The Future of Analytics: How Tableau Next Transforms Data into Action – Part 2

Part 1 introduced the foundational pillars of Tableau Next, including unified data, semantic clarity, and a flexible architecture built to address long-standing analytics challenges. Now, those foundations come to life through intelligent features that bring analytics closer to decision-making than ever before. With AI-driven skills, automated modeling, and seamless integration, Tableau Next empowers users to access insights faster and act with greater confidence in every workflow.

AI-Powered Analytics Skills: The Technical Foundation

Tableau Next introduces three core AI-powered analytics skills that transform how users interact with data:

Data Pro: Accelerate your analytics journey and turn data into actionable insights with a Data Pro, a skill that helps prepare, model, and visualize data.

Concierge: Ask questions and get analytical answers in natural language with the Concierge skill. It identifies root causes, provides relevant visualizations, and suggests next-best actions.

Inspector: Inspector proactively monitors data in real time, keeping you informed of trends and anomalies. This is the future of BI—where insights find you in your workflow.

Technical AI Capabilities in Tableau Semantics

The semantic layer is enhanced with several AI-powered features that accelerate data modeling:

AI-Driven Relationship Generation: Enhance efficiency with automated suggestions for relationships and joins across existing and new data objects, while intuitively joining data objects using natural language.

Natural Language Field Creation: Effortlessly create calculated fields, formulas, and filters using natural language. AI-powered suggestions help identify existing fields before creating new ones, minimizing duplication and maintaining data quality.

Automated Model Creation: Create semantic data models in seconds using natural language. Instantly extract relevant data entities without setup, and extend your model using AI-powered features – all without modifying the original structure.

Agent Enrichment: Get accurate responses and relevant insights with trusted, context-rich data, while continuously expanding agent knowledge to enable deeper understanding of your business, intent, and nuances.

Enterprise-Grade Technical Infrastructure

Hyperforce Foundation: The semantic layer is built on Hyperforce, which abstracts much of the complexity around security and compliance. By leveraging this infrastructure, we avoid reinventing security measures, enabling both speed and reliability.

Scalability Architecture: To manage system load, we employ a two-pronged approach: top-down and bottom-up. During the design and planning phases, we identify and mitigate potential bottlenecks through collaboration and by leveraging our team’s expertise. This proactive top-down strategy is complemented by stress-testing during development.

Query Performance Optimization: We emphasize optimizing the SQL queries generated by the semantic layer. By using efficient joins, applying early filtering, and minimizing query complexity, we follow best practices in SQL design. Additionally, we test and refine SQL patterns early in development, proactively identifying and resolving performance bottlenecks.

From Reactive to Proactive: The Tableau Next Technical Workflow

Here’s where things get technically exciting. Imagine starting with your CRM data and following this journey:

1. Data Cloud Ingestion: Raw customer data flows into Data Cloud through various connectors (Snowflake, BigQuery, Databricks, etc.)
2. Semantic Model Creation: AI-powered tools help create semantic models that define consistent business metrics
3. Query Translation: The Semantic Query Generator translates business questions into optimized SQL queries
4. Visualization Generation: Tableau Next creates visualizations based on semantic definitions
5. Agentic Recommendations: AI agents analyze patterns and provide specific, actionable recommendations

This technical workflow ensures that every step is grounded in trusted, semantically-rich data while maintaining enterprise-grade performance and security.

Technical Implementation: Your Path to Smarter Analytics

Salesforce isn’t asking organizations to rip and replace their existing systems. Instead, Tableau Next is rolling out in phases throughout 2025 and beyond with specific technical milestones:

Available Now:

• Tableau Semantics is currently GA to Tableau Next customers as part of the Tableau+ SKU and to Data Cloud customers.
• Output CRM Analytics and Tableau data to Data Cloud
• Build semantic models with AI assistance
• Create early Tableau Next experiences

Summer 2025:

• Tableau Core connects directly to semantic models
• Beta support for semantic models on Tableau Published Data Sources
• Easily import your Tableau Published Data Sources into Tableau Semantics without having to migrate existing data or semantic models.

Winter 2025:

• CRM Analytics connects directly to semantic models
• Enhanced visualization capabilities
• Connect Tableau Cloud, Server, and Desktop to Tableau Semantics for deep analysis of your semantic models, ensuring seamless interoperability.

2026 and Beyond:

• Direct semantic model creation from CRM datasets
• Transformation tools for migrating existing assets
• Enhanced ecosystem integrations

Technical Benefits: Solving Real Business Problems

At its technical core, Tableau Next tackles four fundamental challenges:

Unified Data Architecture: Tableau Semantics helps synchronize your organization’s business intelligence tools with a standardized data model, enhancing data integrity and governance across analytics experiences and applications — ultimately making data more accessible, consistent, and actionable.

Composable Analytics: Empower data professionals and analysts with semantic models as building blocks. This allows data administrators to maintain a single source of truth while enabling data analysts to self-serve by extending these federated semantic models in their own versions.

AI-Enhanced Accuracy: Designed for agentic AI, Tableau Semantics unlocks the power of Tableau Next, Data Cloud, and Agentforce by enriching the harmonized and unified data with business knowledge, creating cleaner foundations for agentic experiences. Having deep, meaningful context for data and metadata improves the quality, reliability, and efficiency of retrieval-augmented generation (RAG).

Enterprise Governance: Simplify management and governance by centralizing all organizational metrics in a single source of truth, including setting goals on metrics and triggering actions accordingly, along with gen AI-based insights for deduping, cleaning, and maintaining the quality of metrics.

Integration and Ecosystem: Technical Connectivity

Slack Integration: Deliver the power of Tableau Next in Slack, bringing actionable insights to every conversation. Share live metrics, explore dashboards, and ask agents questions about your data in natural language to get accurate responses instantly.

API-First Architecture: Designed as an open, API-first platform, it accelerates analytics development with reusable, composable assets and enables users to access insights anywhere, making data-driven decision-making more intuitive and impactful.

Ecosystem Compatibility: We understand that many organizations have made deep investments in other tools, and our roadmap includes connecting them to Tableau Next to allow you to use them together. Customers can use their own data lakehouse by leveraging Data Cloud connectors—this includes batch and streaming ingestion, zero copy, and bring-your-own-lake capabilities.

Technical Positioning: Tableau Next vs. Existing Solutions

Tableau Next vs. CRM Analytics: CRM Analytics is a tool for visual analytics and predictions native to Salesforce Clouds. Tableau Next provides agent-powered analytics that can be embedded everywhere and an end-to-end API-first, enterprise analytics stack that is flexible and reusable.

Migration Strategy: Whether you have Tableau Cloud, Tableau Server, CRM Analytics, or any other analytics solution from Salesforce, you and your analytics assets are in good hands and will continue to be invested in. We have robust product roadmaps for each of these existing products today and will deliver on them.

Getting Started: Technical Requirements

Access: Tableau+ offers the most direct path to Tableau Next today by providing a comprehensive package tailored for wall-to-wall adoption. It includes management tools, generative AI features, and Premium Support to bring insights to everyone.

Prerequisites: Tableau Semantics is deeply integrated into Data Cloud and is not available without it.

Licensing: You can get Tableau Semantics with Tableau Next as part of the Tableau+ SKU and with Data Cloud.

The Bottom Line: From Dashboards to Decisions

For too long, analytics has been trapped in the world of “what happened.” Tableau Next represents a fundamental shift toward “what should happen next.” It’s the first BI platform with a workflow engine that seamlessly connects the entire analytics journey—from raw data to insights to action.

By embedding agentic AI within a composable, enterprise-grade platform built on Hyperforce and powered by Data Cloud, Tableau Next finally delivers on analytics’ long-standing promise: turning insights into action, efficiently and confidently.

The technical architecture—combining Data Cloud’s unified data layer, Tableau Semantics’ AI-powered semantic modeling, and Agentforce’s agentic capabilities—creates an unprecedented foundation for data-driven decision making. This enables users to query data conversationally without needing technical expertise. Our ultimate goal is to make data accessible, actionable, and valuable while ensuring a seamless, intelligent, and scalable analytics experience.

If your organization feels stuck in the “data-rich, insight-poor” trap, Tableau Next’s technical capabilities might be the key to unlocking your analytical potential. The question isn’t whether the future of analytics is here—it’s whether you’re ready to embrace its technical possibilities.