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Azure Warehouse Analytics Chatbot

Business Need​

Enable business users to query enterprise data warehouses using natural language instead of SQL, with automatic generation of visualizations and insights, while maintaining proper authentication and data access controls.

Solution Overview​

This solution leverages a Custom Chatbot with Azure integration to deliver conversational data access:

Step 1: Authentication & Access Control

  • Authentication: Users authenticate through Microsoft Entra ID (Azure Active Directory)
  • Security: Row-level and table-level permissions are automatically enforced based on user identity
  • Connection: Azure SQL user connector maintains live connection to the data warehouse

Step 2: Natural Language to SQL Translation

The Custom Chatbot interprets natural language queries and generates optimized SQL:

  • Understands business terminology and translates to technical column names
  • Handles complex queries including joins, aggregations, and time-based filtering
  • Optimizes queries for performance on large datasets
💡Pro Tip

Provide the chatbot with a data dictionary mapping business terms to technical schema. For example, "last quarter's sales" → "SUM(revenue) WHERE date >= DATEADD(quarter, -1, GETDATE())". This dramatically improves accuracy for domain-specific queries.

Step 3: Visualization & Insights

  • Python code is automatically generated for data visualization based on query results
  • Creates appropriate chart types (line graphs, bar charts, heatmaps) based on data structure
  • Provides statistical summaries and key insights alongside visualizations

How It's Used in Practice​

This solution provides self-service analytics to business users:

Daily Operations:

  • Business analysts access the Chatbot through the ChatLLM UI
  • Ask questions like "Show me sales trends by region for Q4" or "Which products had the highest return rate last month?"
  • Receive immediate SQL execution results with visualizations
  • Export results or share visualizations with stakeholders

Typical Response Time: 10-25 seconds for most queries

Common Use Cases:

  • Sales performance analysis across regions and time periods
  • Customer behavior and cohort analysis
  • Inventory and supply chain metrics
  • Financial reporting and variance analysis

Users report reduction in time spent requesting and waiting for data from analytics teams, with business users now able to self-serve 80% of their data needs.

✨Key Outcomes

Key achievements:

🔍 Self-service analytics for 40+ business users​

Democratized data access without requiring SQL knowledge

⚡ 50% reduction in ad-hoc data request volume​

Analytics teams can focus on strategic projects instead of routine queries

🔒 Secure, role-based data access​

Maintains enterprise security while improving accessibility

Additional Information​