Difference between Data Redundancy and Data Inconsistency

Introduction

In database management, two common issues often encountered are Data Redundancy and Data Inconsistency. These terms refer to different challenges that arise when handling large datasets. A well-structured database design aims to minimize both of these issues to ensure data integrity, accuracy, and efficient storage. This article will explain what Data Redundancy and Data Inconsistency are, highlight the differences between them, and provide solutions to mitigate these problems.

What is Data Redundancy?

It is defined as redundancy means duplicate data and it is also stated that the same parts of data exist in multiple locations in the database. This condition is known as Data Redundancy. 

Problems with Data Redundancy

Here, we will discuss a few problems with data redundancy as follows.

1. Wasted Storage Space.
2. More Difficult Database Update.
3. It will lead to Data Inconsistency. 
4. Retrieval of data is slow and inefficient.

Example -
Let us take an example of a cricket player table.

Step-1 :
Consider cricket player table as follows. 

Step-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.

Step-3 :
This is known as Normalization used to reduce Data Redundancy.

What is 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. 

Problems with Data Inconsistency

• Unreliable Information: Data inconsistency can result in conflicting information across different parts of the database, making it difficult to ascertain the truth.
• Difficulties in Data Retrieval: Retrieving data from inconsistent records can be challenging as you may encounter contradictory data.
• Challenges in Decision Making: Inconsistent data can hinder accurate decision making, especially when businesses rely on updated information.

Example - 
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.

Differences :

Conclusion

Both Data Redundancy and Data Inconsistency can cause significant challenges in database management. Data redundancy, if not controlled, can lead to data inconsistency, making the database inefficient and unreliable. To address these issues, database designers often use Normalization to reduce redundancy and Constraints to maintain data consistency. A well-structured database ensures minimal redundancy and consistency across all data entries, leading to efficient data handling and reliable decision-making.