Course Outline
Introduction to Conversational Analytics
- The definition of conversational analytics and its significance for product teams.
- Core capabilities of WrenAI and an overview of its architecture.
- Common product team workflows facilitated by Wren AI.
Connecting Data Sources and Access
- Supported data sources and ingestion methods.
- Data access controls, permissions, and joining multiple sources.
- Best practices for utilizing sample datasets and sandbox environments.
Semantic Modeling and Metrics Standardization
- Establishing a metrics layer with canonical definitions.
- Creating reusable metrics and dimensions for product analytics.
- Versioning and governance of the semantic model.
Natural-Language to SQL Workflows
- How WrenAI converts NL queries into SQL and the associated validation strategies.
- Prompting techniques and fallback mechanisms for product inquiries.
- Managing ambiguity, clarifying questions, and designing user intent.
Self-Service BI and Embedded Use Cases
- Designing conversational dashboards and templates tailored for product teams.
- Embedding Wren AI into product workflows and internal tools.
- Evaluating the adoption rate and impact of self-service analytics.
Quality, Evaluation, and Guardrails
- Testing the accuracy of NL-to-SQL conversions and developing validation suites.
- Monitoring data drift, quality signals, and conducting query audits.
- Ensuring safety, access control, and enforcing business-rule guardrails.
Workshop: Build a Product Insights Flow
- Hands-on lab: Modeling a product metric, generating conversational queries, and validating outcomes.
- Assembling a self-service dashboard with user guidance.
- Presentations, peer feedback, and outlining 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
