Application of Database Normalization in Increasing Data Storage Efficiency - Hardini, Marviola; Agarwal, Vertika; Apriani, Desy; Widjaya, Irene Apriani; Setiawaty, Elika; Nurasiah, Nurasiah (Pandawan Sejahtera Indonesia, 2025)
Database normalization is a key process in relational database design that reduces redundancy and ensures data integrity. As data volumes increase, maintaining efficient and consistent storage becomes critical. This study investigates the application of normalization techniques from First Normal Form (1NF) to Third Normal Form (3NF) on a sample inventory database to evaluate their impact on storage efficiency. The process focuses on eliminating data repetition and optimizing table structures to enhance performance. Experimental results show that normalization reduces database size by approximately 30%, significantly minimizing redundancy. Smaller, more organized tables improve storage utilization, especially in large-scale systems. However, normalization can introduce query complexity due to increased joins, potentially affecting execution time. Despite this, the trade-off is considered acceptable given the gains in data integrity and storage optimization. This research emphasizes the value of normalization for scalable and maintainable systems. It also aligns with Sustainable Development Goals (SDGs), particularly Goal 9 (Industry, Innovation, and Infrastructure) and Goal 12 (Responsible Consumption and Production), by promoting efficient digital infrastructure and responsible data management practices. These improvements contribute to more sustainable, cost-effective systems in industries relying on large-scale data, such as e-commerce, healthcare, and finance. In conclusion, normalization is an essential tool for optimizing storage and ensuring data consistency in relational databases. Although performance trade-offs exist, they can be mitigated through indexing and query optimization. The study offers insights for database designers seeking to balance efficiency and system performance in data-intensive environments.