Learn about Agentforce for Analytics in Tableau Next
Learning Objectives
After completing this unit, you’ll be able to:
- Explain how Agentforce for Analytics in Tableau Next enhances data analysis capabilities.
- Describe the benefits of the pre-built data analytics skills.
Meet Agentforce for Analytics in Tableau Next
Agentforce for Analytics provides advanced, conversational analytics within Tableau Next. It delivers prebuilt analytics skills that help you go from data to insights to action faster than ever before. These prebuilt skills help every user—from data analysts looking to prepare, model, and analyze data faster, to business professionals wanting to stay informed about key performance indicators.
How It Works
Built on Salesforce, with Data Cloud as its unified data layer, Tableau Next helps you connect to your data across various databases and applications in one place. Then, by applying the semantic layer provided by Tableau Semantics, the complex data structures and technical jargon used in your data sources is translated into business-friendly terms. The combination of these layers provides you with a secure, comprehensive view of all your data that’s easy to access and understand. For example, a marketing team might combine sales data, website analytics, and social media engagement data from several different applications and analyze it in Tableau Next to understand a marketing campaign’s overall performance.
Within Tableau Next is a group of AI analytics capabilities known as Agentforce for Analytics. These prebuilt analytics skills help you prepare and model data faster, analyze data more naturally with conversational insights, and easily stay informed about key metrics. Look for the Agentforce icon in the top right corner of your Tableau Next dashboard or metric detail page to get started.
Prebuilt Analytics Skills Kickstart Agentforce for Analytics
Get a helping hand managing your data from these valuable analytics skills in Tableau Next.
Concierge Skill: Make Analytics More Accessible
The Concierge skill is designed to make analytics more accessible to business users. It allows you to ask natural language questions and receive reliable and helpful answers with visual insights. For instance, if you’re a marketing manager and you want to know the performance of a recent campaign, you can simply ask, “How did the Q4 marketing campaign perform?” To make it easy to understand and act on your data, your response from Concierge includes:
- A rich, natural language response that contextually answers your question
- An interactive visualization that helps you see and understand the data behind the answer
- An explanation of the AI reasoning that describes how the answer was generated
- Details about the source data that was used to answer the question
- Options for providing response feedback or copying the answer to share or use elsewhere
Data Pro Skill: Accelerate Semantic Modeling and Data Preparation
The Data Pro skill (beta) is a game-changer for analysts and data stewards. It uses AI to suggest relationships between data sources and helps generate formulas for calculated fields, making the process of building robust semantic data models much faster and more efficient. For example, if you’re working on a project that involves multiple data sources, Data Pro can quickly identify and suggest the best ways to connect these sources, saving you hours of manual work. This means you can focus more on analyzing the data and less on preparing it.
Wrap Up
Now that you know how Agentforce for Analytics enhances data analysis capabilities, you can use these prebuilt skills to jumpstart your analysis and turn data into real results with Tableau Next.
Resources
- Salesforce Help: About Tableau Next
- Tableau Blog: What is Tableau Next?
- Trailhead: Introduction to Agentforce
Quiz Scenario
Nancy is a marketing manager for a national retail chain. She recently launched an advertising campaign across multiple online and offline channels for a new product line. To understand the campaign’s effectiveness, Nancy needs to analyze data from the chain’s website analytics platform, social media engagement metrics, and in-store sales figures, all of which are stored in a single semantic data model in Data Cloud. She wants a quick and easy way to get a high-level overview of the campaign’s performance and identify any immediate areas of concern or success without spending hours manually combining and analyzing the data.