Course Outline
Introduction to Conversational Analytics
- Defining conversational analytics and its significance for product teams
- Key features and high-level architecture of WrenAI
- Common product team workflows facilitated by Wren AI
Data Source Connectivity and Access
- Compatible data sources and ingestion methods
- Data access, permission management, and multi-source joins
- Best practices for handling sample datasets and sandbox environments
Semantic Modeling and Metric Standardization
- Designing metrics layers and defining canonical terms
- Developing reusable metrics and dimensions for product analytics
- Version control and governance of semantic models
Natural-Language to SQL Workflows
- The mechanism by which WrenAI converts NL queries to SQL, along with validation techniques
- Prompting strategies and fallback options for product inquiries
- Managing ambiguity, clarifying questions, and intent structuring
Self-Service BI and Embedded Applications
- Creating conversational dashboards and templates tailored for product teams
- Integrating Wren AI into product workflows and internal tools
- Evaluating the adoption and 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
- Safety protocols, access controls, and business rule guardrails
Workshop: Constructing a Product Insights Flow
- Practical lab: Modeling a product metric, generating conversational queries, and verifying outcomes
- Assembling a self-service dashboard and user guidance materials
- Presentations, peer feedback, and planning subsequent actions
Conclusion and Next Steps
Requirements
- Knowledge of product metrics and KPIs
- 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
