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
Data Source Connectivity and Access
- Supported data sources and ingestion methodologies
- Data access controls, permissions, and multi-source joins
- Best practices for handling sample datasets and sandbox environments
Semantic Modeling and Metrics Standardization
- Designing a metrics layer with canonical definitions
- Developing reusable metrics and dimensions for product analytics
- Version control and governance of the semantic model
Natural-Language to SQL Workflows
- How WrenAI transforms NL queries into SQL, along with validation strategies
- Prompting techniques and fallback mechanisms for product inquiries
- Managing ambiguity, clarifying questions, and intent design
Self-Service BI and Embedded Applications
- Creating conversational dashboards and templates tailored 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 NL-to-SQL accuracy and establishing validation suites
- Monitoring drift, data quality indicators, and query audits
- Security, access control, and business-rule guardrails
Workshop: Creating a Product Insights Flow
- Hands-on lab: Modeling a product metric, generating conversational queries, and verifying results
- Building a self-service dashboard with user guidance
- Presentations, feedback sessions, and action plans for next steps
Conclusion and Next Steps
Requirements
- Familiarity with product metrics and KPIs
- Practical 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
