Course Outline
Introduction to Conversational Analytics
- Defining conversational analytics and its significance for product teams.
- Key features and high-level architecture of WrenAI.
- Common product team workflows facilitated by Wren AI.
Data Source Connectivity and Access
- Supported data sources and ingestion methods.
- Data access control, permissions, and multi-source joining.
- Best practices for handling sample datasets and sandboxing.
Semantic Modeling and Metrics Standardization
- Structuring a metrics layer with canonical definitions.
- Developing reusable metrics and dimensions for product analytics.
- Version control and governance of the semantic model.
Natural-Language to SQL Workflows
- The mechanism by which WrenAI converts NL queries to SQL, along with validation strategies.
- Prompting techniques and fallback mechanisms for product questions.
- Managing ambiguity, clarifying questions, and intent design.
Self-Service BI and Embedded Use Cases
- Creating conversational dashboards and templates tailored for product teams.
- Integrating Wren AI into product workflows and internal tools.
- Tracking the adoption and impact of self-service analytics.
Quality Assurance, Evaluation, and Guardrails
- Testing NL-to-SQL accuracy and constructing validation suites.
- Monitoring drift, data quality indicators, and query audits.
- Safety protocols, access controls, and business-rule guardrails.
Workshop: Creating a Product Insights Flow
- Practical lab: Modeling a product metric, generating conversational queries, and verifying results.
- Building a self-service dashboard with user guidance.
- Presentations, peer feedback, and defining next-step action plans.
Summary and Next Steps
Requirements
- A solid grasp of product metrics and 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
