Data integrity is the overall coherent, complete, unaltered, unpenetrated, and accurate state of data. It’s a crucial part of database management and ensures that data is always in a perfectly raw state and open for unbiased analysis and interpretation.

In this blog post, we are going to cover the basics of data integrity, what it means, what are the best practices, and more.

Let’s get started 🚀

Why is Data Integrity Important?

Numbers don’t lie. This is a proverbial phrase often used in defending the virtue of raw data. However, if the data isn’t registered, stored, or maintained properly, numbers can paint an inaccurate picture and could lead to incorrect interpretation or waste of human resources.

Consider this as an example:
A food processing corporation is launching a new baby food product. However, when authorities go for an inspection before the product goes to market, it turns out that the data on experimental ingredients are inconsistent and a lot of it is missing. 

As a result, the product launch will be stalled for months while a lot of time and capital will have to be allocated to get the required data for inspection.

This sort of situation is more than common and can be directly blamed on poor data integrity. 

Types of Data Integrity 

There are primarily two types of data integrity—Physical and Logical. 

Physical Data Integrity

As the name suggests, physical data integrity is about protecting data from physical harm. It can range from power shortage, natural disasters, server room short circuits, physical break-ins, data manipulation by adding or removing datasets on hard drives, etc. 

Logical Data Integrity

On the logical size, it safeguards data integrity from a myriad of digital threats from simple errors to security breaches. 

Entity integrity: Entity integrity involves the creation of primary keys that make sure that the data registered in the database is unique and not repeated, or void tables. It’s a feature of the relational database which enables the data to be stored in tables and be interlinked.

Referential Integrity: Referential integrity focuses on the storage and maintenance of data to make sure that it’s stored uniformly and in a coherent manner to make it easily searchable. It enforces rules in the database to manage foreign keys and assures that only intended data addition, deletion, and changes take place. 

Domain Integrity: Domain integrity is about ensuring the uniformity of data in a specific domain. Certain domains come with a specific set of constraints that allows only certain data types and formats to be stored while restricting other entries. 

User Defined Integrity: User-defined integrity is a type of integrity put forth by specific users to facilitate the storage and management of data. Oftentimes standard data integrity procedures aren’t enough to store data, as a result, businesses create customized rules to add, change, or delete data. 

Potential Risks for Data Integrity

The potential risk for data integrity is high and can affect a business in several forms as listed down below.  

Human Error

Human error is the most prevalent risk for data integrity. When individuals in charge of data entry or data management make an error while adding data, managing data, or failing to implement proper measurements, it leads to human error. 

Transfer Errors

When data fails to be transferred from one specific location in a database to another, it leads to a transfer error. The most common transfer error is data being located at the destination table while the source returns empty results in database management. 

Bugs or Malware

If bugs or malware are detected in the database, it can jeopardize data and render the database useless.

Compromised Hardware

Compromised hardware is another risk to data integrity, however, it scores low on the list. It happens when the hardware used to store and manage the data malfunctions and the data gets completely deleted in the process.

Best Practices

To avoid the risks mentioned above and maintain data integrity, follow these steps. 

Validate Input

Always validate the data that is being stored. All data input coming from known, unknown sources, third-party platforms, or other databases must go through a mandatory validation process.

Remove Unsecured Data 

It is critical that sensitive data are always removed from external sources such as public platforms, messages, emails, and unsecured storage locations. Data important to business or research can prove fatal if leaked to other interested parties or in the public domain.

Back-Up Data

Backing up data is of utmost importance to keep the database always updated and free from all sorts of risks. Backing up will prevent data from getting permanently lost or being stored in obscure locations. It’s also helpful in the case of ransomware attacks.

Access Controls

Create a classification order for data access to prevent unspecified personnel from accessing sensitive data. Implement a least-privileged approach to data access to make sure that personnel only get access to data that is necessary to perform their duties. 

Maintain Audit Trail

If you can’t prevent a data breach, the next best thing is to identify the source of the breach. Audit trails help understand the nature of a breach and possible sources to nullify the threat with minimum consequence.

Data Integrity Vs. Data Security?

Data integrity and data security are closely related terms, however, these are slightly different. 

In simple terms, the goal of data security is data integrity. 

Data security ensures that the data is stored and maintained in a secured space without any external threats, or breaches. It also ensures that data is properly managed internally and isn’t manipulated or presented in a biased manner. 

If data is secured at the highest level, then it reaches perfect data integrity. The goal of data integrity is to present data in its raw and unaltered form.

Wrapping it Up

Today everything is data-driven; education, health, commerce, and tech, today we understand everything through the lens of data. Data is what provides us with accurate contexts of the past and present while providing insights into the future. 

However, if that data is stored or portrayed incorrectly, it severely affects the interpretation of the data, therefore, leading to an incorrect conclusion. Data integrity makes sure that data is always presented in its purest form and not altered or manipulated whether intentionally or accidentally.