Data redundancy occurs when the same piece of data is stored in two or more separate places and is a common occurrence in many businesses. As more companies are moving away from siloed data to using a central repository to store information, they are finding that their database is filled with inconsistent duplicates of the same entry. Although it can be challenging to reconcile — or even benefit from — duplicate data entries, understanding how to reduce and track data redundancy efficiently can help mitigate long-term inconsistency issues for your business.
How does data redundancy occur?
Sometimes data redundancy happens by accident while other times it is intentional. Accidental data redundancy can be the result of a complex process or inefficient coding while intentional data redundancy can be used to protect data and ensure consistency — simply by leveraging the multiple occurrences of data for disaster recovery and quality checks.
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If data redundancy is intentional, it’s important to have a central field or space for the data. This allows you to easily update all records of redundant data when necessary. When data redundancy isn’t purposeful, it can lead to a variety of issues which we’ll discuss below.
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Understanding database versus file-based data redundancy
Data redundancy can be found in a database, which is an organized collection of structured data that’s stored by a computer system or the cloud. A retailer may have a database to track the products they stock. If the same product gets entered twice by mistake, data redundancy takes place.
The same retailer may keep customer files in a file storage system. If a customer purchases from the company more than once, their name may be entered multiple times. Duplicate entries of the customer name is considered redundant data.
Regardless of whether data redundancy occurs in a database or in a file storage system, it can be problematic. Fortunately, data replication can help prevent data redundancy by storing the same data in multiple locations. With data replication, companies can ensure consistency and receive the information they need at any time.
Top 4 advantages of data redundancy
Although data redundancy sounds like a negative event, there are many organizations that can benefit from this process when it’s intentionally built into daily operations.
1. Alternative data backup method
Backing up data involves creating compressed and encrypted versions of data and storing it in a computer system or the cloud. Data redundancy offers an extra layer of protection and reinforces the backup by replicating data to an additional system. It’s often an advantage when companies incorporate data redundancy into their disaster recovery plans.
2. Better data security
Data security relates to protecting data, in a database or a file storage system, from unwanted activities such as cyberattacks or data breaches. Having the same data stored in two or more separate places can protect an organization in the event of a cyberattack or breach — an event which can result in lost time and money, as well as a damaged reputation.
3. Faster data access and updates
When data is redundant, employees enjoy fast access and quick updates because the necessary information is available on multiple systems. This is particularly important for customer service-based organizations whose customers expect promptness and efficiency.
4. Improved data reliability
Data that is reliable is complete and accurate. Organizations can use data redundancy to double check data and confirm it’s correct and completed in full — a necessity when interacting with customers, vendors, internal staff, and others.
Watch out for data redundancy disadvantages
Although there are noteworthy advantages of intentional data redundancy, there are also several significant drawbacks when organizations are unaware of its presence.
Possible data inconsistency
Data redundancy occurs when the same piece of data exists in multiple places, whereas data inconsistency is when the same data exists in different formats in multiple tables. Unfortunately, data redundancy can cause data inconsistency, which can provide a company with unreliable and/or meaningless information.
Increase in data corruption
Data corruption is when data becomes damaged as a result of errors in writing, reading, storage, or processing. When the same data fields are repeated in a database or file storage system, data corruption arises. If a file gets corrupted, for example, and an employee tries to open it, they may get an error message and not be able to complete their task.
Increase in database size
Data redundancy may increase the size and complexity of a database — making it more of a challenge to maintain. A larger database can also lead to longer load times and a great deal of headaches and frustrations for employees as they’ll need to spend more time completing daily tasks.
Increase in cost
When more data is created due to data redundancy, storage costs suddenly increase. This can be a serious issue for organizations who are trying to keep costs low in order to increase profits and meet their goals. In addition, implementing a database system can become more expensive.
How to reduce data redundancy
Fortunately, it is possible to reduce unintentional cases of data redundancy that often lead to operational and financial problems.
Master data
Master data is a single source of common business data that is shared across several applications or systems. Although master data does not reduce the occurrences of data redundancy, it allows companies to work around and accept a certain level of data redundancy. This is because the use of master data ensures that in the event a data piece changes, an organization only needs to update one piece of data. In this case, redundant data is consistently updated and provides the same information.
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Database normalization
Database normalization is the process of efficiently organizing data in a database so that redundant data is eliminated. This process can ensure that all of a company’s data looks and reads similarly across all records. By implementing data normalization, an organization standardizes data fields such as customer names, addresses, and phone numbers.
Normalizing data involves organizing the columns and tables of a database to make sure their dependencies are enforced correctly. The “normal form” refers to the set of rules or normalizing data, and a database is known as “normalized” if it’s free of delete, update, and insert anomalies.
When it comes to normalizing data, each company has their own unique set of criteria. Therefore, what one organization believes to be “normal,” may not be “normal” for another organization. For instance, one company may want to normalize the state or province field with two digits, while another may prefer the full name. Regardless, database normalization can be the key to reducing data redundancy across any company.
Efficient data redundancy use cases
Efficient data redundancy is possible. Many organizations like home improvement companies, real estate agencies, and companies focused on customer interactions have customer relationship management (CRM) systems.
When a CRM system is integrated with another business software like an accounting software that combines customer and financial data, redundant manual data is eliminated, leading to more insightful reports and improved customer service.
Database management systems are also used in a variety of organizations. They receive direction from a database administrator (DBA) and allow the system to load, retrieve, or change existing data from the systems. Database management systems adhere to the rules of normalization, which reduces data redundancy.
Hospitals, nursing homes, and other healthcare entities use database management systems to generate reports that provide useful information for physicians and other employees. When data redundancy is efficient and does not lead to data inconsistency, these systems can alert healthcare providers of rises in denial claim rates, how successful a certain medication is, and other important pieces of information.
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Reducing data redundancy with data management
Although data redundancy in a database or file storage system can benefit an organization when it’s intentional, this process can also be detrimental when done by accident. Companies can alleviate the headache that often comes with data redundancy with jonathanlewisforcongress.com Data Fabric.
jonathanlewisforcongress.com Data Fabric allows you to collect, govern, transform, and share data with internal stakeholders while enabling automated data quality. Try jonathanlewisforcongress.com Data Fabric today to mitigate data redundancy issues.
