What is Data Redundancy?[Definition, Types and Causes]

Today’s business world understands the importance of data very well. Data can make or break businesses. When businesses rely so much on data today, maintaining its quality becomes essential. Noise in data often tends to reflect wrong trends, leading to wrong decisions. Data redundancy is one of the everyday noises with which data gets infected. Data redundancy refers to storing duplicate copies of data within a database or storage system. While redundancy can be intentional, sometimes it can lead to inconsistencies and increased storage and processing costs. Maintaining data redundancy is essential for data integrity. In this blog, we go through data redundancy in-depth, covering its types, causes, advantages, disadvantages, and how it can be managed effectively.

What is Data Redundancy?

Let’s understand data redundancy using an example. Most of us might have seen our parents taking photocopies of our documents and telling us we must keep copies at different places with us for safety. Data Redundancy is the same, it is when the same portion of data is stored at multiple places within a database or a system. This could be intentional for security and recovery purposes or unintentional due to poor database design.

Another technical example could be that clients’ contact details are stored separately in multiple tables in a database. This could be done by maintaining two tables related to CRM and invoicing. Though this is done to separate invoicing addresses from CRM in case contact details change, this could lead to redundancy.

Thus we can categorize redundancy based on intent into two types :

1. Intentional: This is done for backup and security
2. Unintentional: This could happen due to poor database design or insufficient architecture discussions.

Types of Data Redundancy

Data redundancy can be classified into the following five parts based on cause and how it is implemented.

1. Manual Data Redundancy

It is type of redundancy that occurs due to human errors while manually entering data into the system. This is mostly found in old systems where no or limited automation exists. For example, Govt institutes mostly have manual work, and the same type of information is required and fed at each counter.

2. Hardware-Based Redundancy

This redundancy appears as a cause of storing copies of data in different hardware devices. For example, RAID (Redundant Array of Independent Disks) systems use mirroring techniques to prevent data loss.

3. Software-Based Redundancy

This redundancy is mostly found in database systems, where duplicate records are maintained to recover during a disaster. For example, Cloud-based systems automatically create multiple copies of data for security.

4. Backup and Disaster Recovery Redundancy

This is an intentional type of redundancy that is done to ensure data recovery if the system fails. For example, all the major cloud providers store multiple copies of data at different locations to withstand any unexpected disaster.

5. Data Mirroring

As the name suggests, it is when data is copied in real time. This could be done to make it highly available in real-time. For example, this is most common in financial and healthcare data where data availability and accuracy are highly critical.

Causes of Data Redundancy

Data redundancy could be done or might happen due to many factors. Let’s discuss them below.

• Poor Database Design: The Unplanned implementation of databases or inefficient database structures can lead to the duplicate storage of data.
• Lack of Normalization: Intentional or unintentional negligence of database normalization can lead to duplication.
• Manual Data Entry Errors: Humans make mistakes. Manual data entry is often accompanied by redundant records getting inserted.
• Multiple Storage Locations: Storing the same data in multiple hardware systems can cause redundancy.
• Backup and Recovery Strategies: Sometimes, redundancy is done intentionally for security and recovery purposes.
• Mergers and Data Integration: When data is merged from different sources, overlap in data sources can also cause redundant data to be stored.

Advantages of Data Redundancy

Data redundancy, apart from its limitation, also has some advantages. Below, we discuss some places where data redundancy can be helpful.

• Data Recovery & Backup Data redundancy helps to ensure that data is available in case of failure or unknown disasters.
• Improved Data Availability: Sometimes, copies of data are created intentionally to allow easier access to data. This is majorly done in cloud systems to ensure high availability.
• Fault Tolerance: When any system failure happens, Redundant data helps systems to continue functioning.
• Enhanced Performance: Data redundancy also helps in the speedy retrieval of data. Cloud systems do this to ensure the seamless availability of data.

Disadvantages of Data Redundancy

While Data redundancy can be beneficial, it also has some disadvantages. Let’s discuss some of them below.

• Increased Storage Costs: Duplicate data leads to higher storage expenses.
• Data Inconsistency: Syncing any updated information from one location to redundant copies is highly difficult. In a later stage, it creates an overhead to keep all the information updated.
• Complex Data Management: When data grows, managing redundant copies of data becomes challenging.
• Reduced System Efficiency: While duplicate copies of data make it highly available, this can slow down data retrieval and database queries.

How to Reduce Unnecessary Data Redundancy

Well-planned redundant data can be beneficial for systems. Let’s discuss how data redundancy can be reduced.

1. Database Normalization: Database normalization is a very old yet very powerful concept for reducing data redundancy. Data can be planned and organized into structured tables to minimize data duplication.

2. Use of Primary and Foreign Keys: Primary and foreign keys can be leveraged to stop duplicate data from getting ingested into the system. This can help to ensure data integrity and prevent any unnecessary duplication.

3. Data Deduplication Techniques: Data systems can plan and schedule various deduplication techniques to remove any redundant copies. For example, A scheduled job can be run on data lakes to ensure the removal of any duplicated data.

4. Efficient Data Management Tools: Efficient data management tools can be used to optimize data storage. For example, DBMS (Database Management Systems) can help in the structured storage of data.

5. Automation in Data Entry: Human intervention in data entry is the main reason for redundancy in storage systems. Automation in this area can help to reduce human errors.

6. Data Integration Techniques: Proper tools or processes can be implemented to detect any duplicates while merging data from multiple sources. This can help to avoid redundancy right while ingestion.

When is Data Redundancy Necessary?

Though redundancy has its advantages and disadvantages, Its implementation is very important in various use cases.

• Disaster Recovery:  Multiple backup copies of data can prevent any data loss in case of any unknown disaster. It becomes highly important to keep multiple copies of data.
• High Availability Systems: Everybody needs high availability and speed in today’s fast-paced world. Critical applications like healthcare or banking sites require redundant data to avoid any downtime.
• Distributed Databases: Data replication across multiple database storage locations improves accessibility. This ensures high availability of data across various regions.
• Cloud Storage Systems: Cloud storage systems use the concept of redundancy to ensure security and faster access to data.

Conclusion

By the end of this article, we now know how vital data redundancy is for data management. Though it might help in easy data recovery in case of disaster and improved availability, it can lead to inconsistency and increased costs. Businesses should decide on what data is very important and should be duplicated. Strategies like normalization and deduplication can help eliminate this redundancy. Proper data management is essential to ensure effectiveness in handling information. To avoid data redundancy and migrate correct data to your destination, try Hevo. Sign up for a 14-day free trial and experience seamless data operations.

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