📊 SQL-Driven Ad-hoc Business Insights | AtliQ Hardware (FMCG)

This Project is a part of Codebasics Resume Project Challenge #4
This project contains 10 real-world ad-hoc business requests solved using SQL for AtliQ Hardware, a global FMCG company(Imaginery Company).
The analysis simulates how data analysts answer stakeholder requests and provide actionable insights.

⚠️ Note: Raw datasets are confidential and not shared. Only SQL queries, schema documentation, results, and summaries are provided.

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📑 Table of Contents

• 📌 Project Overview
• 🛠️ Tech Stack
• 🔎 Methodology
• 📂 Repository Structure
• 📸 Reports Preview
• 📈 Key Takeaways
• 📈 Key Learnings
• 🚀 Results Delivered
• 📑 Deliverables
• 🏁 Conclusion
• 🧑‍💻 Author

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📌 Project Overview

Business stakeholders from Sales, Finance, Marketing, and Supply Chain raised 10 ad-hoc requests to address questions such as:

• Which APAC markets should we prioritize for AtliQ Exclusive?
• How many unique products were launched YoY?
• Which customers receive the highest discounts?
• Which channels and products drive most of the sales?

This project demonstrates how to convert business requests into SQL queries, extract insights, and deliver data-driven recommendations.

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🛠️ Tech Stack

• SQL (MySQL/PostgreSQL) → Querying, joins, aggregations, window functions
• Schema Documentation → Fact & dimension table mapping
• Excel / CSV Exports → Tabular summaries
• PowerPoint → Final presentation (in reports_data/SQL_DRIVEN_ADHOC_BUSINESS_INSIGHTS.pptx)
• GitHub → Version control & portfolio hosting

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🔎 Methodology

The approach followed the business analytics workflow:

1. Understand the request
   • Translate stakeholder questions into precise problem statements.
2. Explore schema
   • Identify required tables (fact & dimension).
   • Document schema (schema.md).
3. Write SQL queries
   • Each request solved in a separate .sql file (queries ).
   • Comments explaining objective, inputs, expected output, insights, and recommendations.
4. Validate outputs
   • Review query results for accuracy (counts, aggregates, consistency).
5. Summarize insights
   • Convert raw SQL results into business insights (reports_data/insights_summary.md).
6. Visualize findings
   • Build Power BI visuals (tables, charts) from SQL outputs.
7. Deliver recommendations
   • Strategic takeaways compiled into a presentation (reports_data/SQL_DRIVEN_ADHOC_BUSINESS_INSIGHTS.pptx).

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📂 Repository Structure

SQL_Driven_Ad-hoc_Business_Insights/
│
├── queries/                     # Individual SQL files (one per ad-hoc request)
│   ├── 01_apac_exclusive.sql
│   ├── 02_unique_products_yoy.sql
│   ├── 03_products_by_segment.sql
│   ├── ...
│   └── 10_top_products_division.sql
│
├── schema.md
|                  # Database schema documentation
├── result_images/
│   ├── star_schema_atliq.png    # ERD diagram (visual schema)
|   ├── adhoc_1.png              # output visual
|   ├── adhoc_7.png                   :
|   ├── adhoc_8.png              # output visual   
|
├── reports_data/
|   ├── SQL_PROJECT_PPT_ENHANCED.pptx # Presentation with insights
|   ├── insights_summary  # contains all the adhoc_request's objective, insights, recommendation etc
|
└── README.md                    # This file

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📸 Reports Preview

👉 Below are sample visuals created from SQL query outputs (for presentation purposes):

Ad-hoc_request_1:APAC Markets for "AtliQ Exclusive"

• Key Insight:
  • Mapped "AtliQ Exclusive" presence across APAC countries to reveal geographic coverage and white-space opportunities.*
• Recommendation:
  • Use findings to guide strategic decisions on strengthening operations and exploring underpenetrated APAC markets.*

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Ad-hoc_request_7:Monthly Gross Sales for "AtliQ Exclusive"

• Key Insight:
  • Sales collapsed during Apr–May 2020 due to COVID disruption.
  • Explosive rebound in Nov 2020 (> $20M) during festive season.
  • FY2021 stabilized at ~$10–13M/month with occasional dips (e.g., Apr 2021 ~$7M), showing resilience but volatility risk.*
• Recommendation:
  • Prioritize Q1 (Sep–Nov) campaigns to maximize festive peaks.
  • Strengthen resilience for Q3/Q4 to mitigate dips.
  • Use post-2020 recovery momentum to drive retention programs and explore new APAC market opportunities.*

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Ad-hoc_request_8:Quarter with Total Sold Quantity (FY2021) *Key Insight:

• Q1 (Sep–Nov 2020) showed the highest sold quantities, driven by festive season demand and recovery momentum after COVID disruptions.
• Other quarters remained steady at lower levels, with occasional dips (e.g., Q3 Apr–Jun 2021).*

• Recommendation:
  • Prioritize inventory build-up and marketing spend in Q1.
  • Explore strategies to boost Q3 demand where volumes dip.
  • Use seasonal trend data to improve forecasting accuracy.*

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📈 Key Takeaways

• APAC Market Expansion: Regional growth opportunities identified for AtliQ Exclusive.
• Portfolio Growth: Unique product count grew by +36% YoY.
• Seasonality: Clear sales peaks in Q1 (festive season), requiring proactive inventory planning.
• Channel Risk: Over-dependence on Retail channel highlights need for diversification.

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📈 Key Learnings

• How to translate vague business questions into precise SQL queries.
• Designing joins, aggregations, CTEs, and window functions to solve real-world problems.
• Importance of clear documentation and storytelling for stakeholders.
• Building a complete end-to-end analytics case study for portfolio presentation.

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🚀 Results Delivered

• 10 SQL ad-hoc requests solved with clear documentation.
• Leadership-level insights on markets, products, segments, discounts, channels, and divisions.
• Recommendations to improve sales strategy, margin management, and product portfolio balance.

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📑 Deliverables

• SQL Queries → /queries/
• Schema Documentation → /data/schema.md
• Insights Summary → /reports/insights_summary.md
• Final Presentation → /reports/SQL_PROJECT_PPT_ENHANCED.pdf

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🏁 Conclusion

This project demonstrates the end-to-end business analytics workflow: from translating stakeholder requests, querying data with SQL, validating outputs, and extracting insights, to creating Power BI visuals that highlight Sales and Finance performance.

By combining SQL-driven analysis with clear visual storytelling, the project showcases the ability to:

• Identify revenue drivers and risks.
• Quantify performance gaps (e.g., sales growth vs. profit decline).
• Communicate insights in a way that supports data-driven decision-making.

It highlights strong skills in SQL, Power BI, and business analytics, making it a portfolio-ready project for Data Analyst / BI Analyst roles.
👉 This project simulates a real FMCG business scenario, showing how data analysts bridge stakeholder needs with actionable insights.

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🧑‍💻 Author

Mohammad Navaman Jamadar
Data Analyst & Machine Learning Practitioner

• 📌 Skills: SQL, Power BI, Excel, Python, Data Analysis
• 🔗 LinkedIn Profile | GitHub Portfolio | Portfolio

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