Tableau Semantics

Use Tableau Semantics to build semantic models for the Data 360 semantic layer and for Tableau Next analytics assets. Semantic models are where you define, govern, and query all your business metrics.

Tableau Semantics operates on top of Data 360 data sources, and powers all consumption experiences that source data from Data 360. This includes Salesforce applications, such as Data 360 Reports, Tableau Next, Intelligent apps, and AI. Tableau Semantics is an integral part of Data 360. It’s applied on top of all the data sources you connect or ingest into Data 360 and serves as a single source of truth for all your consumption layers.

Use Tableau Semantics to create semantic models and semantic definitions to input your data into Data 360 to serve as a single source of truth within Salesforce. Semantic models provide a consistent way to interpret data across Salesforce by mapping your data to familiar business terms and standardized business logic. Tableau Semantics also uses a Query Generator to leverage the data to answer key business questions by sending customizable semantic queries based on your semantic models.

You can create semantic models in Data 360 by using the Semantic Layer tab. If you're a Tableau Next customer, you can also create and manage semantic models in Tableau Next. For more information, see Create Semantic Models and Define Metrics.

You can also use the Tableau Semantics connector for Tableau Desktop and Cloud to connect and use semantic models in Data 360 just like you can in Tableau Next. For more information, see Salesforce Help: Tableau Semantics Connector.

• About Tableau Semantics
  With Tableau Semantics, you can create and manage your semantic models. Before you start using Tableau Semantics, review requirements and other introductory information.
• Create Semantic Models
  Semantic Models are first—class Salesforce metadata, integrated throughout Data 360, Tableau Next, and intelligent apps to power analytical and data-driven experiences. The semantic model includes definitions tailored for a specific analytical use case. The model can have one or more data objects. You can create flexible joins and unions across objects and enrich your analysis with calculated dimensions and measures.
• Structure Data within a Semantic Model
  With Tableau Semantics, you can use the Semantic Model Builder in Data 360 or Tableau Next to build and make changes to your semantic model.
• Semantic Definitions in Tableau Semantics
  In Tableau Semantics, you can create semantic definitions that are composable and reusable across semantic models, such as calculated fields, parameters, groups, bins, and more.
• Metrics in Tableau Semantics
  A metric is a KPI tracked over time, such as revenue over the past year. The core components of a metric are a measure or calculated field representing the KPI and a time dimension that tracks its progression over time.
• Predictive Insights in Tableau Semantics (Beta)
  Predictive Insights adds real-time machine learning predictions to your semantic model as a field you can chart, filter, and reuse. Connect any AI model from Data 360, map your semantic model fields to its inputs, and get a predicted value for every record—no data exports, separate tools, or code needed.
• Validate Semantic Models and Prepare for AI in Tableau Semantics
  Test, validate, and optimize semantic models for best performance and to prepare for collaboration with AI agents.
• Manage Access to Semantic Models in Tableau Semantics
  Control who can view or edit your semantic models by sharing them with specific users or your entire org. Semantic models are private by default, but you can manage access through the Share option and assign access roles, such as Viewer or Editor.
• The C360 Semantic Model
  Use the C360 Semantic Data Model (SDM) to get unified and trusted insights when you work across multiple Salesforce products to create cross-cloud KPIs.
• Develop with Tableau Semantics
  Build, edit, test, and deploy Tableau semantic models as code, directly in VS Code.