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 Access
- Compatible data sources and ingestion methods
- Managing data access, permissions, and multi-source joins
- Best practices for handling sample datasets and sandbox environments
Semantic Modeling and Metrics Standardization
- Structuring a metrics layer and establishing canonical definitions
- Developing reusable metrics and dimensions for product analytics
- Versioning and governance of the semantic model
Natural-Language to SQL Workflows
- How WrenAI converts NL queries to SQL and associated validation strategies
- Prompting techniques and fallback options for product inquiries
- Addressing ambiguity, clarifying questions, and intent design
Self-Service BI and Embedded Use Cases
- Designing conversational dashboards and templates tailored for product teams
- Embedding 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 precision and constructing validation suites
- Monitoring drift, data quality indicators, and query audits
- Ensuring safety, access control, and business-rule guardrails
Workshop: Build a Product Insights Flow
- Practical lab: Model a product metric, formulate conversational queries, and verify results
- Constructing a self-service dashboard and user guidance
- Presentations, feedback sessions, and planning next steps
Summary and Next Steps
Requirements
- Knowledge of product metrics and KPIs
- Practical experience with data analysis or BI tools
- Basic SQL proficiency is advantageous
Target Audience
- Product managers
- Data analysts
- Data champions within business units
Testimonials (4)
That it was very practical.
Alfonso Ramos - Banco de Mexico
Course - Fundamentos de Integración de Datos Pentaho
Machine Translated
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
