Analytics Navigator: AI Question Formulation Partner

A data-driven insight framework using AI as a thinking partner to guide question development, research context, and formulate SQL queries.

Problem Statement

Extracting meaningful insights from data often hinges on asking the right questions. However, users may lack familiarity with the database structure, be unaware of relevant external context (like market trends), or struggle to translate their business needs into effective technical queries (e.g., SQL). This system addresses these challenges by acting as a collaborative partner, guiding the user through a structured process to formulate better questions and retrieve data-driven answers.

Diagrams & Visuals

Note: The diagram outlines the structured process from understanding data structure and external context to generating actionable insights.

Demo Video

Note: The video walks through the application's steps, showing how it guides the user from initial data exploration to generating and executing a specific analytical query based on strategic focus areas.

Results & Reflections

This project demonstrates a novel approach to data analysis, shifting the focus towards collaborative question formulation with AI. Key reflections and potential impacts:

• Improved Analysis Quality: By guiding users to consider data structure and external context before asking questions, the system has the potential to significantly improve the relevance and impact of data analysis.
• Empowerment of Users: Bridges the gap between business questions and technical queries, enabling a wider range of users to engage directly with data.
• Structured Thinking: Enforces a logical workflow, promoting more systematic and thorough data exploration compared to ad-hoc querying.
• Value of Context: Highlights the importance of integrating external information (market trends) to avoid analyzing internal data in a vacuum.
• Agentic Workflows: Showcases the power of multi-step, agentic workflows (using LangGraph) where AI tools collaborate to achieve a complex goal.
• Efficiency Potential: While adding steps initially, this guided process can reduce time wasted on poorly formulated queries or analyses lacking strategic context.