Course Outline
Introduction to Conversational Analytics
- Defining conversational analytics and its significance for product teams
- Key features of WrenAI and an overview of its architecture
- Common product team workflows facilitated by Wren AI
Data Source Integration and Access
- Compatible data sources and ingestion methods
- Managing data access, permissions, and cross-source joins
- Best practices for utilizing sample datasets and sandbox environments
Semantic Modeling and Metrics Standardization
- Architecting a metrics layer and defining canonical terms
- Developing reusable metrics and dimensions for product analytics
- Version control and governance of the semantic model
Natural-Language to SQL Processes
- Mechanisms for WrenAI to convert NL queries to SQL and validation tactics
- Prompting techniques and fallback strategies for product inquiries
- Addressing ambiguity, clarifying questions, and intent formulation
Self-Service BI and Embedded Applications
- Crafting conversational dashboards and templates for product teams
- Embedding Wren AI into product workflows and internal utilities
- Tracking adoption rates and the impact of self-service analytics
Quality Assurance, Evaluation, and Guardrails
- Assessing NL-to-SQL precision and constructing validation suites
- Monitoring data drift, quality indicators, and query logs
- Ensuring security, access management, and business-rule constraints
Workshop: Creating a Product Insights Workflow
- Practical exercise: Model a product metric, generate conversational queries, and verify outcomes
- Building a self-service dashboard with user guidance
- Presentations, peer feedback, and action plans for next steps
Recap and Future Directions
Requirements
- Familiarity with product metrics and KPIs
- Prior experience with data analysis or BI tools
- Foundational knowledge of SQL is advantageous
Target Audience
- Product managers
- Data analysts
- Data champions 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
