Course Outline
Introduction to Conversational Analytics
- Understanding conversational analytics and its significance for product teams
- Overview of WrenAI's core capabilities and architectural design
- Common product team workflows facilitated by WrenAI
Data Source Integration and Access
- Compatible data sources and ingestion strategies
- Managing data access, permissions, and multi-source joins
- Best practices for sample datasets and sandbox environments
Semantic Modeling and Metrics Standardization
- Defining a metrics layer and establishing canonical definitions
- Building reusable metrics and dimensions for product analytics
- Governance and versioning of the semantic model
Natural Language to SQL Workflows
- Mechanisms for translating NL queries to SQL and validation techniques
- Prompting strategies and fallback options for product inquiries
- Managing ambiguity, clarifying questions, and intent structure
Self-Service BI and Embedded Applications
- Designing conversational dashboards and templates for product teams
- Integrating WrenAI into product workflows and internal systems
- Tracking adoption rates and assessing the impact of self-service analytics
Quality Assurance, Evaluation, and Guardrails
- Testing NL-to-SQL precision and developing validation suites
- Monitoring data drift, quality indicators, and audit trails
- Ensuring safety, access control, and business rule enforcement
Workshop: Developing a Product Insight Flow
- Practical lab: Modeling a product metric, generating conversational queries, and verifying outcomes
- Assembling a self-service dashboard with user guidance
- Presentations, peer feedback, and defining next-step action plans
Conclusion and Future Actions
Requirements
- Familiarity with product metrics and key performance indicators (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
