Lately, I’ve been diving deep into SQL, moving past the basics to master intermediate and advanced concepts like Common Table Expressions CTEs, generated columns, and complex multi table joins.
To put my learning into practice, I worked with a database blueprint modeled after a real world fashion retail brand in Bangladesh. I have been working for almost a month as a Trainee Business Analyst at one of the largest players in the industry, serving both local and export markets. One thing I noticed is that fashion retail brands still rely heavily on third party systems like Mediasoft for data management.
As someone passionate about data, I find it limiting to depend on external systems where I need to extract data manually, then clean and analyze it in Excel. That said, Excel still provides a fast, flexible environment and remains highly effective for communicating insights to management, which I genuinely enjoy working with.
However, I do not want to limit myself to Excel. As a data professional, I aim to build strong capability across tools. I already have a solid foundation in Excel, Python, R and Power BI. SQL was not part of my academic coursework, but I realized its importance in a tech driven job market. That is why I started learning SQL after graduation, to ensure I grow beyond tools and build a more complete analytical skillset.
As a Business Analyst, my goal was not just to write queries, but to solve real business challenges. Here is a snapshot of the operational insights I extracted:
- Inventory and Capital Optimization: Using multi table CTEs, I calculated net available stock across outlets and mapped it against pricing to track total cost investment versus potential revenue at MRP.
- Dynamic Customer Recency Metrics: Built a dynamic script using PostgreSQL AGE and CURRENT_DATE functions to calculate accurate purchase recency directly from transactional data.
- Customer Lifetime Value CLV: Analyzed purchase volume and margins to identify repeat buyer behaviour and align them with CRM loyalty tiers.
- Data Pipeline Automation: Implemented stored generated columns to automatically calculate revenue and discount percentage at ingestion level.
- Team Productivity: Mapped sales performance to individual designers and handled missing data efficiently using COALESCE.
- Category Structuring: Created subcategories from parent categories to improve product level analysis, reporting clarity, and merchandising insights.
Every script and analysis I build is driven by one core question: how can data help run a retail business better? This journey of learning and applying has been incredibly rewarding, and I am continuously pushing myself to go deeper.
#SQL #BusinessAnalytics #DataAnalysis #RetailAnalytics #CTEs #EDA
Shaikh Ahmed
Statistician & Data Analyst | Trainee, Business Analyst - Dekko ISHO Group