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Course Outline

Overview of Conversational Analytics

  • Definition of conversational analytics and its strategic value for product development teams
  • Core capabilities and high-level architecture of WrenAI
  • Standardized workflows supported by Wren AI for product personnel

Data Source Integration and Access Management

  • Compatible data sources and ingestion methodologies
  • Protocols for data access, permission management, and multi-source data integration
  • Best practices for utilizing sample datasets and sandbox environments

Semantic Modeling and Metric Standardization

  • Framework for constructing a metrics layer and establishing canonical definitions
  • Development of reusable metrics and dimensions specific to product analytics
  • Version control and governance procedures for the semantic model

Natural Language to SQL Conversion Workflows

  • Mechanisms for translating natural language queries into SQL, including validation strategies
  • Prompt engineering patterns and contingency plans for product-related inquiries
  • Approaches to managing ambiguity, facilitating clarifying questions, and refining intent design

Self-Service Business Intelligence and Embedded Applications

  • Configuration of conversational dashboards and templates for product teams
  • Integration of Wren AI into existing product workflows and internal government tools
  • Metrics for evaluating adoption rates and the operational impact of self-service analytics

Quality Assurance, Evaluation, and Security Guardrails

  • Testing accuracy in natural language to SQL conversion and establishing validation suites
  • Monitoring data drift, quality indicators, and conducting query audits
  • Implementing safety protocols, access controls, and business-rule guardrails for government operations

Workshop: Development of a Product Insights Workflow

  • Practical exercise: modeling product metrics, generating conversational queries, and validating outcomes
  • Assembly of self-service dashboards and development of user guidance materials
  • Review sessions, feedback collection, and formulation of next-step action plans

Summary and Strategic Next Steps

Requirements

  • Proficiency in evaluating product performance metrics and key performance indicators
  • Practical experience utilizing data analysis or business intelligence platforms
  • Foundational knowledge of SQL is advantageous

Target Audience

  • Product management professionals
  • Data analysis specialists
  • Subject matter experts and data advocates within business units
 14 Hours

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