Oracle Analytics January 2026 Update  

The January 2026 update to Oracle Analytics introduces significant advancements in AI-powered analytics and semantic modeling. This update brings Oracle Analytics AI Agents, new capabilities for the Oracle Analytics AI Assistant to analyze clusters and outliers, and direct feedback options for AI-generated insights. Enhanced fine-grained permissions and support for Analytic Views in Oracle Analytics Semantic Modeler, along with improved management of data items in catalog folders, offer organizations greater flexibility, security, and control over their analytics environment. 

Featured highlights:

Data Visualization and Experience  

AI and Gen AI 

• Oracle Analytics AI Agents  

• Asking Oracle Analytics AI Assistant about clusters and outliers  

• AI Assistant feedback in Oracle Analytics  

Modeling, Preparation, and Connectivity  

Data Visualization and Experience  

Manage and organize data items in catalog folders  

The unified catalog experience introduces an organized way to manage not just workbooks and dashboards but also datasets, data flows, connections, sequences, and more within clearly structured catalog folders. This familiar, folder-based system helps users find, share, and govern both analytics and machine learning items more easily, with clear permission inheritance offering secure and consistent access control. For instance, a user can drag and drop their dataset into a shared folder, allowing team members to discover and collaborate on trusted data assets while respecting folder permissions. 

Add aliases to data items in the Oracle Analytics catalog  

The new aliasing catalog object feature in the data items inspect dialog lets users link newly uploaded datasets to their previous object IDs. This is especially helpful if a dataset was deleted and then restored, as creating an alias for the old object ID enables any workbooks referencing the original dataset to continue to work without interruption. By preserving these links, users can avoid broken reports and maintain smooth analytics workflows when restoring or replacing catalog items.  

Shared images in Oracle Analytics workbooks 

Workbook authors can now create and use shared images as part of the workbook layout, making it easy to add custom graphics or backgrounds to charts and canvases. This feature allows images to be uploaded and reused across multiple workbooks, streamlining branding, and design consistency. For example, a company logo or branded background can be applied to various dashboards, offering a cohesive and professional look throughout an organization’s analytics content.  

Perform multiple data action on a column in Oracle Analytics 

The January 2026 update allows workbook authors to assign multiple data actions to a specific column in tables or pivot tables, giving them more flexibility and control over interactivity. With this feature, different actions such as drilling into details, launching external links, or triggering workflows can be set up to respond to user clicks on a column. Clicking on a state in a sales table, for instance, might show detailed customer data or provide instant access to relevant analytics links, streamlining analysis and decision-making directly from the table. 

Vector map layers in Oracle Analytics  

Vector map layers enable responsive and interactive map visualizations by dividing maps into small grids and delivering only the needed tiles as users pan or zoom. This can help improve performance and responsiveness, even when working with complex or high-volume spatial data. A business could use this feature to visualize customer locations or asset coverage across a region, allowing users to interactively zoom and filter maps without slowdowns or loading delays. 

AI and Generative AI 

Oracle Analytics AI Agents  

Oracle Analytics AI Agents empower consumer users to get deeper, more context-aware insights by combining enterprise data, custom instructions, and proprietary knowledge documents within the Oracle Analytics AI Assistant. This feature allows organizations to tailor how the Oracle Analytics AI Assistant responds, helping ensure answers are accurate, relevant, and aligned with internal policies. For instance, an HR team can build an AI agent that references both employee data and company HR guidelines, so questions about salary changes or tenure receive precise, policy-driven responses directly within analytics dashboards. 

Asking Oracle Analytics AI Assistant about clusters and outliers  

The Oracle Analytics AI Assistant now makes it easy to identify patterns and anomalies in your data by answering natural language questions about clusters and outliers. Users can ask the Oracle Analytics AI Assistant to group data points, such as cities or products, into clusters, or to highlight outliers within a visualization, all without complex setup. This helps business users quickly uncover trends or spot unusual results in their data — for example, by grouping sales by region or identifying high-performing products— and easily add these insights to dashboards and reports. 

AI Assistant feedback in Oracle Analytics  

Users can now provide direct feedback on the quality of responses from the Oracle Analytics AI Assistant and AI Agents, helping administrators improve accuracy and relevance over time. After asking a question, users can give a thumbs up or thumbs down and specify the reason for their feedback, such as unclear or incomplete answers. This feedback is collected via the Oracle Cloud Infrastructure logging capability, allowing administrators to review community input, address common issues, and deliver a better natural language experience for everyone. 

Modeling, Preparation, and Connectivity  

Fine-grained permissions in the Oracle Analytics Semantic Modeler 

Fine-grained permissions in the Oracle Analytics Semantic Modeler allow administrators to create custom roles with more precise control, such as allowing users to create and edit semantic models without granting deployment privileges. This flexibility helps organizations support development workflows securely, so that only authorized users can deploy changes to production environments. A team member, for example, can be given permissions to build and update semantic models, while deployment rights remain limited to designated administrators.

Analytic Views in the Oracle Analytics Semantic Modeler 

With the January update you can now use Oracle Analytic Views as a direct data source in the Semantic Modeler, making it easy to build semantic models that leverage existing analytic logic and hierarchies. This capability streamlines the model creation process by automatically importing dimensions, hierarchies, and relationships from your Analytic Views. For instance, a user can quickly create a business model and subject area from an existing Analytic View, then build visualizations and dashboards without manual data modeling. 

Key Takeaways 

The January 2026 update to Oracle Analytics Cloud introduces powerful capabilities to help organizations manage and analyze data with greater control and insight. With enhancements like fine-grained permissions in the Semantic Modeler, support for Analytic Views, Oracle Analytics AI Agents, and new Oracle Analytics AI Assistant feedback options, users can work more securely and receive deeper, context-aware insights. The addition of features like performing multiple data actions on a column, adding aliases to data items in the Oracle Analytics catalog, and improved catalog folder management further enable teams to organize, interact with, and maintain their data more efficiently. These advancements help organizations streamline workflows, govern data assets, adapt quickly to business needs, and provide trusted access to information, supporting continuous innovation in how they use analytics. 

For more information: 

• What’s New for Oracle Analytics January 2026 

• January 2026 New Features Playlist  

• Oracle Analytics Community 

• Oracle Analytics Product Roadmaps