Library Management Data Analysis using SQL

Managing a library efficiently requires a structured approach to organizing books, tracking borrowings, and analyzing user behavior. A well-designed Library Management System (LMS) helps streamline these processes by maintaining comprehensive records of books, borrowers, transactions, and fines.

In this article, we will explore a dataset schema for a library management system, covering key attributes such as book details, borrower information, and transaction history. Additionally, it delves into Exploratory Data Analysis (EDA) using SQL where various queries extract insights on book genres, borrowing patterns, overdue books, and fines collected.

Library Management Data

• Book_ID (Unique ID for books): A unique identifier assigned to each book in the library system.
  
• Title (Book title): The name of the book being managed in the system.
  
• Author (Author's name): The name of the author or authors who wrote the book.
  
• Genre (Book category): The classification of the book, such as Fiction, Non-fiction, Mystery, Science, etc.
  
• ISBN (International Standard Book Number): A unique identifier assigned to the book by publishers for global recognition.
  
• Publication_Year (Year of publication): The year in which the book was published.
  
• Publisher (Publishing house): The company or individual responsible for publishing the book.
  
• Copies_Available (Number of available copies): The total number of copies of the book available for borrowing in the library.
  
• Borrower_ID (Unique ID for borrowers): A unique identifier for each borrower who uses the library services.
  
• Borrower_Name (Name of the person borrowing the book): The full name of the person borrowing the book.
  
• Borrower_Age (Age of borrower): The age of the borrower who has checked out the book.
  
• Borrower_Gender (Male/Female/Other): The gender of the borrower (Male, Female, or Other).
  
• Membership_Type (Regular/Premium): The type of library membership, either Regular or Premium, which may offer different privileges.
  
• Issued_Date (Date when the book was issued): The date on which the book was borrowed by the user.
  
• Due_Date (Due date for return): The date when the borrowed book is expected to be returned to the library.
  
• Return_Date (Date of book return): The actual date when the book was returned by the borrower.
  
• Fine_Amount (Late return fine if applicable): The amount charged if the book is returned after the due date.

This schema should provide you with a solid foundation for the Library Management System dataset for your data analysis project.

Exploratory Data Analysis Using SQL

The library management system stores information about books, borrowers, fines, and borrowing transactions. To help library administrators make data-driven decisions, the following queries are designed to extract insights on book genres, borrower behavior, fines, and overdue books. Let's perform some SQL queries and find quick overview as defined below:

1. Total Books by Genre

Query:

SELECT Genre, COUNT(*) AS Total_Books
FROM library_management
GROUP BY Genre
ORDER BY Total_Books DESC
LIMIT 5;

Output:

Explanation: This query groups books by genre, counts how many books belong to each genre, and sorts them in descending order. The top 5 genres with the highest book count are displayed. This helps administrators understand which genres are most prevalent in the library and can inform decisions on inventory and acquisitions.

2. Top 5 Borrowers Who Borrowed the Most Books

Query:

SELECT Borrower_Name, COUNT(*) AS Books_Borrowed
FROM library_management
GROUP BY Borrower_Name
ORDER BY Books_Borrowed DESC
LIMIT 5;

Output:

Explanation: This query counts the number of books each borrower has taken and lists the top 5 borrowers who borrowed the most books. This data can be used to identify frequent borrowers, enabling the library to tailor its communication and services to this key group.

3. Total Fines Collected

Query:

SELECT SUM(Fine_Amount) AS Total_Fines FROM library_management;

Output:

Explanation:

This query calculates the total fines collected from all overdue books by summing up the Fine_Amount column. It provides the library with an understanding of its revenue from fines and helps assess if the fine policies are effective in ensuring timely returns.

4. Most Borrowed Books

Query:

SELECT Title, COUNT(*) AS Borrow_Count
FROM library_management
GROUP BY Title
ORDER BY Borrow_Count DESC
LIMIT 5;

Output:

Explanation: This query finds the books that have been borrowed the most times, displaying the top 5. By identifying the most popular books, libraries can make informed decisions about restocking and promoting these books, potentially improving user satisfaction and engagement.

5. Overdue Books

Query:

SELECT Title, Borrower_Name, Due_Date, Return_Date
FROM library_management
WHERE Return_Date > Due_Date
ORDER BY Return_Date DES
CLIMIT 5;

Output:

Explanation: This query retrieves the titles of books that have been returned after their due date, showing the borrower’s name, due date, and return date. It helps the library track overdue books, identify borrowers with recurring overdue patterns, and implement more effective return policies.

Advanced-Data Analysis with Library Management Dashboard

A well-designed Power BI dashboard enables efficient visualization and analysis of library management data. This dashboard includes KPIs, bar charts, a pie chart, and slicers to derive actionable insights.

1. Key Performance Indicators (KPIs)

KPIs are essential for evaluating library performance and user engagement. The four primary KPIs in this dashboard are:

Total Books Borrowed

This KPI represents the total number of books borrowed by users. It is calculated by summing up the total borrow transactions in the dataset. Monitoring total book borrowings helps in understanding the popularity of the library and user engagement over time.

Total Active Borrowers

This KPI shows the total number of unique borrowers who have borrowed at least one book. It is determined by counting distinct user IDs in the dataset. Understanding the number of active borrowers helps the library measure its outreach and engagement with readers.

Average Fine Per Borrower

This KPI represents the average fine imposed on each borrower. It is calculated by dividing the total fine collected by the number of active borrowers. Tracking this metric helps in identifying overdue book patterns and implementing policies to minimize fines.

Books Overdue Rate

This KPI measures the percentage of books returned after their due date. It is calculated by dividing the number of overdue books by the total number of borrowed books, then multiplying by 100. Understanding the overdue rate helps libraries enforce return policies and optimize lending durations.

2. Dashboard Creation

The Power BI dashboard includes various visualizations to present data effectively:

Total Books by Genre (Bar Chart)

This visualization displays the total number of books available in each genre. The X-axis represents different genres. The Y-axis shows the count of books in each genre. Each bar represents a genre, and its height indicates the number of books available. This chart helps in:

• Identifying the most and least popular book genres in the library collection.
• Analyzing trends in user preferences to enhance the book acquisition process.
• Making informed decisions about expanding specific book categories based on demand.

Most Borrowed Books (Pie Chart)

A pie chart is used to represent the books that have been borrowed the most. Each section of the pie represents a specific book, with the size indicating the proportion of total borrowings. This visualization helps in:

• Identifying the most popular books among readers.
• Understanding book demand to manage inventory effectively.
• Determining which books should be restocked or promoted to new users.

Slicer for Interactive Analysis Due Date Slicer

Slicers allow users to interactively filter data for detailed analysis. The key slicer included is:

Due Date Slicer: This slicer enables users to filter borrow records based on due dates. Users can select a specific time range to analyze overdue trends and book return patterns. This feature helps in understanding borrowing behaviors over different periods and implementing strategies to reduce overdue books.

Conclusion

Power BI provides a comprehensive platform for analyzing library data, helping librarians monitor borrowing trends, user engagement, and inventory management. Library professionals can optimize operations and improve user satisfaction by utilizing interactive features like the due date slicer and visualizations such as bar charts and pie charts.