Course Outline
Overview of Conversational Analytics
- Defining conversational analytics and its significance for product teams
- Core features and high-level architecture of WrenAI
- Common product team workflows facilitated by WrenAI
Data Source Integration and Access Control
- Compatible data sources and ingestion methods
- Managing data access, permissions, and cross-source joins
- Best practices for utilizing sample datasets and sandbox environments
Semantic Modeling and Metrics Uniformity
- Building a metrics layer with standardized definitions
- Developing reusable metrics and dimensions for product analytics
- Version control and governance of the semantic model
Natural-Language to SQL Processes
- Mechanisms for WrenAI to translate NL queries into SQL and validation methods
- Effective prompting strategies and fallback options for product inquiries
- Managing ambiguity, clarifying prompts, and intent design
Self-Service BI and Integrated Applications
- Creating conversational dashboards and templates tailored for product teams
- Integrating WrenAI into product workflows and internal tools
- Tracking adoption rates and assessing the impact of self-service analytics
Quality Assurance, Evaluation, and Controls
- Testing the precision of NL-to-SQL conversions and developing validation suites
- Monitoring data drift, quality indicators, and query audits
- Ensuring safety, access management, and adherence to business rule guardrails
Workshop: Constructing a Product Insight Pipeline
- Practical lab: Modeling a product metric, generating conversational queries, and verifying outputs
- Assembling a self-service dashboard with user instructions
- Presentations, peer feedback, and actionable next steps
Recap and Future Directions
Requirements
- A solid grasp of product metrics and KPIs
- Hands-on experience with data analysis or BI tools
- Foundational knowledge of SQL is advantageous
Target Audience
- Product managers
- Data analysts
- Data champions across 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
