WHAT IS DATA REDUNDANCY?

摘要： Data redundancy means keeping data in two or more locations within a database or storage infrastructure.

Data redundancy means keeping data in two or more locations within a database or storage infrastructure. Data redundancy can occur either intentionally or accidentally within an organization. In case of data corruption or loss, the organization can continue operations or services if conscious redundancy is provided. On the other hand, unconscious redundancy causes duplicate data to waste database space and information inconsistencies throughout the organization.

TYPES OF DATA REDUNDANCY

There are two types of data redundancy. Positive data redundancy is provided intentionally within the organization. It ensures that the same data kept and protected in different places are used for redundancy and business sustainability in case of a possible disaster.

Wasteful data redundancy, which occurs with unintentional data duplication and is an indicator of failed database management, may cause information inconsistencies throughout an organization. When data is stored in numerous places, it takes up valuable storage space and makes it difficult for an organization to figure out which data should be accessed or updated.

WHAT IS THE DIFFERENCE BETWEEN DATA REDUNDANCY, DATA DUPLICITY, AND BACKUP?

The main difference between redundancy and duplicity, which is often confused, lies in the reason for adding a new copy of the data. From a database point of view, data duplicity refers to data added back to the system by users. In contrast, redundancy requires synchronization between databases to ensure positive redundancy without any problems. While data duplicity inevitably causes inconsistency in databases, database synchronizations and data normalization prevent this issue in data redundancy.

The distinction between data backup and redundancy may be subtle, but it is crucial. Backing up data creates compressed and encrypted versions of data stored locally or in the cloud. In contrast, data redundancy adds an extra layer of protection to the backup. Local backups are necessary for business continuity; however, it’s also essential to have another protective layer for data. You can reduce the risks by including data redundancy in your disaster recovery plan.

WHAT IS THE RELATIONSHIP BETWEEN DATA REDUNDANCY AND DATA INCONSISTENCY?

Simply put, data redundancy leads to Data Inconsistency. The data inconsistency condition occurs when the same data exists in different formats in multiple tables. It means that other files contain different information about a particular object, situation, event, or person. This inconsistency can cause unreliable and meaningless information.

BENEFITS OF POSITIVE DATA REDUNDANCY

Data must be stored in two or more locations to be considered redundant. Suppose the initial data is damaged or the hard drive on which it is stored fails. In that case, the backup data can help save the organization money.

The redundant data may be either a complete copy of the original information or particular elements of it. Keeping only certain pieces of data allows organizations to reassemble lost or destroyed data without pushing their resource limitations. Backups and RAID systems are used to protect data in case of failure. Backups, for example, can be stored on multiple hard drives so that if one fails, the array can activate with minimal downtime.

There are distinct advantages to data redundancy, which depend on its implementation. The following are some of the potential benefits:

• Data redundancy helps to guarantee data security. Organizations can use redundant data to replace or recompile missing information when data is unavailable.

• Multiple data servers enable data management systems to examine any variances, assuring data consistency.

• Data may be easier to access in some areas than others for an organization that covers several physical locations. Accessing information from various sources might allow individuals in a company to access the same data more quickly.

• Data redundancy is a must in business continuity management. Backup technology ensures data security, while disaster recovery services minimize downtime by prioritizing mission-critical information. Data redundancy serves as an add-on to both of these processes for increased recoverability.

HOW TO AVOID WASTEFUL DATA REDUNDANCY?

As wasteful data redundancy grows, it takes up a significant server storage space over time. The fewer storage slots there are, the longer it will take to retrieve data, eventually harming business results. On the other hand, inconsistent data is likely to corrupt reports or analytics that can cost organizations direly.

Data redundancy is popular among organizations as data security or backup method. It appears to be an excellent solution when you have all the resources needed to store and manage your data. But if you don’t have enough resources, the positive redundancy can turn wasteful quickly. Here are some valuable tips to avoid wasteful redundancy:

• Master Data provides more consistency and accuracy in data. It’s the sum of all your vital business information stored in various systems throughout your company. The use of master data does not eliminate data redundancy; instead, it helps organizations work around a certain degree of redundancy. The main advantage of master data is that it allows companies to work on a single changed data element instead of the overall data.

• Another source of data redundancy is keeping information that isn’t relevant any longer. Suppose you migrate your data to a new database but forget to delete it from the old one. In that case, you’ll have the same information in two locations, wasting space. Make sure databases that aren’t required anymore are deleted.

• Data normalization is a technique that involves organizing data in a database to minimize duplication. This approach ensures that the data from all records are comparable and may be interpreted similarly. Standardizing data fields, including customer names, contact information, and addresses is easy with data normalization. Therefore, it will allow you to quickly delete, update, or add any information.

轉貼自Source： dataconomy.com

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