Data is an essential part of the business world today. Imagine running an online store and not having a pre-defined set of rules according to which you would organize data. Customer addresses, files, and other information would be all over the place, and anyone could access it.
But luckily, data integrity helps keep a business on its feet by setting rules on how this data can be accessed, used, and changed.
Summary: The term data integrity refers to the maintenance and assurance of data consistency and accuracy over the data’s lifecycle. Data integrity is a crucial aspect of the design, implementation, and usage of any system that works with data. Data integrity types include physical and logical integrity, with the latter consisting of entity, domain, referential, and user-defined integrity. Examples of data integrity include all data stored inside an organization’s database on servers. This can be user information, healthcare records, pharmaceutical files, etc.
What is Data Integrity?
Data integrity is a process that makes sure data is accurate, complete, and consistent over the data’s lifecycle. When an organization follows this process, it ensures data integrity and has correct, accurate, and uncompromised data in the database.
Compromised data is of little use to an enterprise. Also, sensitive data loss presents a true danger. That’s why maintaining data integrity is a must for organization security. Employees can use different validation procedures and error checking methods to make sure data is reproduced or transferred in its integrity without being changed.
Importance of Data Integrity
Data integrity is becoming more important day by day. This is mainly because the data volume increases exponentially. All major organizations rely on data integration and its ability to accurately interpret information to assess market activity, mitigate data security risks, or analyze consumer behavior.
Data integrity is maintained using a set of rules, processes, and standards created during the design stage. This means that data integrity is also important to data mining, as it allows data scientists to work with correct information.
Data Integrity Characteristics
Here’s an overview of data integrity characteristics.
Data completeness refers to the degree to which data inside a database is available.
Data is accurate when it is used in its correct form and supplies the organization with appropriate information.
Consistency of data can be low level (for example, whether customer addresses are formatted in the same way) or high level (whether different groups in an organization use the same data set).
Timeliness is essential to data integrity because data needs to be collected promptly to be valuable.
Data should meet compliance standards for particular organizations like the GDPR or similar privacy regulations.
Types of Data Integrity
Maintaining data integrity calls for an understanding of different data integrity types. The two main types include physical integrity and logical integrity. Both consist of a set of methods and processes made to enforce data integrity in hierarchical and relational databases.
By physical integrity, we encompass the overall data protection (including its accuracy and wholeness) as we store and retrieve it. For example, in the case of storms or natural disasters, power outages, or hacker attacks on database functions, it’s crucial to maintain physical data integrity.
Human error or storage erosion can also be an issue that falls under physical integrity, which can make data management useless and make jobs harder for system and application programmers, managers, or internal auditors.
Some ways to ensure the physical integrity of data include redundant hardware, error-correcting memory, RAID arrays, uninterruptible power supply, and many others.
Logical integrity is about the accuracy of data in a specific context. The aim of logical integrity is to keep data unchanged from how it’s used in a relational database. This integrity type can protect data from hackers and human errors as well. However, the way that it does so is different from physical integrity.
There are four types of logical integrity:
Entity integrity creates a primary key value (a value that identifies data) to make sure information isn’t listed multiple times or that no fields in the tables are null. Relational systems that store data in tables and use them in different ways especially benefit from entity integrity.
Referential integrity makes sure organizations and enterprises use and store data uniformly. This means that the data in databases can only operate under certain rules.
By implementing referential integrity, database managers can set the rules for changing, adding, or deleting data in a particular set. Some rules may also include integrity constraints that eliminate duplicate data entry, allow data accuracy, and prevent adding irrelevant data.
Domain integrity refers to a set of processes that make sure data pieces inside a domain are accurate. By domain, we mean a set of values one column can contain. This can also refer to integrity constraints or similar measures aiming to limit the type, format, and amount of entered data.
User Defined Integrity
This integrity type is about the constraints and rules a user can make to fit their needs. If previous data integrity types aren’t enough to ensure data integrity, then businesses can proceed to user-defined integrity.
Data Integrity Risks
Now that we have outlined different types of data integrity, let’s see what some of the most significant data integrity risks are.
Human error is a commonly found cause of data integrity issues across organizations. Users often enter incorrect data, duplicate it, or delete it by mistake. They may also not follow protocols or can make errors with pre-set procedures that aim to maintain data integrity.
Viruses and Hackers
Data security plays a huge role in maintaining data integrity. Hackers can attack an organization’s data integrity using spyware, malware, viruses, and other software. They attack computers and steal or compromise data.
If the hardware on which data is stored becomes compromised, it can result in computer or server crashes or cause a number of computer issues. This can make the data incorrect, limited, or inaccessible. Compromised hardware can also cost a lot to repair, so it’s essential to ensure data on the hardware won’t be lost.
A transfer error can happen when data can’t be moved from one database location to another. With this type of error, data may be available only in the destination table but not in its relational database source table.
Preserving Data Integrity
Companies, organizations, and enterprises can try different methods to secure data integrity. The options range from technical to creative, and most of them are relatively simple to apply.
Popular ways of boosting data integrity include:
Limiting data access
Restricting changes by unauthorized parties
Logs that keep track of data changes
Regular internal audits
Investing in error detection software
Using services to prevent human error
Using assessments to track potential gaps
Data Integrity vs. Data Security
Both data integrity and data security play a crucial role in making one another successful. Data security is about protecting data from unauthorized parties or corruption. It is therefore obvious that data integrity is the ultimate result of data security. However, note that data integrity only refers to the accuracy and validity of data and not protecting data.
Data Integrity vs. Data Quality
Data quality answers the basic question of whether database information meets the standards defined by a company. The main interest of data quality managers is to ensure the data they collect and manage meets the needs of their business.
Data quality is a part of data integrity, much like data security. It encompasses all data quality aspects, including how you enter, store, transform, or manipulate data.
GDPR Compliance Data Integrity
General Data Protection Regulation (GDPR) is data protection and privacy law regulation originating in the EU and the European Economic Area. It’s one of the crucial components of the union’s privacy and human rights laws.
Data integrity is essential when it comes to complying with this regulation. If a company ignores these regulations, it can make them liable for penalties, and they can even get sued. Business owners need to ensure GDPR compliance data integrity to avoid having similar problems.
Data Integrity Examples
Data integrity examples involve just about any data stored inside a company’s server or database. For example, this can be customer information like addresses and credit card details. Other examples include medical records in healthcare offices, pharmaceutical companies’ data, SaaS data, etc.
Understanding Data Integrity
As you can see, data integrity is a crucial component of ensuring data inside an organization is available, organized, and stored properly. To achieve data integrity, companies need to apply a set of rules, processes, and methods for entering, managing, and changing data on their servers. After all, compromised data is one of the biggest concerns for any organization, and data integrity seeks to prevent this from happening.
Frequently Asked Questions
What is the meaning of data integrity?
Data integrity makes sure information isn’t corrupted and can be accessed by authorized parties. It includes consistency, accuracy, and trustworthiness of the data throughout its lifecycle.
What are the four types of logical data integrity?
Logical data integrity consists of domain integrity, referential integrity, entity integrity, and user-defined integrity.
What is a primary key?
Ideally, every table should have a primary key. The primary key makes it possible to identify each row of a table uniquely, but also to make sure there are no duplicate rows in a single table.
Author: Tibor Moes
Founder & Chief Editor at SoftwareLab
Tibor is a Dutch engineer and entrepreneur. He has tested security software since 2014.
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