Course Outline
Introduction to Conversational Analytics
- Understanding conversational analytics and its significance for product teams.
- Overview of WrenAI key capabilities and high-level architecture.
- Typical product team workflows facilitated by Wren AI.
Connecting Data Sources and Access
- Supported data sources and ingestion methods.
- Data access, permissions management, and multi-source joins.
- Best practices for sample datasets and sandbox environments.
Semantic Modeling and Metrics Standardization
- Designing a metrics layer and establishing canonical definitions.
- Creating reusable metrics and dimensions for product analytics.
- Versioning and governance of the semantic model.
Natural-Language to SQL Workflows
- Mechanism of WrenAI translating natural language queries to SQL and validation strategies.
- Prompting patterns and fallback options for product inquiries.
- Managing ambiguity, clarifying questions, and intent design.
Self-Service BI and Embedded Use Cases
- Designing conversational dashboards and templates for product teams.
- Embedding Wren AI into product workflows and internal tools.
- Measuring the adoption and impact of self-service analytics.
Quality, Evaluation, and Guardrails
- Testing NL-to-SQL accuracy and developing validation suites.
- Monitoring drift, data quality signals, and conducting query audits.
- Safety measures, access control, and business-rule guardrails.
Workshop: Build a Product Insights Flow
- Hands-on lab: modeling a product metric, creating conversational queries, and validating results.
- Assembling a self-service dashboard and user guidance.
- Presentations, feedback sessions, and next-step action plans.
Summary and Next Steps
Requirements
- Understanding of product metrics and Key Performance Indicators (KPIs).
- Experience with data analysis or Business Intelligence (BI) tools.
- Basic familiarity with 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
