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The Salesforce Data 360 Glossary: Key Terms and Features
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Published on
June 12, 2024
At a Glance
This glossary provides a comprehensive overview of relevant terms and features of Salesforce Data 360 (formerly Salesforce Data Cloud). Key components such as “Connect,” “Harmonize,” “Engage,” and “Experience” are explained, along with core concepts like Customer Data Platform, Calculated Insights, and Identity Resolution. We outline the capabilities for data analysis and automation, including the use of artificial intelligence in Data 360.
What Is Data 360?
Salesforce Data 360 is a real-time data platform that helps companies unify customer data from various sources to enable valuable insights and personalized experiences. By integrating data from different Salesforce applications as well as other systems, software, and devices, companies can better understand and target their customers. With the help of Artificial Intelligence (AI) and automation, Data 360 can analyze data in real time and execute actions based on it.
Data Cloud Becomes Data 360
Until autumn 2025, Data 360 was known as Salesforce Data Cloud. With the renaming, Salesforce placed a stronger emphasis on the strategic role of data within the platform. From a functional perspective, the solution remained unchanged: it consolidates data from various sources in a central customer data platform, standardizes it, and makes it usable for applications across sales, service, and marketing. The change therefore primarily affects positioning: data is not only captured and stored—it forms the foundation for AI, automations, and AI agents.
How Data 360 Works
Simply explained: Imagine customer information is like puzzle pieces scattered around your house. Data 360 is like a smart assistant that gathers these pieces, puts them together, and presents you with the complete picture.
Key Components of Data 360
Data 360 consists of four main components: Connect, Harmonize, Engage and Experience. In the following section, we explain the components.
Connect: How to connect your data
Salesforce Data 360 provides connectors for seamless integration of internal Salesforce applications and external sources. The following integrations are possible:
- Salesforce applications: Use connectors for Marketing Cloud, Sales Cloud and Service Cloud to integrate data from your existing Salesforce applications.
- External sources: Thanks to MuleSoft connectors, you can integrate data from external sources such as AWS, Google Cloud, and Azure. For example, you can import customer data from an Amazon S3 bucket or integrate transaction data from Google Cloud Storage.
- Real-time streams: Integrate data from real-time streams, such as web interactions or IoT devices. Use these connections to transfer real-time data from platforms like Apache Kafka or AWS Kinesis into Data 360.
- Mobile and web apps: With software development kits (SDKs) for mobile and web applications, you can ingest user data directly into Data 360. This enables you to capture and process data from mobile devices and web browsers in real time.
Use cases: How you can link your data in Data 360
- E-commerce platform: Online retailers can store sales data from their e-commerce platform in Amazon S3 and then integrate this data into Data 360 to build comprehensive customer profiles and develop personalized marketing campaigns.
- Marketing campaigns: Use data from Marketing Cloud to analyze customer interactions and campaign performance. This data can be updated in real time to optimize ongoing campaigns immediately.
- IoT Devices: A smart home device manufacturer can, for example, collect sensor data from its devices and integrate these data streams into Data 360. This enables real-time insights into device usage patterns, leading to improved product features and better customer service.
Harmonize: How to Standardize Your Data
Automatically harmonize all integrated data into a single customer profile. Salesforce Data 360 collects data from multiple sources, cleanses and consolidates it to create a complete and accurate view of every customer. For example, customer data from Sales Cloud (orders), Service Cloud (support inquiries), and Marketing Cloud (campaign interactions) is brought together. If a customer places an order in Sales Cloud, opens a support ticket in Service Cloud, and opens an email sent from Marketing Cloud, this information is unified into a single profile.
Engage: How to interact with your customers based on real-time data
Enable every department to work with unified customer profiles that adapt in real time to customer activity. With the Engage component, marketing, sales, and service teams can access up-to-date customer information and respond immediately. For example, a sales team can send a special offer to customers who have just viewed a product on the website, or a support team can offer immediate help if a customer repeatedly gets stuck on the same page.
Experience: How to Create Personalized Experiences for Your Customers
The Experience component of Salesforce Data 360 enables companies to create personalized experiences based on customers’ real-time data and activities. For example, e-commerce websites can dynamically adapt their user interface to display personalized product recommendations, or mobile apps can send offers and messages based on the user’s current location and preferences.
Key Terms
A Customer Data Platform (CDP) is software that serves as a central database in which all customer data from various sources is collected, integrated, and managed. A CDP brings together data from different channels and systems such as CRM, e-commerce, and apps to create a comprehensive customer profile. This consolidated data enables a unified view of the customer across various business processes.
A data lakehouse combines the advantages of a data lake and a data warehouse to store and manage both structured and unstructured data. It provides the flexibility of a data lake for storing large volumes of unstructured data, along with the structure and governance of a data warehouse for managing and analyzing that data. This enables organizations to generate insights from their data faster.
A data model is a structured representation of data that describes its organization, relationships, and rules. It includes Data Model Objects (DMOs), defined as groups of attributes that organize data from various sources. By storing data in a standardized way, integration and analysis are simplified.
The Data Model enables the collected data from various sources to be modelled and structured so it can be used efficiently for analysis, reporting, and activation purposes. For example, the Data Model can define how customer data from Salesforce is linked with demographic data from external sources to create targeted marketing segments.
For details on data modeling, please refer to the “Data Mapping” section.
Data Model Objects (DMOs) are a grouping of data created from data streams in Salesforce and other sources. DMOs can be standard or custom.
Segments are groups of objects, such as individuals in the B2C sector, that share a set of common characteristics.
An Application Programming Interface (API) is an interface that enables different applications to communicate with each other and exchange data. It defines, through rules and protocols, how software components interact.
In Data 360, the API is used to retrieve data from various external data sources and integrate it into Data 360. It enables specific operations such as querying data, retrieving metadata, or triggering events.
APIs play a central role in consolidating and standardizing data from various systems. For more details on using APIs, see the section “Connect, prepare, and model data”.
Calculated Insight (CI) is a tool in Data 360 that queries stored data, transforms it, and performs complex calculations.
Identity Resolution is the process of identity management by matching data about individuals to create a comprehensive view, referred to as a unified profile.
Dara Explorer is a tool in Data 360 that enables users to view data from a Data Model Object (DMO), Data Lake Object (DLO), or Computed Insight Object (CIO). The Data Explorer provides a user-friendly interface for browsing, analyzing, and validating data.
A DLO, in turn, is a container for raw data that has been imported from various sources and stored in its original format. This facilitates the analysis and understanding of large, complex data sets.
Example:
Imagine you have imported sales data from an e-commerce platform into a DLO. With the Data Explorer, you can browse this data to analyze sales figures for different products. You can quickly identify which products sell best and spot seasonal trends. You can also cleanse the data and prepare it for further analysis in Calculated Insights (CIOs) to obtain more detailed information.An activation is the process of publishing a segment to activation platforms (targets). Activation means that the data of a specific customer segment is sent to a platform such as Marketing Cloud, Google Ads, or Facebook Ads in order to run targeted marketing campaigns.
Example:
Suppose you have created a segment of customers who purchased a specific product in the last 30 days. The activation process sends this segment to the Marketing Cloud, where automated email campaigns are launched. At the same time, the segment is sent to Google Ads to display targeted ads for related products. This way, you can reach your customers across multiple channels with relevant offers.
Connect, Prepare and Model Data
To get started with Data 360, connect a data source either via a direct connection or by importing data. With a direct connection, also known as data federation, the data is used directly from the source in real time without having to copy it into Data 360. With data import, also known as data ingestion, the data is loaded into Data 360 in its original format and stored in a dedicated repository, a so-called Data Lake Object (DLO).
You can connect data sources such as Marketing Cloud, Amazon S3, and Google Storage to Data 360 via a dedicated connector. Select the connector for the data source you want to import data from, then specify the object or dataset you want to link (federate) or ingest. Data 360 retrieves a sample of your data and provides a recommended source schema for review.
A data stream uses a connector to establish a connection to a data source. It acts as a pipeline to connect data with Data 360. The received data is organized in a schema and stored as Data Lake Objects.
The data from all data streams is written to Data Lake Objects (DLOs). After you create your data streams, you must map your DLOs to the Data Model Object (DMO). Only mapped fields and objects with relationships can be used for segmentation and activation.
A Data Graph combines and transforms normalized tabular data from Data Model Objects (DMOs) into new, materialized views of your data. Because the data is precomputed, fewer queries are required and queries respond in near real time. Real-time data graphs are required to perform identity resolution, calculations, or segmentation in real time.
Insights and Personalization in Data 360
Use the tools and features of Salesforce Data 360 to gain deep insights into your data and create highly personalized experiences for your customers. With calculated insights, streaming insights, real-time insights, and the Visual Insight Builder, you can define comprehensive metrics and analyze data in real time.
Use Calculated Insights to define and calculate multidimensional metrics for your entire digital health in Data 360. You can create metrics at the profile, segment, and population levels.
Gain insights faster by building metrics on streaming data. Streaming Insights are queries that continuously refresh and are based on the latest data. This enables you to get real-time information about your customers’ engagement and respond quickly.
Use Real Time Insights to create more complex personalization logic for your website. These analytics enable you to track cumulative activity and determine whether a user’s activity meets defined thresholds.
This user-friendly tool enables you to create visual representations of your data without in-depth technical expertise. This allows you to run analyses quickly and efficiently and gain valuable insights to help you make informed decisions.
Analyze Data in Data 360
Use the powerful analytics tools in Salesforce Data 360 to analyze your unified and harmonized data. With Tableau, CRM Analytics, and Data 360 reports and dashboards, you can gain deep insights, create standard reports, and run targeted analyses that drive business success and enable personalized customer experiences.
Create standard reports on one or more related Data Model Objects (DMOs) or calculated insights to determine which business areas you should focus on.
Tableau, in combination with Data 360, offers extensive capabilities for data analysis and visualization. With Tableau, you can create interactive dashboards and visualize complex datasets in clear, easy-to-understand charts. The software enables real-time analytics and allows you to share reports and dashboards with your teams.
Data 360 enables you to perform exploratory analyses of your data at various stages of its lifecycle using CRM Analytics.
In contrast to Tableau, which specializes in visualizing data from a wide range of data sources and meeting complex requirements, CRM Analytics is geared toward Salesforce and CRM use cases.
Datorama, part of Salesforce Marketing Cloud, is an integrated marketing intelligence platform that enables companies to aggregate, analyze, and visualize data from various marketing channels. With Datorama, marketers can create comprehensive reports and dashboards to monitor and optimize campaign performance. It supports data source integration, data visualization, and advanced analytics.
Conclusion and Recommendations for Action on Data 360
Salesforce Data 360 enables companies to integrate, analyze, and leverage customer data from multiple sources. This extension of the Salesforce Core Platform enhances decision-making, delivers personalized experiences, and streamlines business processes.
To fully leverage the benefits of Salesforce Data 360, you should:
- Prepare data architecture and quality: First, review your data quality and establish a clear data architecture to enable the effective use of Data 360.
- Standardize data: Integrate all your enterprise data from various sources using connectors or interfaces. When developing interfaces or connecting systems via connectors, we recommend consulting an experienced Salesforce partner.
- Introduce automation: Automate processes across Service, Marketing, and Sales to benefit from real-time analytics. With automation and activation on the Salesforce Platform, your employees can work directly with the recommendations, eliminating manual steps.
These steps will help you maximize the potential of Data 360 and pursue a data-driven, customer-centric strategy. Do you have any further questions about the features of Data 360 or implementation? Get in touch. Our experts will be happy to advise you.
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