Course Outline
Introduction to Conversational Analytics
- Defining conversational analytics and its significance for product teams
- Core capabilities and high-level architecture of WrenAI
- Common product team workflows facilitated by Wren AI
Connecting Data Sources and Managing Access
- Supported data sources and ingestion patterns
- Data access controls, permissions, and multi-source joins
- Best practices for handling sample datasets and sandbox environments
Semantic Modeling and Metrics Standardization
- Structuring a metrics layer with canonical definitions
- Developing reusable metrics and dimensions for product analytics
- Versioning and governance of the semantic model
Natural-Language to SQL Workflows
- The process by which WrenAI converts NL queries to SQL and associated validation strategies
- Prompting patterns and fallback mechanisms for product-related questions
- Managing ambiguity, clarifying queries, and intent design
Self-Service BI and Embedded Applications
- Designing conversational dashboards and templates for product teams
- Integrating Wren AI into product workflows and internal tools
- Assessing the adoption and impact of self-service analytics
Quality Assurance, Evaluation, and Guardrails
- Testing the accuracy of NL-to-SQL conversions and building validation suites
- Monitoring drift, data quality indicators, and query audits
- Ensuring safety, access control, and adherence to business-rule guardrails
Workshop: Constructing a Product Insights Flow
- Hands-on lab: Modeling a product metric, generating conversational queries, and verifying results
- Building a self-service dashboard and providing user guidance
- Presentations, feedback sessions, and defining next-step action plans
Recap and Future Steps
Requirements
- Knowledge of product metrics and KPIs
- Prior experience with data analysis or BI tools
- Foundational familiarity with SQL is advantageous
Target Audience
- Product managers
- Data analysts
- Business unit data champions
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
