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What is data integrity?

The overall precision, completeness, and continuity of data is known as data integrity. Data integrity also applies to the data's protection and security in terms of regulatory enforcement, such as GDPR compliance. It is kept up to date by a set of procedures, guidelines, and specifications that were put in place during the design phase.

It's easy to get the true sense of data integrity muddled because there's so much chatter about it. Data protection and data quality are often confused with data integrity, but the two terms have different meanings.

Data integrity also ensures that the information is protected from outside influences.

Different Kinds of data integrity

Physical and logical data integrity are the two forms of data integrity. Both are a collection of procedures and methods for maintaining data integrity in hierarchical and relational databases.

Physical integrity

Physical integrity refers to the safeguarding of data's completeness and precision during storage and retrieval. Physical integrity is jeopardized when natural disasters occur, electricity goes out, or hackers interrupt database functions.

Data processing administrators, device programmers, applications programmers, and internal auditors may be unable to obtain reliable data due to human error, storage degradation, and a variety of other issues.

Logical integrity

In a relational database, logical consistency ensures the data remains intact as it is used in various ways. Logical integrity, like physical integrity, defends data from human error and hackers, but in a different way. There are four different forms of logical consistency.

Entity integrity

It's a characteristic of relational databases, which store information in tables that can be connected and used in a number of ways.

Referential integrity

The term "referential integrity" refers to a set of procedures that ensure that data is stored and used consistently. Only necessary modifications, additions, or deletions of data are made, thanks to rules embedded in the database's layout on how foreign keys are used.

Rules can include restrictions that prevent duplicate data entry, ensure data accuracy, and/or prohibit the entry of data that does not apply.

Domain integrity

A domain is a collection of suitable values that a column may contain in this context. Constraints and other steps that restrict the format, sort, and amount of data entered can be used.

User-defined integrity

User-defined integrity refers to the rules and restrictions that the user creates to meet their specific requirements. When it comes to data security, person, referential, and domain integrity aren't always enough.

It's easy to get the true sense of data integrity muddled because there's so much chatter about it. Data protection and data quality are often confused with data integrity, but the two terms have different meanings.

Data integrity is not data security

The collection of procedures for preventing data corruption is known as Data security. It entails the use of programs, protocols, and procedures to keep data out of the hands of anyone who may misuse it in negative or unintended ways. Data security breaches can be small and easily contained, or they can be massive and cause substantial harm.

Data protection is just one facet of data integrity's many aspects. Data protection isn't comprehensive enough to include all of the processes required to keep data consistent over time.

Data integrity is not data quality

Data quality measures the age, importance, precision, completeness, and reliability of the data and uses a variety of processes to address these questions.

Data quality, like data reliability, is just a small part of data integrity, but it's an important one.

DATA INTEGRITY AS A PROCESS AND AS A STATE

Data integrity is also a bit of a misnomer since it may refer to either a state or a mechanism. As a state, data integrity refers to a collection of data that is both true and correct. Data integrity, on the other hand, is a mechanism that defines the steps taken to ensure the authenticity and consistency of a data collection or all of the data in a database or other structure.

A CASE FOR DATA INTEGRITY

Data integrity is critical for a variety of reasons. Data integrity guarantees recoverability and searchability, as well as traceability (back to the source) and connectivity. Protecting the quality and accuracy of data improves reusability and maintainability while increasing stability and efficiency.

Data is increasingly driving business decisions, but it must go through a series of transformations and processes to get from its raw state to formats that are more useful for defining relationships and making informed decisions. As a result, data integrity is a top priority for today's businesses.

Data integrity can be harmed in a number of ways, so data integrity policies are an important part of any enterprise protection strategy.

Because only some of these compromises can be effectively avoided by data security, data backup and replication become essential for maintaining data integrity.

DATA INTEGRITY FOR DATABASES

Data integrity, in its broadest sense, is a term used to describe the health and upkeep of any digital data. Many people associate the word with database management. There are four forms of data integrity in databases.

  • Entity Integrity: There are columns, rows, and tables in a database. These elements should be as numerous as possible for the data to be correct in a primary key, but not more than that. A database of workers, for example, should have primary key data such as their name and a unique "employee number."
  • Referential Integrity: A foreign key table in a database is a second table that may refer to a database's primary key table. Foreign keys are used to connect data that is either shared or null. Employees can, for example, have the same job title or work in the same department.
  • Domain Integrity: In a database, all categories and values are set, including nulls (e.g., N/A). The common ways to input and read data in a database are referred to as domain integrity. Three decimal places would not be allowed in a database that uses monetary values such as dollars and cents.
  • User-Defined Integrity: Outside of object, referential, and domain integrity, there are sets of data generated by users. This data would be labeled as "user-defined" if an employer created a column to input employee corrective action.

DATA INTEGRITY VS. DATA SECURITY

Data security is the protection of data from unauthorized access or corruption, and it is required to maintain data integrity.

However, while data integrity is a desirable outcome of data security, the term data integrity only applies to the validity and consistency of data, not the act of data protection. To put it another way, data protection is one of the methods for ensuring data integrity.

If it's a case of malicious intent or an unintentional compromise, data protection is crucial to data integrity. It's also a big part of a lot of data protection systems.

Data integrity is important but manageable for enterprises today, thanks to a range of data security approaches such as backup and replication, database integrity restrictions, authentication procedures, and other systems and protocols.


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