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
Testimonials (3)
Deepthi was super attuned to my needs, she could tell when to add layers of complexity and when to hold back and take a more structured approach. Deepthi truly worked at my pace and ensured I was able to use the new functions /tools myself by first showing then letting me recreate the items myself which really helped embed the training. I could not be happier with the results of this training and with the level of expertise of Deepthi!
Deepthi - Invest Northern Ireland
Course - IBM Cognos Analytics
he was well prepared - and he is very sympathetic
Oliver - Post CH AG
Course - Splunk Fundamentals
lots of pratical exercises