Difference between Data Redundancy and Data Inconsistency
Data Redundancy and Data Inconsistency are the important terms used in the Database. A good Database Design is the one in which there is minimum Data Redundancy and Data Inconsistency. In this article, we will what are these two terms and what is the difference between them.
Data Redundancy :
It is defined as the redundancy means duplicate data and it is also stated that the same parts of data exist in multiple locations into the database. This condition is known as Data Redundancy.
Problems with Data Redundancy :
Here, we will discuss the few problems with data redundancy as follows.
- Wasted Storage Space.
- More Difficult Database Update.
- It will lead to Data Inconsistency.
- Retrieval of data is slow and inefficient.
Let us take an example of a cricket player table.
Consider cricket player table as follows.
|Player Name||Player Age||Team Name||Team ID|
|Ross Taylor||37||New Zealand||2|
|Kane Williamson||30||New Zeland||2|
We can clearly see that the Team Name and Team Id are repeated at multiple places. we can make a separate table to store this information and reduce data redundancy.
Player Name Player Age Team Id Virat Kohli 32 1 Rohit Sharma 34 1 Ross Taylor 37 2 Shikhar Dhawan 35 1 Kane Williamson 30 2
This is known as Normalization used to reduce Data Redundancy.
Team Id Team Name 1 India 2 New Zealand
Data Inconsistency :
When the same data exists in different formats in multiple tables. This condition is known as Data Inconsistency. It means that different files contain different information about a particular object or person. This can cause unreliable and meaningless information. Data Redundancy leads to Data Inconsistency.
If we have an address of someone in many tables and when we change it in only one table and in another table it may not be updated so there is the problem of data inconsistency may occur.
Topic Data Redundancy Data Inconsistency Condition It will be applicable when the duplicate data exists in multiple places in the database. It will be applicable when the duplicate data exists in different formats in multiple tables. How to minimize it? we can use normalization to minimize Data Redundancy. we can use constraints on the database to minimize Data Inconsistency.
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