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Data Analysis

Data Integrity: Definition, Importance, and Types

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September 7, 2021

Apart from data security, companies are now learning the significance of investing in data integrity. This concept goes beyond ensuring that your data is safe – in fact, strong data integrity practices are required by law, as errors or mistakes can lead to significant fines for your company. Privacy laws and GDPR (General Data Protection Regulation) now require firms to go beyond data security and ensure the accuracy of their data. The following sections deeply explore the importance of data integrity and its key elements so you can preserve your company’s integrity, reputation, and avoid all setbacks. 

What is data integrity?

Data integrity refers to several data characteristics, including accuracy, consistency, and completeness. It is also related to regulatory compliance and data security and is ensured through various processes, standards, and well-established rules. 

In many cases, data integrity relates to data quality, as one of the overarching goals of data integrity is to ensure data quality. When data integrity is guaranteed, it means that your company’s database is complete, accurate, and fully reliable. This does not only help you access error-free data to improve your operations but also makes sure you avoid regulatory non-compliance that can result in setbacks and fines. 

Why is data integrity important?

  • First of all, it ensures that your company is not susceptible to data leaks or losses. To ensure that your internal data remains safe, you first need to check if your data is handled correctly. Implementing error checking and validation processes means that sensitive data is correctly categorized and stored, reducing risks.
  • Secondly, data integrity means that your company stored correct, up-to-date, and reliable information. Using biased or wrong information to make business decisions could cost your organization money, time, and effort. All data-driven decisions that are successful must be based on accurate, consistent data. If your company’s database is compromised or contains errors, the negative consequences can be extremely long-lasting.
  • Thirdly, in addition to enabling you to make the correct business-related decisions, data integrity is also important for your organization’s image. For example, many companies collect their customers’ PII (personally identifiable information), including full name, address, SSN, and banking information. Let’s suppose a mistake occurs in this data set. Whether it be an innocent typing mistake or a hacking attempt, either way, your customers’ personal information can be compromised or leaked, ending up in the wrong hands and used for malicious reasons.
  • Finally, companies may store customer information that is not so sensitive, like their credit card information, but this is also extremely valuable. For instance, if you track your clients’ actions on your organization’s website or ask them to provide feedback, any errors in your data sets could mean loss of business as you incorrectly categorize or target your customers. 

As a result, data integrity is not only important from a legal perspective. Its consequences are far-reaching, improving your relationship with your customers, maintaining a positive brand image, and helps to keep your company resilient to external attacks.

Coresignal ensures the quality and freshness of the data; therefore, you can rest assured it's always accurate and up-to-date.

Types of data integrity

Data integrity is of different types – they all consist of a series of methods and processes that ensure data integrity in relational and hierarchical databases. 

Physical integrity

Physical data integrity refers to protecting the data as you store and access it when needed. For instance, it relates to your ability to maintain the database functions when the electricity goes out in case of a natural disaster or if a hacker were to compromise the database. 

Keeping accurate data at this stage is threatened by storage erosion, human error, and others. This is where DaaS, or data as a service, can bring numerous benefits and cut costs while ensuring data security. 

Logical integrity

Logical integrity ensures that data remain unchanged in your database. It also aims to protect the information against human mistakes or errors while keeping hackers at bay. There are four types of logical integrity:

  • Entity integrity consists of creating primary keys to avoid data duplication or null values. 
  • Referential integrity contains several processes to ensure that your data are used and stored efficiently, avoiding changing, adding, or deleting the data by mistake. 
  • Domain integrity means that each data piece in the domain is accurate. For instance, each column may contain only a specific type of data in terms of format, type, or even amount of information. 
  • User-defined integrity contains rules or constraints that allow the user to make the information suit particular requirements or needs. 
Computer with code and data

What is an example of data integrity?

Depending on your company, data integrity processes can be extremely varied. For instance, let’s assume that you have a software company that stores all the information related to your current customers (known as “parent” records) and the products they use (known as “child” records). 

Data integrity can be used to ensure that all customers are assigned one or more products. Also, the process may ensure that no customer can be deleted as long as they own any child records (i.e., they are currently using one or more products or services). 

Data integrity helps to avoid human errors, such as deleting current customers by mistake. Suppose the company allows each customer to access only one product or service at a time. In that case, data integrity mechanisms can be employed to ensure that the company’s data reflects that no customer is associated with two or more products. Based on this, your data is more accurate, so you can better target your customers and create successful marketing campaigns or providing relevant promotions to the right clients. 

How to assess data integrity

There are several crucial metrics you could identify to assess the current situation of your data in your systems. This will also tell you more about the quality and usefulness of your databases. Some of them include:

  • Complete datasets are crucial as completeness is a key characteristic of data integrity; incomplete data sets can lead to mistakes and biased results. 
  • Data consistency refers to recording data the same throughout the entire organization; for instance, whether all numbers are formatted the same way or all customer addresses follow the same format. For example, some customers may have the state they live in as the address, and others contain street and house numbers. The lack of data consistency could render your data useless as it may not be possible to compare your information.
  • Timeliness is another characteristic of data integrity. All of the relevant data should be collected at the right time. For instance, you should not make business decisions today using the data collected five years ago as it may not be still relevant. 
data security

Data integrity vs. Data security vs. Data quality

It is important to mention that data integrity is not interchangeable with data quality. Rather it is a subset of data quality. For example, data integrity makes sure that there are no duplicate values in your dataset. This improves the quality of your data. 

However, having no duplicates does not guarantee that your information is correct – i.e., in the example above, a customer may be incorrectly associated with a product they don’t use. However, appropriate data integrity helps you avoid mistakes and errors, which makes it a subset of data quality. 

Another related but different concept is data security. Data security refers to protecting your information against unauthorized use, corruption, or access. Data security is crucial to ensure data integrity. In simple terms, data security can be used to maintain data integrity. 

For businesses, data integrity is vital to ensure that all the decision-making and business processes are accurate and efficient. Many data security programs include data integrity processes, such as database integrity constraints, backup, and others. 

How do you maintain data integrity?

Data integrity must be maintained in order to ensure the performance and stability of your company. For instance, if you maintain your data integrity, you can easily recover or search past information, you can trace data to its origins when needed. 

More importantly, your company’s databases most likely include both structured and unstructured data. This means that your data has to go through other transformations and processes to become practical to facilitate decision-making properly. As a result, data integrity maintenance is a must for enterprises. It allows you to base your decisions on sound, accurate information and track said information to its origin when required, among others. 

There are several ways data can be compromised, which is why data integrity maintenance is absolutely critical. Some of the main ways to compromise data include human errors, which is perhaps the most common phenomenon. For instance, when humans insert incorrect data, delete it by mistake, or duplicate it, data integrity is compromised, affecting your data quality. 

Errors can also happen during transfers. For instance, lack of completeness and duplicate errors can occur if you need to transfer data from one department to another. Additionally, manual transfer of data from one department to another increases the risk of compromising data accuracy. 

Next, data storage nowadays makes use of software and hardware (computers). These may be prone to malware, spyware, or viruses that can corrupt, delete or steal the data. Additionally, computer and server crashes can also occur, leading to massive failures that could alter or corrupt your data. 

There are several data integrity practices that help you maintain it, including:

  • Error detection mechanisms or data validation processes for improving data accuracy
  • Security measures, including loss prevention, monitoring malicious intent, and data encryption
  • Validating data to make sure it’s correct and meets high-quality data standards
  • Protecting data by backing up data frequently
  • Use logs to keep track of when the data are added, edited, or deleted 
  • Conducting internal audits for ensuring data integrity and tracking unauthorized access
  • Use error detection software for all data stored, both on-site and via cloud


Data integrity is not an easy one-time process. Instead, it is an ongoing procedure that requires regular commitment to ensure that your company’s information is safe and accurate. Apart from regulatory compliance, data integrity has numerous other benefits for your company. For instance, it allows you to have access to reliable data to make business decisions. 

Additionally, data integrity means that you have trustworthy datasets, and you can recover or trace information back to its origins when needed. There are so many risks and factors that can easily alter or corrupt your data, so it is important to ensure that you maintain data integrity via strong data security systems to ensure data quality. 

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