Course Outline
Introduction to Conversational Analytics
- Understanding conversational analytics and its significance for product teams
- Key WrenAI capabilities and high-level architectural overview
- Common product team workflows facilitated by Wren AI
Linking Data Sources and Access Control
- Compatible data sources and ingestion methods
- Data access rights, permissions, and joining multiple sources
- Best practices regarding sample datasets and sandbox environments
Semantic Modeling and Standardizing Metrics
- Designing the metrics layer and establishing canonical definitions
- Developing reusable metrics and dimensions for product analytics
- Version control and governance of the semantic model
Natural-Language to SQL Processes
- How WrenAI converts NL queries to SQL and associated validation strategies
- Prompting techniques and fallback mechanisms for product queries
- Managing ambiguity, asking clarifying questions, and designing intent
Self-Service BI and Embedded Applications
- Creating conversational dashboards and templates tailored for product teams
- Integrating Wren AI into product workflows and internal tools
- Tracking adoption rates and the impact of self-service analytics
Quality Assurance, Evaluation, and Guardrails
- Testing the accuracy of NL-to-SQL conversions and creating validation suites
- Monitoring drift, data quality indicators, and auditing queries
- Safety protocols, access management, and business-rule guardrails
Workshop: Creating a Product Insight Flow
- Practical lab: Modeling a product metric, generating conversational queries, and verifying results
- Building a self-service dashboard along with user guidance materials
- Presentations, peer feedback, and action plans for next steps
Summary and Future Directions
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
