<![CDATA[In today's digital era, efficient information management is key to a business's operational success. In the sales sector, high transaction volumes generate vast amounts of data daily. However, many sales systems still face a classic data architecture problem: data redundancy, or unnecessary duplication of information. Data redundancy is not simply a matter of inefficient storage; it directly impacts the emergence of data anomalies. When the same data is stored in multiple locations, the risk of information inconsistency during updates, deletions, and insertions is very high. Inconsistent data can mislead management in decision-making, for example, errors in inventory reports or financial recapitulations. The main objective of this research is to eliminate data redundancy and prevent anomalies in the sales system to ensure information integrity. Through systematic table organization, this research seeks to create a more efficient and accurate database architecture to support management decision-making. The method used is structured analysis through data decomposition stages. The process begins with collecting raw (unnormalized) transaction data, which is then processed through the 1NF, 2NF, and 3NF stages. The final stage involves validating the Create, Read, Update, and Delete (CRUD) functions to ensure the consistency of the new schema. The desired result is an optimal relational database schema, where data is logically distributed into specific, interconnected tables. This implementation will result in a lighter system, efficient storage usage, and error-free sales reporting.]]>
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