Focused on identifying Preferred payments methods and high selling menu items/orders to optimize staffing and inventory.

About the dataset used.
It is a sales data of a restaurant company operating in multiple cities in the world. It contains
information about individual sales transactions, customer demographics, and product details. The
data is structured in a tabular format, with each row representing a single record and each column
representing a specific attribute. This dataset can be commonly used for business intelligence, sales
forecasting, and customer behaviour analysis.
Classified as Internal use only
These are the main Features/Columns available in the dataset:

1. Order ID: A unique identifier for each sales order. This can be used to track individual transactions.
2. Order Date: The date when the order was placed. This column is essential for time-series analysis,
   such as identifying sales trends over time or seasonality.
3. Product: The name or type of the product sold. This column is crucial for analysing sales
   performance by product category.
4. Price: The unit price of the product. This, along with ‘Quantity Ordered’, is used to calculate the
   total price of each order.
5. Quantity: The number of units of the product sold in a single order. This is a key metric for
   calculating revenue and understanding sales volume.
6. Purchase Type: The order was made online or in-store or drive-thru.
7. Payment Method: How the payment for the order was done.
8. Manager: Name of the manager of the store.
9. City: The location of the store. This can be used for geographical analysis of sales, such as
   identifying top-performing regions or optimizing logistics.
   Using this dataset, I answered multiple questions with Insights using power bi tool.
   Q.1) Most Preferred Payment Method?
   Q.2) Most Selling Product - By Quantity & By Revenue?
   Q.3) Which city had maximum revenue and Which Manager earned maximum revenue?
   Q.4) Average Revenue of November & December month.
   Q.5) Is revenue increasing or decreasing over time?
   Q.6) Average ‘Quantity Sold’ & ‘Average Revenue’ for each product?
   Q.7) Total number of orders
   Q.8) Total Revenue

Developed a real-time BI solution to monitor and analyse Value-Added Services (VAS) across major
telecom providers (MTN & Airtel). I engineered a pipeline that converted millions of raw SQL rows
into a high-level strategic tool for monitoring liquidity and transaction health.
Key Technical Contributions:
• ETL & Data Engineering: Leveraged SQL for complex data extraction and used Power Query
to standardize inconsistent date formats and status logs from multiple telecommunications
transaction in databases.
• Multi-Channel Analysis: Visualized “Account-to-Wallet” flows and utility payments (TV, URA,
Electricity, Water), providing a 360-degree view of digital revenue.
• Failure Rate Monitoring: Designed a “Root Cause” tracker to identify peak failure hours,
allowing technical teams to reduce “Pending” transactions and improve user experience.
• Stakeholder Impact: Enabled leadership to identify “Top N” revenue drivers and peak
service hours, shifting the team from manual daily reporting to automated, actionable
insights.
VAS Reporting Project
• Automated Daily Reporting: Developed a SQL-to-Power BI pipeline to track daily VAS
transactions, reducing reporting time by 95%.
• Transactional Integrity: Standardized status tracking for MTN & Airtel (Wallet-to-Account,
Airtime, Utilities), ensuring 100% data accuracy for successful vs. pending transactions.
• Operational Insights: Identified peak transaction windows and failure trends, providing a
“why” behind system downtime to support technical infrastructure upgrades.
• Revenue Growth: Built “Top N” product visualizations that helped stakeholders prioritize
high-margin services like ATW, Airtime top-ups, URA and Electricity payments.