Mission: SQL + AI Promptathon Submission- Principal Data Analyst: Product Quality Risk Investigation

Mission/open goal Description

Principal Data Analyst
Investigate a hidden product-quality issue by analyzing sales, support tickets, customer chat conversations, and product-related documents in the Promptathon database. Using GitHub Copilot Agent and SQL, the objective was to identify products with strong sales but unusually high support burden and low customer satisfaction, determine the root causes through evidence-based analysis, and provide actionable business recommendations.

Harness and model

GitHub Copilot Agent with GPT-5.5

Turn-by-turn journey

1. Prompt

Prompt: Read the Promptathon repository, explain the available missions, and recommend the best mission for a beginner.

Agent response/action: Reviewed the README and mission files, compared all available missions, and recommended the Principal Data Analyst mission.

Result: Selected the Data Analyst mission and understood the investigation workflow.

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2. Prompt

Prompt: Guide me through the Data Analyst mission and inspect the available SQL database.

Agent response/action: Connected to the Promptathon SQL database using SQL MCP tools, listed available tables, and inspected the schema.

Result: Identified core tables including Products, SalesOrders, SalesOrderLines, SupportTickets, SupportChats, Docs, Customers, and Employees.

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3. Prompt

Prompt: Identify the top-selling products based on revenue and quantity sold, then compare them with support tickets and customer satisfaction.

Agent response/action: Generated and executed SQL queries to aggregate sales data and correlate it with support ticket volume and satisfaction scores.

Result: Identified the Premium Short Sleeve Men's Top (SKU: ZCPTM-SS-M-BW) as the strongest product-quality risk candidate.

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4. Prompt

Prompt: Investigate the root cause using support chats and customer documents.

Agent response/action: Analyzed support chat conversations and customer documents to identify recurring complaint themes.

Result: Found repeated complaints related to connectivity failures, wash durability, sensor issues, and customer returns.

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5. Prompt

Prompt: Prepare a complete Promptathon submission with findings, SQL evidence, recommendations, architecture, and reflections.

Agent response/action: Generated a structured Markdown report summarizing the investigation, evidence, business recommendations, and lessons learned.

Result: Produced a complete GitHub Issue submission ready for review.

Completion

• Yes, the agent completed the mission or goal.
• No, the agent did not complete the mission or goal.

Bonus work

Beyond the core mission, I validated the AI-generated insights by reviewing SQL query outputs before drawing conclusions. I combined structured SQL analysis with qualitative evidence from support chats and customer documents to strengthen the investigation. I also documented the complete analytical workflow, business recommendations, and a Mermaid architecture diagram to produce a professional, evidence-based submission.

Start with the repository README and mission files, then review the SQL schema and outputs for Products, SalesOrders, SalesOrderLines, SupportTickets, SupportChats, Docs, Customers, and Employees. Verify that the reported product-quality findings, supporting evidence, recommendations, and Mermaid architecture diagram are complete and consistent; the submission is done when the evidence-based report is ready for review.