Multinational Tour Operator Agency Gains New Business

A Multinational Tour Operator Agency Has Gained New Business Growth In

A multinational tour operator agency has experienced significant business growth in the North American market through social media marketing. Their operations expanded by 50% within six months, underscoring the need for an improved data management strategy to sustain this growth. Currently, their reservation processing system relies on a limited data repository with inadequate business intelligence and reporting capabilities. To address this, they require a comprehensive proposal for enhancing their database management, focusing on designing a normalized data structure, creating an Entity Relationship Model (ERM), and implementing optimized SQL queries and stored procedures. This proposal aims to facilitate accurate sales forecasting, efficient reporting, and streamlined data operations.

Paper For Above instruction

Introduction

The rapid expansion of the multinational tour operator’s business calls for a robust, scalable, and efficient database management system (DBMS). Proper data organization is critical for delivering insightful analytics, supporting sales forecasting, and ensuring operational efficiency. Current challenges stem from a non-normalized data structure that leads to redundancy, inconsistency, and difficulty in maintaining data integrity. This paper proposes an optimized database design following normalization principles, details the creation of an Entity Relationship Model (ERM), and discusses the implementation of SQL queries, triggers, and stored procedures to enhance database functionality and performance.

Understanding the Existing System

The current system comprises three main tables: salespersons, customer data, and tour rates. The salespersons table stores employee information and a manually updated "Tours sold" field, which is prone to errors and lacks real-time accuracy. The customer data table records customer details and booked tours; however, it can contain duplicate entries for customers booking multiple tours, leading to data redundancy. The tour rate table maintains seasonal rates varying every three months, but without normalization, updating rates is inefficient and error-prone.

Normalization Process and Data Model Design

Normalization is a systematic approach to organizing data to reduce redundancy and dependency. To achieve at least Second Normal Form (2NF), the process involves:

Step 1: Define Entities and Primary Keys

Identify distinct entities: Tour, Customer, Salesperson, and Booking. Assign primary keys: TourID, CustomerID, SalespersonID, BookingID.

Step 2: Eliminate Partial Dependencies

Separate data about tours and rates into a dedicated table (TourRate) linked to Tour via TourID to handle seasonal pricing. Customer bookings linked to customers and tours via BookingID, avoiding duplication of customer data for multiple bookings.

Step 3: Establish Relationships and Foreign Keys

Design relationships where each booking references one customer, one tour, and one salesperson. The Staff (Salesperson) table maintains individual performance data, with the total tours sold stored distinctly for aggregation.

Proposed Data Structure: Entities and Attributes

  • Tour: TourID, TourName, Description
  • TourRate: RateID, TourID, Season, CostPerPerson
  • Customer: CustomerID, Name, Address, City, State, ZipCode
  • Booking: BookingID, CustomerID, TourID, SalespersonID, TourDate, NumberOfPersons, TotalAmount, PaymentDueDate
  • Salesperson: SalespersonID, FirstName, LastName, ToursSold

Entity Relationship Model (ERM)

The ERM diagram depicts:

  • A one-to-many relationship between Tour and TourRate (one Tour has many Rates).
  • A many-to-one relationship from Booking to Customer, Tour, and Salesperson tables.
  • Each booking references one customer, one tour, and one salesperson, establishing referential integrity.

Graphical tools such as Microsoft Visio or Dia can visualize these relationships effectively, ensuring clarity in database architecture.

SQL Query for Payment Due Calculation

To determine the number of days until payment is due if the total amount is within 45 days, a SQL query can be constructed as follows:



SELECT CustomerID, Name, TotalAmount, PaymentDueDate, 


DATEDIFF(day, GETDATE(), PaymentDueDate) AS DaysUntilDue


FROM Customer


WHERE DATEDIFF(day, GETDATE(), PaymentDueDate) 

AND TotalAmount > 0;

This query calculates the days remaining until the invoice payment date for customers with due dates within 45 days.

Trigger for Updating Tours Sold

A trigger in SQL can automatically increment the 'ToursSold' field for a salesperson each time a new booking is inserted:



CREATE TRIGGER trg_UpdateToursSold


ON Booking


AFTER INSERT


AS


BEGIN


UPDATE Salesperson


SET ToursSold = ToursSold + 1


WHERE SalespersonID IN (SELECT SalespersonID FROM inserted);


END;

This trigger ensures the 'ToursSold' field remains accurate without manual updates, facilitating real-time performance tracking.

Query for Customer Count per Salesperson

A query to count the number of customers each salesperson has sold tours to:



SELECT s.SalespersonID, s.FirstName, s.LastName,


COUNT(DISTINCT b.CustomerID) AS CustomerCount


FROM Salesperson s


JOIN Booking b ON s.SalespersonID = b.SalespersonID


GROUP BY s.SalespersonID, s.FirstName, s.LastName;

This provides insights into each salesperson's customer engagement levels.

The Role of Stored Procedures in Database Optimization

Stored procedures are precompiled SQL statements stored within the database that optimize transaction performance by reducing the need for repeated SQL parsing and compilation. They enhance security by encapsulating logic and allow for complex operations to execute efficiently, minimizing network traffic. Using stored procedures also ensures consistency in data operations, improved maintainability, and easier updates, critical for large-scale or growing systems such as this tour operator's database. For example, a stored procedure can handle booking insertions, apply discounts, and update tour sales metrics in a single atomic operation, maintaining data integrity and performance (Elmasri & Navathe, 2015).

Conclusion

This proposal highlights the necessity of normalization in designing a scalable and efficient database system for the multinational tour operator. By transitioning to a structured, normalized data model and establishing clear relationships through an ERM, the organization can improve data accuracy and reporting capabilities. Implementing SQL queries, triggers, and stored procedures will further streamline operations, supporting strategic decision-making and sustainable growth. This comprehensive approach ensures the database can adapt to business fluctuations, seasonal changes, and evolving operational needs, fostering continued success in the competitive travel industry.

References

  • Elmasri, R., & Navathe, S. B. (2015). Fundamentals of Database Systems (7th ed.). Pearson.
  • Hernández, M. J., & Kakas, A. (2018). Entity relationship modeling: A systematic review. International Journal of Database Theory and Application, 11(4), 23-34.
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  • Silberschatz, A., Korth, H. F., & Sudarshan, S. (2011). Database System Concepts (6th ed.). McGraw-Hill.
  • Connolly, T., & Begg, C. (2014). Database Systems: A Practical Approach to Design, Implementation, and Management (6th ed.). Pearson.
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  • Ambler, S. (2022). Database normalization techniques for scalable systems. Data Engineering Journal, 15(1), 9-15.

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