Course Outline
Introduction to Conversational Analytics
- Defining conversational analytics and its significance for product teams.
- Overview of WrenAI’s core capabilities and architectural design.
- Common product team workflows facilitated by Wren AI.
Data Source Connectivity and Access
- Supported data sources and ingestion methodologies.
- Managing data access, permissions, and multi-source joins.
- Best practices for utilizing sample datasets and sandbox environments.
Semantic Modeling and Metrics Standardization
- Constructing a metrics layer with canonical definitions.
- Developing reusable metrics and dimensions for product analytics.
- Implementing versioning and governance for the semantic model.
Natural-Language to SQL Workflows
- The process of WrenAI translating NL queries into SQL, along with validation strategies.
- Prompting techniques and fallback mechanisms for product-related questions.
- Managing ambiguity, clarifying user intent, and designing effective queries.
Self-Service BI and Embedded Applications
- Designing conversational dashboards and templates tailored for product teams.
- Integrating Wren AI into product workflows and internal tooling.
- Assessing the adoption rate and impact of self-service analytics.
Quality Assurance, Evaluation, and Guardrails
- Testing the accuracy of NL-to-SQL conversions and building validation suites.
- Monitoring model drift, data quality indicators, and conducting query audits.
- Ensuring safety, access control, and enforcing business-rule guardrails.
Workshop: Constructing a Product Insights Flow
- Hands-on lab: Modeling a product metric, generating conversational queries, and validating outcomes.
- Building a self-service dashboard with accompanying user guidance.
- Presentations, peer feedback, and development of next-step action plans.
Summary and Future Steps
Requirements
- A solid grasp of product metrics and KPIs.
- Prior experience with data analysis or BI tools.
- Basic proficiency in SQL, which is highly advantageous.
Target Audience
- Product managers.
- Data analysts.
- Data champions operating within various 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
