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 Duration 14 hours

Course Outline

Introduction to Conversational Analytics

  • Defining conversational analytics and its significance for product teams
  • Key capabilities and high-level architecture of WrenAI
  • Common product team workflows facilitated by Wren AI

Connecting Data Sources and Access

  • Supported data sources and ingestion methods
  • Managing data access, permissions, and multi-source joins
  • Best practices for sample datasets and sandbox environments

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 converts NL queries to SQL and applies validation strategies
  • Prompting patterns and fallback mechanisms for product questions
  • Managing ambiguity, clarifying queries, 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
  • Assessing the adoption and impact of self-service analytics

Quality, Evaluation, and Guardrails

  • Testing NL-to-SQL accuracy and developing validation suites
  • Monitoring drift, data quality indicators, and query audits
  • Ensuring safety, access control, and business-rule guardrails

Workshop: Building a Product Insights Flow

  • Hands-on lab: Modeling a product metric, generating conversational queries, and verifying results
  • Assembling a self-service dashboard with user guidance
  • Presentations, feedback, and formulation of next-step action plans

Summary and Next Steps

Requirements

  • A solid grasp of product metrics and KPIs
  • Experience with data analysis or BI tools
  • Basic familiarity with SQL is advantageous

Target Audience

  • Product managers
  • Data analysts
  • Data champions within business units

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