Which of the following occurs when different versions of the same data appear in different places?

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How does data redundancy occur?
Understanding database versus file-based data redundancy
Top 4 advantages of data redundancy
1. Alternative data backup method
2. Better data security
3. Faster data access and updates
4. Improved data reliability
Watch out for data redundancy disadvantages
Possible data inconsistency
Increase in data corruption
Increase in database size
Increase in cost
How to reduce data redundancy
Master data
Database normalization
Efficient data redundancy use cases
Reducing data redundancy with data management
Which of the following occurs when different versions of the same data appear in different places in a database?
Which of the following refers to the situation where the same data are stored at different places?
What is the duplication of data or the storage of the same data in multiple places multiple choice question?
What is data inconsistency quizlet?

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Overview :
Data Redundancy and Data Inconsistency are the important terms used in the Database. A good Database Design is the one in which there is minimum Data Redundancy and Data Inconsistency. In this article, we will tell what are these two terms and what is the difference between them.

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

Problems with Data Redundancy :
Here, we will discuss the few problems with data redundancy as follows.

Wasted Storage Space.
More Difficult Database Update.
It will lead to Data Inconsistency.
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.

Player Name	Player Age	Team Name	Team ID
Virat Kohli	32	India	1
Rohit Sharma	34	India	1
Ross Taylor	37	New Zealand	2
Shikhar Dhawan	35	India	1
Kane Williamson	30	New Zealand	2

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.

Player Name	Player Age	Team Id
Virat Kohli	32	1
Rohit Sharma	34	1
Ross Taylor	37	2
Shikhar Dhawan	35	1
Kane Williamson	30	2

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

Team Id	Team Name
1	India
2	New Zealand

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.

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 :

Topic	Data Redundancy	Data Inconsistency
Condition	It will be applicable when the duplicate data exists in multiple places in the database.	It will be applicable when the duplicate data exists in different formats in multiple tables.
How to minimize it?	we can use normalization to minimize Data Redundancy.	we can use constraints on the database to minimize Data Inconsistency.

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.

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.

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.

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.

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 Talend Data Fabric.

Talend Data Fabric allows you to collect, govern, transform, and share data with internal stakeholders while enabling automated data quality. Try Talend Data Fabric today to mitigate data redundancy issues.

Which of the following occurs when different versions of the same data appear in different places in a database?

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.

Which of the following refers to the situation where the same data are stored at different places?

Data redundancy refers to the practice of keeping data in two or more places within a database or data storage system. Data redundancy ensures an organization can provide continued operations or services in the event something happens to its data -- for example, in the case of data corruption or data loss.

What is the duplication of data or the storage of the same data in multiple places multiple choice question?

Data replication is the process of storing the same data in multiple locations to improve data availability and accessibility, and to improve system resilience and reliability.

What is data inconsistency quizlet?

1. Data Redundancy and Inconsistency. ---Data Redundancy, is the presence of duplicate data in multiple data files so that the same data are stored in more than one place or location. ---Data Inconsistency, where the same attribute may have different values.