Programa del Curso

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

Requerimientos

  • Comprender las métricas y KPIs del producto
  • Experiencia con herramientas de análisis de datos o BI
  • Familiaridad básica con SQL es beneficiosa

Target Audience

  • Gerentes de producto
  • Analistas de datos
  • Campeones de datos en unidades de negocio
 14 Horas

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