Potential Issues with Collecting and Storing Unnormalized Data

Collecting and storing unnormalized data in a business setting can lead to several potential issues:

Data Redundancy: Unnormalized data can result in redundant information being stored, leading to wasted storage space and increased maintenance efforts.

Data Inconsistency: Without normalization, data inconsistencies can arise, making it challenging to update, delete, or insert new data without affecting the overall integrity of the database.

Update Anomalies: Unnormalized data can lead to update anomalies, where updating one instance of data may not update all related instances, leading to inconsistencies.

Insertion Anomalies: Unnormalized data can result in insertion anomalies, where certain attributes cannot be inserted into the database without the presence of other attributes.

Deletion Anomalies: Unnormalized data can cause deletion anomalies, where deleting certain data may unintentionally remove other related data that should be preserved.

Complex Queries: Unnormalized data can make querying and retrieving specific information more complex and time$-$consuming, as it may require joining multiple tables or performing additional data manipulation.

Reduced Performance: Unnormalized data can lead to reduced database performance, as the system has to process and manage redundant and inconsistent data.

Data Integrity Issues: Unnormalized data can compromise data integrity, making it difficult to ensure that the data accurately represents the real$-$world entities it is supposed to model.

Scalability Challenges: Unnormalized data can pose challenges in scaling the database as the volume of data increases, leading to inefficiencies and potential performance bottlenecks.