Course Outline
Introduction to Conversational Analytics
- Defining conversational analytics and its relevance to product teams
- Overview of WrenAI's core capabilities and architecture
- Common product team workflows facilitated by Wren AI
Data Source Connection and Access
- Compatible data sources and ingestion methods
- Managing data access, permissions, and multi-source joins
- Best practices for handling sample datasets and sandbox environments
Semantic Modeling and Metrics Standardization
- Developing a metrics layer and defining canonical metrics
- Building reusable metrics and dimensions for product analytics
- Managing versioning and governance of the semantic model
Natural-Language to SQL Workflows
- How WrenAI translates NL queries into SQL and related validation techniques
- Prompting strategies and fallback mechanisms for product questions
- Managing ambiguity, clarifying questions, and intent design
Self-Service BI and Embedded Applications
- Creating conversational dashboards and templates tailored for product teams
- Integrating Wren AI into product workflows and internal tools
- Assessing the adoption rate and impact of self-service analytics
Quality, Evaluation, and Guardrails
- Verifying NL-to-SQL accuracy and creating validation suites
- Tracking drift, data quality indicators, and query audits
- Ensuring safety, access control, and adherence to business rules
Workshop: Constructing a Product Insights Flow
- Hands-on session: modeling product metrics, generating conversational queries, and validating outcomes
- Building a self-service dashboard with user guidance
- Presentations, feedback sessions, and planning next steps
Summary and Future Actions
Requirements
- Knowledge of product metrics and KPIs
- Familiarity with data analysis or BI tools
- Basic SQL knowledge is advantageous
Target Audience
- Product managers
- Data analysts
- Data champions within business units
Testimonials (4)
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
The diversity of topics covered
Romaric - Vacher
Course - Business Intelligence and Data Analysis with Metabase
Machine Translated
he was well prepared - and he is very sympathetic
Oliver - Post CH AG
Course - Splunk Fundamentals
lots of pratical exercises
