What Is Data Integrity? How It Makes Your Business Trustworthy

Data is the lifeblood of business. Protecting it has never been more critical.

One of the things that makes your business unique is the data you work with. Your data is not only one of your most valuable assets, but it’s also a core part of your brand and marketing strategy. You can't make informed decisions on clients, products, and campaigns without the correct information.

Whether you’re a huge corporation or a small startup, having clean, up-to-date information is crucial for accurate results. The need for accurate data doesn't end at just its usability; it also needs protection from potential risks, such as a data breach that could tarnish your brand's image permanently.

But how do you ensure that your data is accurate, relevant, and actionable at all times? One word: data integrity.

Data integrity is vital to protecting information against theft or corruption. It ensures that data hasn’t been corrupted or damaged in any way and that the information hasn’t been changed or deleted unnecessarily. It also verifies that there are no duplicate data entries and is linked correctly to enhance data quality. Data integrity practices also ensure that encrypted data doesn’t become damaged while being transmitted over unsecured networks (like the Internet).

Data as a whole can be in an invalid state or its important components may be missing. When the required information isn’t correct or complete, it can’t be relied on. When organizations achieve data accuracy, they can make informed decisions that contribute to their overall success.

Four pillars of data integrity

When it comes to data integrity, it must address four key pillars. Recognizing that data is a critical corporate asset, intelligent business executives develop explicit frameworks to apply these four integrity pillars.

1. Data integration: Data must be integrated regardless of where it originated – legacy systems, relational databases, or cloud storage, to provide a single perspective that can instantly give organizations an increased insight.
2. Data quality: Data must be comprehensive, unique, valid, consistent, and timely to make relevant business decisions. Inaccurate or incomplete data can lead to invalid results.
3. Data enrichment: By supplementing internal data with external data, businesses can give their data more context, meaning, and nuance. Adding relevant information, such as client data or location intelligence, enriches and contextualizes your data, allowing for more sophisticated analysis.
4. Location intelligence: Location insight and analytics provide a layer of depth and complexity to data, making it far more actionable across the company.

Why is data integrity important?

The potential for errors increases with the volume of data. A slight data loss due to corruption can have significant consequences, such as a customer may be charged twice for the same purchase or a pharmacist may give the wrong medication to a patient.

Preserving data integrity is critical for a variety of reasons. For starters, data integrity ensures that data is recoverable, discoverable, and consistent throughout the organization. Protecting data validity and precision improves stability and performance by increasing its reusability and maintainability. This means that when data integrity practices are robust, the data held in a database remains complete, accurate, and dependable regardless of how long it is held or how frequently it is accessed.

Data integrity is an essential concept in database management. It ensures data protection from corruption, whether accidental or malicious. Real-time data is essential to make the best business decisions. This allows you to keep an eye on your company's efficiency in the marketplace and competitors' performance. Using master data management (MDM) software, businesses can track essential data points and provide data-driven insights.

The importance of data integrity also increases with the volume of data. Since data integrity is an essential component for business intelligence and cybersecurity, both processes become unreliable in the absence of accurate data.

This is a significant concern for businesses and organizations heavily dependent on information technology, like financial services firms and hospitals. Data integrity is also important to consumers, particularly those with sensitive information at risk. For example, cancer survivors need to manage their continuously updated treatment records. 

How can data become corrupted or inaccurate?

Most businesses collect data in a variety of ways, including the following:

• Customer relationship management (CRM) systems
• The company website
• The back-end database

Many businesses employ a mixture of these data-collection strategies. Because the data collected from the above sources is constantly generated, moved, transmitted, and updated, it  can get corrupted or erroneous in a variety of ways.

Data corruption in computer systems can be classified into two categories:

1. Undetected data corruption: Often known as silent data corruption, this type of data corruption can lead to the most dangerous errors.
2. Detected data corruption: This type of data corruption can be permanent, resulting in data loss, or it can be transient, resulting in no data loss if some portion of the system can identify and repair the error.

Data loss and corruption can happen at any point in a system, from the host to the storage media. Most modern systems can identify corruption at different stages of data transfer and then attempt to recover or fix the corruption.

Data, for example, might be destroyed while being transported over a network or to a storage device. It can also become corrupted due to a breakdown of computer hardware.

Data can become defective because of a faulty computer system, such as a bug in new software or cybercriminals utilizing malware that lead to data breaches. Data can also be lost or corrupted due to a simple human error.

There are so many ways in which data can become tainted that it's practically rational to anticipate that it’ll happen at some point. When data becomes corrupt, the decision-making process can produce flawed, useless, or even harmful results for the organization.

Data integrity increases the trustworthiness of data. Whatever the potential consequences, data integrity can help protect your data from corruption and keep it from becoming permanently unusable.

Data quality vs. data integrity

A company's data is its most valuable resource, but only if you can trust that data. Unreliable data can lead to erroneous conclusions, prejudiced observations, and inadequate recommendations.

The term data integrity can refer to a condition or a technique and is interchangeably used with the term data quality. However, the terms data integrity and data quality are not synonymous. Any company attempting to increase the quality, consistency, and authenticity of its data must understand the distinction between the two to make informed judgments.

Data quality

The term data quality refers to the reliability of the data. If data is to be called high-quality, it must meet the following criteria:

• Complete: The data given represents a significant portion of the overall quantity of data required.
• Unique: It’s free of duplicated data entries.
• Valid: The data adheres to the syntax and format specified based on the business needs.
• Timely: The data is always up-to-date for its intended application.
• Consistent: It’s represented consistently throughout the data repository.

Data must meet all of the above conditions to be considered high-quality. If it’s deficient in even one area, it may jeopardize any data-driven endeavor.

Nevertheless, having high-quality data doesn’t always guarantee that it’ll be beneficial to an organization. For example, you could have an accurate and legitimate data repository of client names. Still, if you don't have supporting data about those clients and their relationship with your business, then the data warehouses aren't as valuable as they could be. For high-quality data to be valuable, it must represent the real-world construct that it depicts. This is where data integrity comes into play.

Data integrity

While data quality refers to whether the data is consistent and correct, data integrity extends beyond the concept of data quality. Data integrity necessitates that the information must be comprehensive, accurate, and contextual. The integrity of the data is what makes it worthwhile to any business.

Data integrity is important because when it comes to data management, the last thing you want is bad or corrupt data. Even though data quality is an essential subset of data integrity, enterprises that want to make accurate data-driven choices must consider both data quality and data integrity.

Data security vs. data integrity

The terms data integrity and data security are closely tied. Each plays a vital role in the other's success.

Data security

Data security is primarily concerned with preventing unauthorized data access and data corruption. It entails using systems, methods, and procedures to keep data out of the hands of those who could misuse it in harmful or unanticipated ways. Data security breaches might be tiny and easily contained or huge and cause substantial harm.

Maintaining the integrity of data is the fundamental purpose of data security. Data security techniques include identity authentication, data classification, encryption, masking, threat detection, and security analytics.

Data integrity

Data integrity is concerned with the validity of consistent data across the organization based on the business rules and needs. It also relates to the data's safety and security regarding regulatory compliance, such as GDPR compliance, but it’s not the same as data security. Data backups and error-checking data are some methods for preserving integrity.

Types of data integrity

In a general sense, data integrity is a set of procedures required to maintain the health and upkeep of digital information. Data integrity is classified into two types: physical integrity and logical integrity. Many people associate the categories with database management.

Physical integrity

Physical integrity is concerned with safeguarding the completeness and accuracy of data and deals with how it’s stored and accessed across the organization. Physical integrity is jeopardized during natural calamities, compromised hardware, or cyber attacks that interrupt database functionality.

Data processing managers, programmers, and internal auditors may be unable to access correct data due to transfer errors, storage degradation, and a variety of other difficulties. Ensuring the physical security of equipment and creating a disaster recovery strategy can help business executives mitigate physical integrity challenges.

Logical integrity

In a relational database, logical integrity ensures that the data remains intact when used in the organization. Logical integrity, unlike physical integrity, protects the data from human errors and hackers with malicious intent.

How is data integrity compromised?

Data integrity can be endangered in various ways, making data integrity practices major elements of effective enterprise security practices.

A dataset's integrity may be compromised due to:

• Malicious or inadvertent human error, such as if an employee corrupts data with malicious intent
• Transfer issues, such as unexpected power failure or data corruption while transferring from one device to another
• Bugs, viruses or malware, hacking, and other cyber attacks
• Hardware failure, such as a device or hard drive crash

Because only some of these vulnerabilities can be avoided with data security, data backup and data duplication at secondary locations become crucial for assuring data integrity.

How to ensure data integrity for businesses? 

Data integrity threats are so detrimental to enterprises and data-driven operations that  businesses must implement various strategic steps and processes to ensure the success of data integrity across the organization. Your data integrity process should be thorough in complying with legislation, support your data throughout its life cycle, and guarantee that internal standards are fulfilled.

However, because it's tough to reduce data integrity risk with just one strategy, combining multiple strategies is preferable. The following are some of the most successful methods for ensuring data integrity and lowering risks:

• Internal audits and audit trails: Before you begin developing your data integrity policies, you should conduct an internal audit. You may think that you grasped the quality of your data, but it may not be where you believe it is. Audit trails are essential for understanding what occurred to data at each step of its lifetime, including where it originated and how it was changed or utilized. Conducting frequent audits can help optimize the data integrity process.
• Automated error detection: Mistakes happen all the time. Error detection software can assist businesses monitor and isolate errors. Along with automation, such software can help with frequent data validation and identify why errors happened and avoid them in the future. This entire procedure is essential for minimizing the risk associated with data integrity.
• Data backup and recovery: Data backup and recovery are integral parts of the data integrity process that help prevent irreversible data loss in an unanticipated disaster. Businesses can decide how frequently they want to back up their data. A managed service provider (MSP) can help organizations determine the best option for them. Backups are crucial when businesses are frequently targeted with ransomware attacks. The lost data can be restored from the most recent backup, maintaining the database integrity.
• Eliminating known security flaws: It’s necessary to reduce security vulnerabilities to decrease data integrity risks associated with preserving data assets. Vulnerability management is a great way to identify known security vulnerabilities and execute countermeasures.

Empower data-driven decisions

Inaccurate data can have major ramifications for businesses. Hence, data integrity as a process should be a top concern for any firm, regardless of its size.

Data integrity is a continuous process that necessitates a daily commitment to safeguarding the clients' personal information or of the business to provide your organization's stakeholders with the best quality, most comprehensive, and accurate data for decision-making.

The data created, collected, stored, and shared by your organization is an important asset. Use data security to safeguard your data from corruption and unlawful access.