Course Outline
Introduction to Conversational Analytics
- Defining conversational analytics and its significance for product teams
- Key capabilities and high-level architecture of WrenAI
- Common product team workflows facilitated by Wren AI
Connecting Data Sources and Access
- Supported data sources and data ingestion methods
- Managing data access, permissions, and multi-source joins
- Best practices for handling sample datasets and sandbox environments
Semantic Modeling and Metrics Standardization
- Constructing a metrics layer and defining standard definitions
- Developing reusable metrics and dimensions for product analytics
- Implementing version control and governance for the semantic model
Natural-Language to SQL Workflows
- How WrenAI converts NL queries to SQL and associated validation strategies
- Prompting techniques and fallback mechanisms for product questions
- Managing ambiguity, clarifying queries, and designing intent
Self-Service BI and Embedded Use Cases
- Creating conversational dashboards and templates tailored for product teams
- Integrating Wren AI into product workflows and internal tools
- Tracking the adoption and impact of self-service analytics
Quality, Evaluation, and Guardrails
- Testing NL-to-SQL accuracy and establishing validation suites
- Monitoring drift, data quality indicators, and query audits
- Enforcing safety, access controls, and business-rule guardrails
Workshop: Building a Product Insights Flow
- Practical lab: modeling a product metric, creating conversational queries, and verifying results
- Constructing a self-service dashboard with user guidance
- Presentations, peer feedback, and action plans for next steps
Summary and Next Steps
Requirements
- A solid understanding of product metrics and KPIs
- Hands-on experience with data analysis or BI tools
- Basic 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
