Data redundancy is a situation in which there are multiple copies of the same data stored at different places in the database.
For example: When a customer came to a shop to buy something, and shop owner notes down the list of item every time and generate a bill for each product. Then this is very complex for both the customer and the shop owner to manage data. The shop owner wastes lots of bill paper for the customer and it is very difficult to keep all bills in an organized manner. Here situation of data redundancy occurred, and to overcome this redundancy customer created the bill with the customer name and mention all the items related to that customer in the same bill.
There are lots of problems caused by data redundancy in any database. These are as follows:
I) Insertion anomaly
If data redundancy is present somewhere in a database, then data anomaly or error has occurred while inserting some data that is related to data redundancy items. For example: if we want to insert new details of a student whose course is not yet confirmed in the table in the database, then the data of details cannot be inserted.
II) Deletion anomaly
If we try to delete some data items from a table in the database where redundancy of data is present, then the whole information related to that data is deleted which we don’t want to delete.
III) Updation anomaly
When we try to update some data in a data redundancy situation, then it will change or update all data at all places and for this, it will also take a long period of time to change all data.
No, we say that in the concept of DBMS maximum redundancy can be removed, but not all of that.
Can data redundancies be completely eliminated when the database approach is used?
While the database approach significantly reduces data redundancies through normalization and database management systems (DBMS) that control data integrity and avoid duplication, completely eliminating data redundancy is challenging. Some level of redundancy might be necessary for performance optimization, data recovery, and ensuring data is accessible for different purposes.
For example, redundant data can speed up query responses or be crucial for backup and recovery processes. So, while the database approach aims to minimize unnecessary redundancy, a certain amount might be intentionally kept for these practical reasons.