Course Outline
Overview of Conversational Analytics
- Definition of conversational analytics and its significance for product teams
- Key features of WrenAI and its high-level architecture
- Common product team workflows facilitated by Wren AI
Data Source Connectivity and Access
- Compatible data sources and ingestion methods
- Data access rights, permissions, and multi-source joins
- Best practices for sample datasets and sandbox environments
Semantic Modeling and Metrics Standardization
- Constructing a metrics layer and defining canonical standards
- Developing reusable metrics and dimensions for product analytics
- Versioning and governance of the semantic model
Natural Language to SQL Workflows
- Mechanism by which WrenAI converts NL queries to SQL, including validation strategies
- Prompting techniques and fallback options for product inquiries
- Managing ambiguity, clarifying questions, and intent design
Self-Service BI and Embedded Applications
- Designing conversational dashboards and templates for product teams
- Integrating Wren AI into product workflows and internal tools
- Assessing the adoption and impact of self-service analytics
Quality, Evaluation, and Safety Guardrails
- Testing NL-to-SQL accuracy and developing validation suites
- Monitoring drift, data quality indicators, and query audits
- Safety protocols, access control, and business-rule guardrails
Workshop: Creating a Product Insights Workflow
- Practical lab: Modeling a product metric, generating conversational queries, and validating outcomes
- Assembling a self-service dashboard with user guidance
- Presentations, peer feedback, and action plans for next steps
Summary and Future Directions
Requirements
- Familiarity with product metrics and KPIs
- 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