Reference Analytical Assets Across Tableau Next Workspaces
Learning Objectives
After completing this unit, you’ll be able to:
- Explain the concept of workspaces in Tableau Next and their benefits for efficient analytics.
- Describe how to reference assets across different workspaces.
- List use cases for extending an existing semantic model.
Before You Start
Before you start this module, consider completing this recommended content:
Discover the Workspace Difference
Workspaces in Tableau Next are collaborative environments where teams can access the tools and assets they need to answer business questions. A workspace defines a specific unit of work for you as an analyst. It can range from answering an ad-hoc question all the way to the gathering of all assets needed to build Enterprise-level dashboards or any other content. As part of the workspace, you get instant access to everything they need for data analysis, including connecting to data, preparing and modeling data, and creating visualizations and dashboards.
Manage Workspace Permissions and Access Control
As a workspace owner, you manage access and permissions for your respective spaces. You control access to:
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The workspace as a whole: This determines who can view or edit the workspace and all of its contents. An asset can be edited only if the user has edit rights on its originating workspace.
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Individual assets within the workspace: This allows for granular view control over an asset in the workspace, without granting access to the entire workspace.
A workspace is where content is authored in Tableau Next. By sharing the workspace itself, workspace owners provide the full context of how an asset was built. This level of transparency gives your team more confidence in the data, as they can see exactly how you gathered and organized the information
As a workspace author, you can choose to share individual assets of a workspace in view mode only. This granular control offers distinct advantages.
- Fellow authors can use your shared assets as reliable building blocks within their own separate workspaces.
- Business users get a clean, focused experience that allows them to view and interact with the content without needing to navigate the underlying workspace structure or the complexities of how assets were created.
Explore Workspace Types
Imagine you’re part of Main Stage Analytics, a fictional company using two types of workspaces with distinct purposes to make data analysis more efficient.
The Governed, Long-Term Workspace
In this scenario, you’re a member of the business intelligence (BI) team. You use a persistent workspace for asset management and governance to ensure official financial models remain a single source of truth across your company.
As the owner of this workspace, you’re responsible for managing access to the content within it that you’ve authored. For instance, if you want to allow someone to edit an asset, you must share the entire workspace with them. Or you can simply share view-only access to individual assets as needed.
The Ad-Hoc Investigative Workspace
Now imagine you’re a data analyst who needs to address an immediate, temporary request from a sales manager. You create a separate workspace designed solely to gather the context needed to investigate recent sales trends.
To accomplish this quickly, you populate your investigative workspace by pulling in several existing assets.
- A view-only reference to a validated sales data model from a governed workspace
- References to relevant dashboards owned by the sales team
- A marketing visualization pulled from the marketing team’s workspace
By assembling these assets in one central location, you rapidly gain the understanding needed to share actionable insights with the sales team. When you finish the investigation, you can archive or dissolve this specific workspace, leaving the foundational, governed assets completely untouched.
Organize Assets in a Workspace
Workspaces are designed to make analysts more efficient. They are the places to create analytical assets or to gather useful assets from other places that give a team a holistic view of key business data. These assets include:
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Data objects in Data 360: Data model objects (DMOs), data lake objects (DLOs), and calculated insight objects (CIOs) in Data 360.
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Data connections: You can connect to external data sources (such as Snowflake, Databricks, AWS, and more) using a dedicated connector. You can also upload .csv and Excel (.xlxs) files and the connected data is saved as DLOs in Data 360. Workspaces hold connection information to the data, but changes made in Tableau Next don’t affect the source data.
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Semantic models: These form the semantic layer, containing a data model and business definitions (such as field names, aggregations, and calculations). They add meaning and context to data and are built using the Semantic Model Builder in Tableau Next or Data 360.
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Metrics: A key performance indicator (KPI) tracked over time. You can create metrics in semantic models.
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Visualizations (vizzes): Visual representations of data, such as charts or graphs, built from DMOs or semantic models.
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Dashboards: Consolidated views of critical information, composed of widgets that can include metrics and visualizations.
Reference Assets from Other Workspaces
Many companies are drowning in data yet starving for information. To make data-driven decisions, analysts must transform data into actionable knowledge, which takes effort and expertise. But your effort won’t help others if they don’t know about your analysis, or worse, can’t find it. This can lead to multiple teams duplicating efforts without any real single source of truth.
The asset referencing capabilities in Tableau Next directly address these concerns. A workspace makes it easy to add existing assets from another Tableau Next workspace or from Data 360.
Here’s an example. Let’s say you create a visualization with Sales KPIs inside a Main Stage Analytics workspace. To build that visualization, you created a new semantic model by adding Sales context to data objects from Data 360. The Main Stage marketing team wants to include your Sales KPI viz in a dashboard, but don’t have access to your Sales semantic model.
By selecting Add | Visualization, the marketing team can add a reference to your Sales KPI visualization in their workspace and use it in their dashboard. You still keep control over who can access or make changes to your Sales KPI visualization, but changes you make to the visualization also update any reference to your viz.
Add Specific Context in an Extended Semantic Model
Tableau Next also makes it possible to extend an existing semantic model in another workspace. This gives users the flexibility to customize a semantic model with specific context, even if they don’t have edit rights on the base model.
You already added Sales context to create a Sales semantic model, but an analyst in the marketing team wants to make some tweaks. While the base Sales semantic model contains core revenue metrics, the Marketing team analyst extends the model in another workspace and adds their proprietary campaign cost tables to calculate a Marketing ROI metric. The extension lets them build this marketing-specific calculation without altering the trusted base Sales model. These changes help the marketing team create relevant visualizations and dashboards for their team but leave your original Sales model as-is.
The extension stays connected to your base model to keep it up to date, so any changes you make to the base Sales model are automatically made in the extended Marketing model, too.
Reuse Assets from Production in Your Personal Org
In Tableau Next, analysis can happen in either your Personal Org or the production org. The production org is a governed space controlled and run by Tableau Next admins to maintain control and governance over data, but a Personal Org is a sandbox environment designed for you to do analysis and experimentation. You can reuse the trusted, admin-approved assets from production in your Personal Org for analysis.
For Main Stage Analytics, the business intelligence team manages the company’s Tableau Next production environment, which contains final and financial models for the company. Awanasa, a data scientist, can create a workspace owned by business intelligence where these models are defined and shared with data analysts across the Main Stage Analytics organization.
You can reuse the trusted, admin-approved data and analysis from the production org in one or more workspaces inside a Personal Org. You don’t need to rebuild or second-guess core data, and have the autonomy to work independently without affecting the production environment.
This structure gives analysts the best of both worlds: Access to a reliable, trusted “source of truth” (the production org) and a safe, flexible “sandbox” (the Personal Org) to work in autonomously. Extending the reused semantic model ensures that analysts benefit from a self-service experience rooted in that trusted source.
Drive Better Decisions with Discoverable Insights
Referencing and reusing analytical assets in Tableau Next offers several key advantages that enhance data consistency, business productivity, and the overall quality of insights across your organization.
Avoid Duplication
By leveraging existing data connections, semantic models, visualizations, and dashboards from other projects, you can significantly reduce the time and effort spent recreating assets from scratch. For instance, data modeled in a semantic data model is readily available for analysis, eliminating the need to set up a data model for each new visualization or metric.
Enhance Consistency and Trust in Data
Tableau Next’s semantic layer serves as a centralized, governed layer for data and metrics, ensuring that all teams and tools operate with the same trusted definitions. This consistency is crucial for reducing reporting discrepancies and building trust in the data. Updates to source assets are automatically reflected in all instances where they are referenced or extended, maintaining consistency and requiring no manual effort.
Improve Collaboration and Scalability
Workspaces in Tableau Next help analysts work better together. Analysts can invite colleagues to their projects and even split the work between multiple workspaces. The reconciliation happens through sharing the relevant assets between workspaces. This approach lets teams collaborate smoothly and makes sure everyone is on the same page with the most up-to-date data.
Now that you can see the benefits of referencing and extending assets in Tableau Next workspaces, the next unit covers some best practices for managing them most effectively.
Resources
- Salesforce Help: Release Note: Extend a Semantic Model from the Tableau Next Workspace
- Salesforce Help: Create a Semantic Model Based on an Existing Model
- Salesforce Help: Create or Add an Asset in a Workspace