Welcome to our music store data analysis project conducted using SQL! In this project, we explore an extensive dataset comprising 11 tables, including Employee, Customer, Invoice, InvoiceLine, Track, MediaType, Genre, Album, Artist, PlaylistTrack, and Playlist.
Through the application of SQL queries, our goal is to unravel valuable insights and answer critical questions about our music store's operations. By diving deep into the dataset, we aspire to gain a profound understanding and enhance decision-making processes within the realm of our music retail venture.
Our dataset consists of the following tables:
- Employee
- Customer
- Invoice
- InvoiceLine
- Track
- MediaType
- Genre
- Album
- Artist
- PlaylistTrack
- Playlist
We have employed various SQL queries to analyze the dataset and derive meaningful insights. Some of the key analyses include:
- Determining the senior most employee based on job title.
- Identifying countries with the most invoices.
- Finding the top 3 values of total invoices.
- Discovering the city with the best customers for promotional events.
- Determining the best customer based on total spending.
To replicate our analysis or explore the dataset further, you can use SQL queries to interact with the provided dataset. Simply clone or download this repository and run the SQL queries against your preferred SQL database management system.
Our data analysis provides valuable insights into our music store's operations, customer behavior, and revenue generation patterns. We hope this analysis contributes to informed decision-making and strategic planning within our organization.