The Pizza Lounge sales analysis project involved loading and cleaning data into Power BI to gain insights into customer preferences, peak hours, and sales trends. We found that the Classic category of pizzas was the most popular, peak hour was in the afternoon, and Friday was the busiest day of the week.
The project initiates with the acquisition of raw sales data from diverse sources like databases, CSV files, or other data repositories. This data encompasses crucial information such as customer orders, product details, order dates, and transaction amounts.
SQL (Structured Query Language) plays a pivotal role in the data transformation phase. It is utilized to clean, filter, and reshape the raw data into a structured format suitable for analysis. Tasks include joining tables, aggregating data, handling missing values, and creating new calculated fields.
Subsequent to data preparation, comprehensive data analysis is conducted via SQL queries. This analysis encompasses various aspects such as:
Analyzing sales trends over time. Evaluating customer demographics and preferences. Calculating average order values. Assessing the performance of individual restaurant locations.
Power BI serves as the platform for creating interactive and insightful visualizations that portray the findings from the data analysis. These visualizations may include:
- Bar charts and pie charts showcasing sales by product category.
- Time series charts depicting sales trends.
- Geographic maps illustrating the distribution of restaurant locations.
- Dashboards summarizing key performance indicators (KPIs) for easy interpretation and decision-making.