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
- Compatible data sources and ingestion methods
- Data access protocols, permissions, and multi-source joining techniques
- Best practices for managing sample datasets and sandbox environments
Semantic Modeling and Metrics Standardization
- Architecting a metrics layer and establishing canonical definitions
- Developing reusable metrics and dimensions for product analytics
- Managing version control and governance of the semantic model
Natural-Language to SQL Processes
- How WrenAI converts NL queries into SQL and associated validation techniques
- Effective prompting patterns and fallback strategies for product inquiries
- 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 tooling
- Assessing the adoption rate and impact of self-service analytics
Quality Assurance, Evaluation, and Guardrails
- Testing NL-to-SQL accuracy and developing validation suites
- Tracking drift, data quality indicators, and performing query audits
- Ensuring safety, access control, and enforcing business-rule guardrails
Workshop: Constructing a Product Insights Workflow
- Practical lab: modeling a product metric, generating conversational queries, and verifying outcomes
- Building a self-service dashboard and providing user guidance
- Presentations, peer feedback, and defining next-step action plans
Conclusion 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
