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Course Outline
Introduction to Conversational Analytics
- What is conversational analytics and why it matters for product teams
- WrenAI key capabilities and high-level architecture
- Typical product team workflows enabled by Wren AI
Connecting Data Sources and Access
- Supported data sources and ingestion patterns
- Data access, permissions, and multi-source joins
- Best practices for sample datasets and sandboxing
Semantic Modeling and Metrics Standardization
- Designing a metrics layer and canonical definitions
- Creating reusable metrics and dimensions for product analytics
- Versioning and governance of the semantic model
Natural-Language to SQL Workflows
- How WrenAI translates NL queries to SQL and validation strategies
- Prompting patterns and fallbacks for product questions
- Handling ambiguity, clarifying questions, and intent design
Self-Service BI and Embedded Use Cases
- Designing conversational dashboards and templates for product teams
- Embedding Wren AI into product workflows and internal tools
- Measuring adoption and impact of self-service analytics
Quality, Evaluation, and Guardrails
- Testing NL-to-SQL accuracy and building validation suites
- Monitoring drift, data quality signals, and query audits
- Safety, access control, and business-rule guardrails
Workshop: Build a Product Insights Flow
- Hands-on lab: model a product metric, create conversational queries, and validate results
- Assemble a self-service dashboard and user guidance
- Presentations, feedback, and next-step action plans
Summary and Next Steps
Requirements
- An understanding of product metrics and KPIs
- Experience with data analysis or BI tools
- Basic familiarity with SQL is beneficial
Audience
- Product managers
- Data analysts
- Data champions in business units
14 Hours
