What are the two biggest issues caused by data redundancy?

Data redundancy is a condition created within a database or data storage technology in which the same piece of data is held in two separate places.

This can mean two different fields within a single database, or two different spots in multiple software environments or platforms. Whenever data is repeated, it basically constitutes data redundancy.

Data redundancy can occur by accident but is also done deliberately for backup and recovery purposes.

Within the general definition of data redundancy, there are different classifications based on what is considered appropriate in database management, and what is considered excessive or wasteful. Wasteful data redundancy generally occurs when a given piece of data does not need to be repeated but ends up being duplicated due to inefficient coding or process complexity.

For example, wasteful data redundancy might occur when inconsistent duplicates of the same entry are found on the same database. Accidental data redundancy could occur due to inefficient coding or overcomplicated data storing processes, and represent an issue in terms of efficiency and costs.

Since the existence of duplicate or unnecessary data fields should be resolved, the reconciliation, integration, and normalization operations required to remove inconsistencies can be costly and time-consuming. Errors generated by accessing the wrong redundant data sets might lead to many issues with clients. Lastly, the additional space taken up by redundant data might start to add up over time, leading to bloated databases.

A positive type of data redundancy works to safeguard data and promote consistency. Multiple instances of the same datasets could be leveraged for backup purposes, disaster recovery (DR), and quality checks.

Redundant data can be stored on purpose by creating compressed versions of backup data that can be restored, and become part of specific DR strategies. In the event of a cyberattack or data breach, for example, having the same data stored in several different places can be critical to ensure the continuity of operations as well as damage mitigation.

Data redundancy can also be leveraged to improve the speed of updates and data access if it’s stored on multiple systems that can be accessed by different departments.

Many developers consider it acceptable for data to be stored in multiple places. The key is to have a central, master field or space for this data, so that there is a way to update all of the places where data is redundant through one central access point. Otherwise, data redundancy can lead to big problems with data inconsistency, where one update does not automatically update another field. As a result, pieces of data that are supposed to be identical end up having different values.

Whenever prevention is not enough, database normalization or reconciliation operations can be required to eliminate already existing redundancies. A series of standardization rules are first defined to set what “normal data” actually is. Then, the database is checked to ensure that the dependencies in all columns and tables are enforced correctly and that all unnecessary duplicates are correctly addressed.

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Data modeling is a representation of the data structures in a table for a company’s database and is a very powerful expression of the company's business requirements. This data model is the guide used by functional and technical analysts in the design and implementation of a database.

Data models are used for many purposes, from high-level conceptual models to physical data models.

Data modeling explores data-oriented structures and identifies entity types. This is unlike class modeling, where classes are identified.

Three basic styles of data modeling are generally used in practice today.

• Conceptual Data Models: High-level, static business structures and concepts
• Logical Data Models (LDMs): Entity types, data attributes and relationships between entities
• Physical Data Models (PDMs): The internal schema database design

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Data empowers businesses to make effective decisions based on facts instead of guesswork. With the right data in hand, you can shift through the noise and get the right information to make decisions that can fuel your business growth and success. That’s the power of data; it can make or break any business, depending on how effectively it is used. 

When it comes to dealing with big data sets, data redundancy can be a major challenge your organization may face. Servers are the target destination where all your data stays. Therefore, it becomes crucial to ensure that only useful and relevant information is moved there.  

By feeding relevant data to your data warehouse, you can make the best use of your storage space and ensure that not even a single byte is wasted or misused. Let’s learn about data redundancy and how you can avoid it.

What is Data Redundancy?

Data Redundancy occurs when the same data set is stored in two or more places. It may not seem like a big deal until multiple data sets pile up more than once, taking up gigabytes of storage space on your servers. Managing duplicate data when your servers are already loaded can be a grueling process.

As the data redundancy increases with time, it will eat up a huge chunk of your server’s storage space. The lesser the storage space will be, the more time the process of data retrieval will take, affecting the overall performance of your business.

Another major reason that makes it more critical for you to look out for data redundancy is that the same data stored in several places can confuse users and make it difficult for them to identify which data they should access or update. There is a higher likelihood that you may end up with corrupt reports or analytics that can cost you your organizational growth.

How can DataChannel help?

DataChannel offers brilliant data warehousing solutions that ensure all your data is integrated into your preferred data warehouse, along with all the customization you need. DataChannel’s data integration technology helps you integrate data from dispersed sources into one place so that duplicate data can be avoided across multiple systems. 

DataChannel also facilitates the cleansing of data as part of the process to save a lot of time and effort. We do everything when it comes to data cleansing like preventing data duplication, removing null values, fixing errors, updating records, and that too in real-time and with data security.

With us, you can reduce redundancies within your existing databases and move forward with your business growth.

What else can you do to avoid data redundancy?

Every business prefers to make a copy of the data intentionally as a form of data security or backup. This seems to be a good idea when you have all the resources required to store and manage your data. If you are facing a scarcity of storage resources, here’s what you must do to avoid data redundancy.

Master Data

With Master Data, you can ensure better consistency and accuracy of data. It is the sum of all your business-critical data stored in disparate systems across your organization.

Although master data does not reduce data redundancy, it helps companies understand that it is not practical to expect zero data redundancy and work around a certain redundancy level.

The main benefit of using master data is that in case a data piece changes, the organization, instead of working on the overall data, has only to update that one piece of data.

Deletion of unused data

Another factor that adds up to data redundancy is keeping that piece of data in your server that is no longer required. For example, you moved your customer data into a new database but forgot to delete the same from the old one. In such a scenario, you will have the same data sitting in two places, just taking up the storage space.

To reduce data redundancy, always delete databases that are no longer required.

Design your database

With in-house applications that read from databases, you can design your database’s architecture the right way. The relational databases will ensure that you have common fields and allow you to link up tables and match records. This will make it easier for you to figure out repetition and remove it.

Normalize Database

It is a process in which data is efficiently organized in a database so that duplication can be avoided. It ensures that the data across all the records provide a similar look and can be read in a particular manner. With data normalization, you can standardize data fields, including customer names, contact information, and address. This will help you delete, update, and insert any information with ease.

Data management

Intentional data redundancy in the storage server can help organizations in many ways, but the same can deteriorate your overall organizational efficiency if it happens by accident.

Companies can walk to the safer side of the fence by implementing a reliable data management system. With DataChannel’s data integration solution, you can reduce data redundancy and have only that data in hand that can help your business go a long way.

The Bottom Line

Unplanned data redundancy can be a really big problem for organizations. Therefore, it becomes essential to remove as much redundant data as you can, but be careful while deleting as you don’t want any crucial data piece getting deleted by mistake.

Keep the backup of data only when you need it all back in the same way it was before. With DataChannel’s data integration solution, you can automate the process of data cleaning while the data is being loaded into the target destination. It will help you avoid duplicate data as well as errors and leave you only with the exact data you expected.

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