. The accent moved from collecting the data to using it.

Salesforce still promotes the “360-degree view of the customer”, but Data 360 does it quite differently than other systems.

Key Ring

Data 360 starts with a different point of view in terms of storing and connecting the data. It does not aim for a golden record or golden ID. Instead, it uses the Primary Keys from all data sources and stores them in the “Key Ring”, and this Key Ring is the result of the unification process.

The Key Ring is just an additional data layer, based on references to other data sources. Instead of consolidating the data within a single ID and building the data model around it, Data 360 links the data and creates the unified profile as a data view. Then, following the specified rules (called Match and Reconciliation Rules), the system places a given portion of data (e.g. First Name, Email, Phone) in this data view. This process is called Identity Resolution in the system, or Unification in the “business” language.

A simple example: Anna exists in three systems. Marketing Cloud knows her email, Service Cloud knows her phone number, and your Snowflake warehouse knows her last 40 orders. Data 360 doesn’t merge these three records into one new record. It keeps all three, notes that they belong to the same person, and shows you one profile built from their pieces.

Why there is Data 360

So why did Salesforce create Data 360, instead of adding the features to the current CRM environment? Well, it was probably driven by several technical limitations, architectural constraints and the concept of the future look of the Salesforce ecosystem.

Technical limitations

Salesforce CRM operates on a monolithic, large-scale Java service with an Oracle database. 20 years of development brought some technical debt, and this architecture is not perfectly optimized for processing large amounts of data, especially when it comes to AI.

Data 360 is a microservice-based solution with a completely different tech stack, built for big volumes of data from day one. Completely new infrastructure allowed Salesforce to shift the burden of data integration and maintenance from CRM databases to Data 360, optimized for current business processes.

Data integration

As we well know, many Salesforce apps were acquired and rebranded. This resulted in a lot of isolated back-end architectures. What is more, enterprise customers want to connect the internal data stored in custom databases, cloud databases and third-party apps (or spreadsheets, lol).

This fragmentation made the integrations between Salesforce apps and third-party software a big challenge. Especially with enterprise-grade amounts of data and the Salesforce CRM architecture of that time.

Data 360 was created with these data integration challenges in mind. Salesforce provided a good architecture, a canonical data model and native connectors for Salesforce products and many third-party solutions, including cloud storage and popular apps.

Scalability

Current systems process terabytes of data – not only structured data, but also unstructured. Implementing AI and ML capabilities with scalability in mind within the old architecture would be very, very hard. Add changing requirements, evolving data models and unification rules, and the integration and maintenance becomes potentially very costly.

Data 360 provides this scalability. In Q2 of fiscal 2027 (May–July 2026) it ingested 104 trillion records, 355% more than a year before. 82 trillion of them came through Zero Copy, and the platform processed 22 TB of unstructured data in the same quarter. Migration from Amazon EC2 to Kubernetes provided even better scalability.

Data 360 Functionalities

There are a lot of materials about Data 360 functionalities, so let’s make this as simple as possible. I will categorize the functionalities in 4 sections:

1. Ingest – getting the data from different sources
2. Harmonize – cleaning and mapping the data
3. Unify – unifying the data into one customer profile
4. Act – using the data

But let’s be honest here – it is a huge simplification. Data 360 is a big tool, new features are released monthly, so the functionalities naturally evolve.

Ingest

Ingestion is the process of getting the data from different sources into Data 360. We store the ingested data in Data Lake Objects (DLOs) – tables that keep the data in the shape it came from the source.

There are 3 types of ingestion:

• Batch Ingestion – standard data copy in batches. The basic way to ingest the data with pre-built connectors in time intervals (usually from 1 hour to 24 hours). Since March 2026 batch ingestion is free.
• Streaming Ingestion – low-latency data copy. A faster way to ingest the data with API calls or from CRM apps.
• Data Federation (Zero Copy) – no-copy connection to the data source. Data 360 queries the necessary data without copying it to its own storage. Currently it supports Snowflake, Databricks, Google BigQuery and Amazon Redshift, plus Apache Iceberg catalogs (for example AWS Glue Data Catalog). Microsoft Fabric OneLake is in beta.

There are also two types of data that can be ingested:

• Structured Data – standard data type that can be stored in a relational database. Data with a structure typical for tables, API queries, CSV or Parquet files.
• Unstructured Data – data that doesn’t have a specific format and structure: PDFs, text, HTML, audio and video files.

Unstructured Data ingestion is still a quite fresh functionality in Data 360. You can ingest it from blob storages such as Azure, Amazon or Google, or manually upload the files to the Einstein Data Library.

Moving further, there are several possible data connection types, including:

• Manual CSV import – ad hoc data ingestion from a CSV file. Perfect option for an MVP, demo preparation or quick addition of data from spreadsheets.
• SFTP – encrypting and transferring CSV files from an SFTP server to Data 360 using SSH.
• Ingestion API – REST API, supporting both batch and streaming ingestion types. The schema is based on the OpenAPI format.
• Website and Mobile SDK – another API connection that allows you to collect behavioral data from a selected website or mobile app with the prepared sitemap.
• No-Code connectors – pre-built connectors maintained by Salesforce. You can check current connectors availability here.
• MuleSoft – brings additional third-party integration connectors included in the MuleSoft ecosystem.

Keep in mind that still a lot of connectors are in the beta stage. It means that you can test them or even use them in demo/MVP projects, but I don’t recommend using them in the production environment.

Harmonize

Harmonization is the process of cleaning and mapping the data in Data 360. We store the harmonized data in Data Model Objects (DMOs). Data Model Objects are not physical objects like Data Lake Objects, just virtual data views that can be remapped.

Back to Anna: Marketing Cloud stores her address in a field called EmailAddress, Service Cloud calls it Email, and your warehouse has customer_email. Harmonization maps all three to one field in one standard object, so the rest of Data 360 doesn’t care where the data came from.

There are several options and functionalities supporting the harmonization in Data 360:

• Formula Fields – allow us to create additional fields with calculations, specific text, concatenated keys and more information during the ingestion process.
• Data Transforms – batch or streaming, allowing you to transform the data with a UI or SQL code. Perfect for splitting, joining and flattening Data Lake Objects before mapping.
• Data Mapping – the process of mapping the data from Data Lake Objects to standard or custom Data Model Objects in the UI.
• Data Graphs – transform normalized table data from Data Model Objects into materialized JSON views of the data. Perfect for optimizing queries and the real-time capabilities of Data 360.
• Indexing and embedding – unstructured data is automatically mapped to Unstructured Data Model Objects, then indexed with vector embedding models to provide context for AI.

Data 360 has a Customer 360 canonical data model that can be easily modified and extended with custom objects. Some of the pre-built objects are necessary to map for segmentation purposes, like Individual/Account and Contact Points. If you connect the data from Salesforce products, you can use Data Bundles that automatically create Data Streams and Data Mappings. You can find more information about the Data 360 data model here.

Unify

Unification is the process of connecting data about individuals or accounts into a single customer or account view. This solution, called Identity Resolution, builds the Key Ring in Data 360 and prepares the data for better use in the CRM environment.

Because it is based on references instead of one key, you can easily create multiple Identity Resolution Rulesets, depending on your use cases.

During Ruleset creation, you need to specify:

• Match Rules – specify which profiles you want to unify. Each rule contains one or more criteria. You can use the default match rules or create your own, using exact matching or fuzzy matching (with levels of precision).
• Reconciliation Rules – specify which value from the list of possible records you want to use in the unified profile. You can use the last updated value, the most frequently occurring value, or set the source priority.

For Anna it could look like this: one Match Rule says “same email = same person”, a second one says “same last name + same phone number = same person”. Her Marketing Cloud record matches the warehouse record by email, the Service Cloud record matches by phone, so 3 source profiles become 1 unified profile. Then a Reconciliation Rule decides that the phone number from Service Cloud wins over the one from the warehouse, because Service Cloud has source priority.

TLDR – Match Rules decide which source profiles get merged, and Reconciliation Rules decide which values end up in the unified customer view.

Act

With ingested, harmonized and unified data, we can start acting. Acting means using the data in Data 360 and sending it to other systems.

You can:

• use the data in Salesforce Flows, which opens multiple automation opportunities in the Salesforce environment;
• enrich the Salesforce CRM views with Related Lists and Copy Fields which use data from Data 360;
• build and export Calculated Insights, custom data views built with SQL queries or the UI;
• build Segments based on unified customers in Data 360 (more in Segmentation in Salesforce Data 360 and Segment Canvas);
• Activate the segments to other systems (e.g. Marketing Cloud, advertising platforms, cloud storage);
• use Data Actions to send the data in near real time to Marketing Cloud, Salesforce Platform and other apps;
• analyze the data in Tableau and Tableau Next, which read Data 360 objects directly;
• ground Agentforce agents with profile data and unstructured content (more in the next section);
• build and run ML models with Model Builder;
• query the data with Query API, or run SQL on Data 360 directly from Apex;
• Share the data via zero-copy data sharing (back to Snowflake, Databricks, BigQuery), Python SDK or JDBC Driver;
• match your data with partners’ data in Data 360 Clean Rooms, without moving or exposing the raw records.

Data 360 and AI

This is the part that changed the most since the first version of this article. Agentforce agents (and any other AI) know everything about the world and nothing about your customers. If an agent answers Anna’s question about her order, it needs her orders, her open cases and maybe the returns policy PDF – and Data 360 is the place where all of this is connected.

In practice Data 360 gives AI three things:

• Structured context – unified profiles, Calculated Insights and Data Graphs that an agent can read in one call.
• Unstructured context – documents, transcripts and knowledge articles that are chunked, embedded and searchable with retrievers (Salesforce calls this layer Intelligent Context).
• Governance – the agent sees only what the user or the agent is allowed to see.

At Dreamforce 2026 Salesforce went one step further and opened Data 360 to agents outside Salesforce. Claude, ChatGPT or Slackbot can use the same customer context, with Salesforce permissions still applied.

Customer Reference Data Platform

Moving to the scientific way of defining Data 360, we should look at the history of this tool. It started in 2020 as Customer 360 Audiences, a standard CDP-like tool with basic functionalities for segmenting and activating customers to the Marketing Cloud platform. In 2021 the tool became Salesforce CDP, and in 2022 the name changed to Marketing Cloud Customer Data Platform.

Then we saw a switch from a marketer-centered approach to empowering the other Salesforce products, like Sales and Service. With the next name changes to Genie, then to Data Cloud and, in October 2025, to Data 360, the tool evolved rapidly, offering a not-so-common approach to data unification, which we already covered. From the history of the product, we can clearly see that Data 360 is something more than a Marketing Data Warehouse, and it is also something other than a Data Management Platform.

So can we say that Data 360 is a Customer Data Platform, as it was called by Salesforce in the past?

Well, theoretically, yes… but no… but it is complicated. 🤔

Salesforce promotes Data 360 as something “more than just a traditional CDP”. Timo Kovala defined Data 360 as an “account-based CDP”, highlighting the possibility to unify not only customer but also account data.

Here comes the Key Ring again. Typical CDPs usually offer unification within a single Primary Key, Golden Key, or similar. Data 360 works a little bit differently, based on references instead of strict keys and relationships, which provides a more elastic approach for matching users and organizing data hierarchies from different sources. What is more, Zero Copy (the old BYOL – Bring Your Own Lake) shows the reference-based approach even more – today most of the records Data 360 works with never land in its storage.

On the other side, Data 360 is still highly based on customers or accounts. For a long time there were no native options to export the data without the user or account context (apart from the API). This is slowly changing – zero-copy data sharing and full DMO exports (like the one for Meta Ads) go in this direction – but segmentation and activation still revolve around profiles.

That’s why I define Data 360 as a Customer Reference Data Platform. This name highlights the reference-focused approach and still refers to the CDP. And the AI shift from 2026 fits it even better – agents don’t need a golden record, they need references to the right context.

What Data 360 is not

Data 360 is often compared to tools that solve different problems. This is the part where many projects go wrong, so let’s make it clear:

Data 360 does not replace your warehouse – with Zero Copy it works on top of it. It is not an MDM either, and Salesforce knows that: it closed the Informatica acquisition in November 2025 and now positions Data 360 (context), Informatica (data quality and governance) and Tableau (semantics) as separate layers. If you already own MDM or data quality tooling, check what you would be paying for twice.

When Data 360 makes sense

Data 360 is a good fit when:

• your customer data lives in several systems (Salesforce clouds, e-commerce, warehouse, apps) and you need one profile in Salesforce
• you want to use Agentforce with more context than a single CRM object
• you run Marketing Cloud and need segmentation on data that doesn’t fit into Data Extensions
• you already have a warehouse and want to use its data in Salesforce without building another pipeline

It is probably not the right tool (or not yet) when:

• all your data is already in one Salesforce org and standard reports cover your needs
• you look for an analytics warehouse or an enterprise MDM
• nobody in your organization will own credit consumption after go-live. Seriously, this one hurts

Data 360 pricing

Data 360 comes with multiple licenses, depending on your requirements. There are also some add-ons that provide more functionalities or extend the license limits.

The most common licenses are:

• Data 360 Provisioning – free Data 360 tier for customers that have Salesforce Enterprise or Unlimited edition, with a limited amount of storage and credits. Data 360 is also included in Salesforce Foundations.
• Data 360 Starter – basic license with the configuration depending on the specific license name (e.g. Starter for Marketing or for Tableau).
• Marketing Cloud Growth and Advanced – Marketing Cloud on Core licenses, containing a Data 360 license with fewer consumption credits than Data 360 Starter.

Since March 2, 2026 you can choose between three pricing models:

Profiles make sense when your use case is mostly “build a profile and use it in journeys or service”, because the expensive unification is un-metered. Flex Credits scale better for everything else (and for AI use cases you will need them anyway).

The most important Data 360 add-ons are:

• Data Spaces ($60,000/year/space): creates internal data spaces for e.g. different brands
• Ad Audiences ($2,400/year/audience): activates segments to advertising platforms like Meta, Amazon Ads and Google Ads
• Data Storage ($1,800/year/TB): provides additional storage beyond the basic 1TB or 5TB limit

Like many other enterprise-class solutions, Data 360 pricing details are not widely available, so for detailed pricing and bundles you should contact your Salesforce Account Executive. There are many possible licenses, including Salesforce Foundations, specific Suites, Agentforce editions and others.

Data 360 billing

Data 360 was the first product in the Salesforce ecosystem that switched from lump-sum billing to usage billing. You pay for the license that provides the tool and for so-called consumption credits. You use the consumption credits to perform operations in Data 360, and you can buy more credits if you need them.

And here’s the catch – you have to calculate the potential costs. Depending on your implementation, every functionality has its own usage multiplier:

credits = (rows processed / 1,000,000) × multiplier

Some examples from the Flex Credits rate card (base tier, production, version from April 21, 2026):

Let’s calculate one unification run for Anna and her 10M friends. 10M rows × 75,000 = 750,000 credits, which is about $3,750 at $500 per 100k Flex Credits. Once. If the ruleset runs every night, you pay it 30 times a month (a bit less thanks to volume tiers, but still). That’s why the refresh schedule of Identity Resolution is usually the first thing I check when a client’s credits disappear.

I recommend reading my Salesforce Data 360 Credits Guide, which is the complete article about billing and credits in Data 360. To see where the credits actually go in your org, check How to use Digital Wallet in Salesforce Data 360.

For estimations you can use the official Data 360 Pricing Calculator or the Data Cloud Credit Calculator made by Isaac Shaffer. Just make sure the calculator uses the pricing model you are actually on.

FAQ

Is Data 360 the same as Data Cloud?

Yes. Salesforce renamed Data Cloud to Data 360 on October 14, 2025. The license, the data model and the features stayed the same, and existing implementations didn’t need any changes.

Do I need Data 360 to use Agentforce?

Not for simple agents working on CRM records only. But when an agent needs data from other systems, unified profiles or unstructured content (PDFs, transcripts), Data 360 is the layer that provides it.

Is Data 360 free?

Partially. Salesforce Enterprise and Unlimited customers can provision Data 360 for free, with a limited amount of storage and credits. For production use cases you will need a paid license or additional credits.

Does Data 360 copy all my data?

No. You can ingest data (copy it into Data 360) or federate it with Zero Copy from Snowflake, Databricks, BigQuery, Redshift or Iceberg-based lakes. In 2026 most of the records Data 360 processes come through Zero Copy.

What is the most expensive operation in Data 360?

Among regular operations, unification (Identity Resolution) – 75,000 credits per 1M rows at base tier. The real-time pipeline is also expensive, but it is used only for sub-second use cases. Check Data Cloud Credits Guide for more details.

Summary

• Data 360 (formerly Data Cloud) connects data from many sources, unifies it into customer and account profiles and makes it usable across Salesforce and AI agents.
• It works on references (the Key Ring), not on a golden record – that’s why I call it a Customer Reference Data Platform.
• It is not a warehouse and not an MDM – it works next to them.
• Since March 2026 you can pay with Flex Credits or per profile, ingestion is free, and unification is still the operation to watch.

If you have questions or see something that already changed (it happens every month with Data 360), let me know on LinkedIn.

Sources:

• Data 360 Pricing | Salesforce
• Flex Credits Rate Card (April 21, 2026)
• Salesforce Q2 FY27 Results
• Data 360 Zero Copy | Salesforce
• Salesforce Completes Acquisition of Informatica
• Source of the diagram images: A Visual Guide to Salesforce Data Cloud Capabilities | Salesforce Developers Blog