Enhance Your Workbooks with Sigma + LLM Plugins: A Generative AI Guide

Think of Sigma + LLM Plugins as your AI-powered assistant that lives right inside your workbooks. You can:

• Generate instant summaries from complex datasets

• Perform sentiment analysis on customer feedback

• Classify data automatically

• Extract key insights from text data

• Translate content across languages

• Detect patterns and similarities in your data

The magic happens through SQL functions in your cloud data warehouse, allowing you to tap into AI capabilities without leaving your familiar Sigma environment. You don't need to switch between multiple tools or platforms – everything happens right where your data lives.

This guide will show you how to harness these AI capabilities to transform your workbooks into dynamic, intelligent analytical tools. You'll learn practical ways to integrate LLM functions into your workflows, create custom AI-powered solutions, and unlock deeper insights from your data.

Get ready to supercharge your data analysis with the combined strength of Sigma's analytical prowess and cutting-edge generative AI technology.

Understanding Sigma + LLM Plugins

Sigma's integration with Large Language Models creates a powerful synergy between data analytics and artificial intelligence. The platform seamlessly connects to your cloud data warehouse, allowing you to harness AI capabilities directly within your workbooks.

Core Integration Components:

• Native SQL function support

• Direct LLM access through formula bar

• Real-time AI processing within workbooks

You can access AI features through simple SQL functions, similar to standard database operations. These functions act as a bridge between your data and the AI models, enabling you to:

• Run sentiment analysis on customer feedback

• Generate text summaries from lengthy documents

• Classify data points into predefined categories

• Extract specific information from unstructured text

Available Generative AI Features in Sigma:

• Text AnalysisSentiment detection

• Entity recognition

• Key topic extraction

• Language detection

• Content GenerationText summarization

• Natural language responses

• Data descriptions

• Report generation

• Data EnhancementTranslation services

• Grammar correction

• Data similarity detection

• Pattern recognition

The platform's architecture allows you to write AI queries directly in the formula bar. For example, to analyze customer feedback sentiment, you might use:

sql SELECT analyze_sentiment(feedback_column) FROM customer_responses

These AI capabilities integrate naturally with Sigma's existing features, creating a unified workspace where data analysis and AI processing coexist. You can apply AI functions to specific columns, create new calculated fields based on AI insights, or generate entire datasets through AI-powered transformations.

The system's flexibility lets you customize AI interactions based on your specific needs. You can create reusable AI functions, build complex analytical workflows, and combine multiple AI capabilities within a single workbook.

Leveraging AI Functions in Cloud Data Warehouses

Each major cloud data warehouse offers unique AI capabilities through SQL functions, enabling powerful data analysis directly within Sigma workbooks.

1. Snowflake Cortex

• [SENTIMENT_ANALYSIS()](https://cloud.google.com/bigquery/docs/choose-ml-text-function): Analyzes customer feedback, social media posts, and support tickets

• ENTITY_EXTRACTION(): Identifies key information from unstructured text

Example query:

sql SELECT text_column, SENTIMENT_ANALYSIS(text_column) AS sentiment FROM customer_feedback

2. Databricks AI Functions

• Built-in classification models for content categorization

• Text clustering capabilities for pattern recognition

Sample implementation:

sql SELECT content, CLASSIFY_TEXT(content, 'topic') AS content_category FROM blog_posts

3. BigQuery AI Integration

• Natural language generation for automated content creation

• Question-answering capabilities using GENERATE_TEXT()

Implementation example:

sql SELECT question, GENERATE_TEXT(question, 'detailed_response') AS ai_response FROM customer_inquiries

4. Amazon Redshift with SageMaker

• Custom model deployment for specialized analysis

• Real-time inference through SQL functions

Query structure:

sql SELECT text_data, CALL_SAGEMAKER_ENDPOINT( text_data, 'custom-model-endpoint' ) AS prediction FROM user_interactions

These AI functions transform raw data into actionable insights:

• Customer Support: Analyze support ticket sentiment in real-time

• Content Management: Automatically categorize and tag content

• Market Research: Extract trends and patterns from customer feedback

• Data Enrichment: Generate descriptive summaries for large datasets

The integration of these AI capabilities within Sigma allows you to process data at scale while maintaining security and governance standards. You can combine multiple AI functions to create sophisticated analysis workflows, such as sentiment analysis followed by topic classification.

Enhancing Workbooks with Generative AI Insights

Sigma + LLM Plugins transform your workbooks into dynamic analytical powerhouses through seamless integration of AI-generated insights. Let's explore how you can enrich your workbooks with these capabilities.

Adding AI-Generated Elements to Workbooks

You can incorporate AI query results as new columns in your Sigma workbooks through these methods:

• Direct Column Creation: Apply AI functions to existing columns and create new ones containing generated insights

• Conditional Analysis: Set up automated AI analysis based on specific data conditions or triggers

• Batch Processing: Run AI functions across multiple data points simultaneously for comprehensive analysis

Dashboard Integration Techniques

Transform your dashboards with AI-powered visualizations:

• Create dynamic text elements that update based on AI analysis

• Generate automated data summaries for dashboard headers

• Build interactive elements responding to natural language queries

• Implement AI-driven data filtering and categorization

Custom Functions for Streamlined Workflows

Design reusable custom functions to standardize your AI operations:

sql CREATE FUNCTION analyze_customer_feedback(text STRING) RETURNS STRING AS $$ SELECT sentiment_analysis(text) $$;

Practical Applications:

• Sales Insights: Automatically generate deal summaries from customer interaction notes

• Support Analysis: Create sentiment trends from support ticket content

• Content Management: Auto-categorize and tag incoming documents

• Market Research: Extract competitive insights from news articles and reports

Workflow Optimization

Build efficient workflows by:

1. Creating templates for common AI analysis patterns

2. Setting up automated refresh schedules for AI-generated content

3. Establishing parameter-driven custom functions for flexible analysis

4. Implementing cascading AI analysis chains for complex insights

These enhancements create a robust analytical environment where data-driven decisions are augmented by AI-powered insights, making your workbooks more powerful and user-friendly.

Seamless Integration of Generative AI with Your Current Systems

Integrating generative AI directly with your existing cloud infrastructure creates a smooth analytical environment that eliminates data silos and reduces complexity. Sigma's LLM plugins connect directly to your cloud data warehouse, allowing for real-time AI processing without the need for data movement or additional security configurations.

Benefits of this Integration:

• Maintains existing data governance policies

• Preserves data lineage and audit trails

• Reduces latency through direct warehouse connections

• Ensures consistent security protocols

• Minimizes IT overhead and maintenance costs

This integration turns your business intelligence setup into an AI-powered analytics hub. Users can interact with data using natural language queries while still being able to perform structured SQL operations. This flexibility allows both technical and non-technical team members to uncover valuable insights.

Features of Dynamic Data Interaction:

• Natural language processing for ad-hoc queries

• Real-time AI model responses within familiar workbook interfaces

• Automated data refreshes and model updates

• Interactive visualizations of AI-generated insights

• Customizable AI workflows based on business needs

The Sigma platform's native AI integration creates a unified experience where advanced analytics become accessible through intuitive interfaces. Data scientists can deploy sophisticated models while business analysts leverage these capabilities through familiar spreadsheet-like controls. This democratization of AI capabilities accelerates decision-making processes and enhances analytical productivity across your organization.

Your teams can build complex analytical workflows combining traditional BI operations with AI-powered insights - all within a single platform that respects your existing data infrastructure investments.

Conclusion

Sigma + LLM Plugins are a game changer for analytical workbooks. This powerful combination brings AI-driven insights directly into your data analysis workflow, breaking down the barriers between data exploration and artificial intelligence.

With Sigma + LLM Plugins, you can:

• Run AI queries seamlessly within your workbooks

• Generate instant insights from complex datasets

• Create dynamic, intelligent dashboards

• Maintain data security and governance

These capabilities make Sigma + LLM Plugins a revolutionary solution for modern data teams. By connecting raw data with actionable intelligence, you can make quicker, more informed decisions while keeping your data secure within your existing infrastructure. The future of analytical workbooks is here - powered by generative AI and ready to unlock unprecedented value from your data assets.

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