Course Outline
Introduction to Conversational Analytics
- Defining conversational analytics and its significance for product teams
- Core capabilities of WrenAI and an overview of its architecture
- Common product team workflows facilitated by Wren AI
Data Source Integration and Access
- Supported data sources and data ingestion patterns
- Managing data access, permissions, and multi-source joins
- Best practices for handling sample datasets and sandbox environments
Semantic Modeling and Metrics Standardization
- Designing the metrics layer and defining canonical standards
- Building reusable metrics and dimensions for product analytics
- Version control and governance of the semantic model
Natural Language to SQL Workflows
- How WrenAI converts NL queries into SQL and the strategies used for validation
- Prompting techniques and fallback mechanisms for product-related questions
- Addressing ambiguity, managing clarifying questions, 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 adoption rates and measuring the impact of self-service analytics
Quality Assurance, Evaluation, and Guardrails
- Testing NL-to-SQL accuracy and developing validation suites
- Monitoring drift, data quality indicators, and query audits
- Implementing safety measures, access controls, and business-rule guardrails
Workshop: Creating a Product Insights Flow
- Hands-on lab: Modeling a product metric, generating conversational queries, and verifying results
- Assembling a self-service dashboard with user guidance
- Presentations, feedback sessions, and planning next steps
Recap and Future Steps
Requirements
- A solid grasp of product metrics and KPIs
- Prior 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
