Course Outline
Introduction to Conversational Analytics
- Defining conversational analytics and its significance for product teams
- Core capabilities of WrenAI and an overview of its architecture
- Common product team workflows facilitated by Wren AI
Data Source Integration and Access
- Supported data sources and ingestion methods
- Data access controls, permissions, and cross-source joins
- Best practices for managing sample datasets and sandbox environments
Semantic Modeling and Metric Standardization
- Architecting the metrics layer and establishing canonical definitions
- Developing reusable metrics and dimensions for product analytics
- Managing versioning and governance of the semantic model
Natural-Language to SQL Workflows
- How WrenAI converts NL queries into SQL, along with validation techniques
- Prompting strategies and fallback mechanisms for product inquiries
- Managing ambiguity, clarifying queries, and defining intent
Self-Service BI and Embedded Applications
- Designing conversational dashboards and templates tailored for product teams
- Integrating Wren AI into product workflows and internal tools
- Tracking adoption rates and assessing the impact of self-service analytics
Quality Assurance, Evaluation, and Guardrails
- Testing NL-to-SQL accuracy and creating validation suites
- Monitoring data drift, quality indicators, and query audits
- Ensuring safety, access control, and business rule compliance
Workshop: Creating a Product Insights Flow
- Practical lab: modeling a product metric, generating conversational queries, and verifying outcomes
- Assembling a self-service dashboard with user instructions
- Presenting findings, receiving feedback, and planning next steps
Conclusion and Next Steps
Requirements
- A solid grasp of product metrics and KPIs
- Hands-on 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
