Data Integrity: What It Is & Why It Matters

Data integrity is becoming increasingly important as business data competencies mature. From companies leveraging data to drive business decisions to individuals relying on secure data storage — it's clear that having trust in the accuracy and consistency of your data is essential.

With the rise of applications using data for business analysis, there's a greater emphasis on having strong data integrity. Therefore, it's critical to understand what data integrity is and why it matters.

Read on for a full introduction to data integrity.

What is Data Integrity?

Data integrity is the term for how accurate, complete and consistent data is over its entire life cycle. It ensures that data is trustworthy and reliable for its intended purpose. If you’re data lacks integrity, you cannot be sure of any reliable data analytics outputs.

Achieving data integrity involves implementing processes and controls to prevent and detect errors, unauthorized access and data loss throughout the data's lifecycle. This maintains the credibility of your organization and helps to prevent data breaches. Data integrity also plays into your overall data quality.

Proper data validation and error checking can prevent sensitive data from being wrongly categorized or stored, reducing the risk of potential exposure.

(See why integrity is one part of the CIA triad for information security.)

Why it matters: the importance of data integrity

Data integrity is the foundation of all data-driven decisions. Without it, organizations cannot trust their data to make informed business decisions. Organizations must understand the importance of data accuracy and consistency to prevent errors in their business processes.

By ensuring that the data they are capturing and storing is accurate, complete, and consistent, businesses can ensure the integrity of their data and reduce the risk of potential exposure. This also helps to protect organizations from reputational damage resulting from data breaches.

In addition, customers have come to expect that their data is kept safe and secure. By ensuring that your organization implements strong data integrity practices, you can protect your customer's trust in your business.

Having strong data integrity practices in place is essential for organizations to remain competitive and secure in the digital age.

Data integrity vs. data security

Data integrity is often confused with data security, but there are some key differences between the two. 

  • Data security refers to protecting your data from unauthorized access and malicious attacks. 
  • Data integrity focuses on maintaining the accuracy of your data. 

Both are important for ensuring that your systems run smoothly, they differ in how they protect and control your data. 

Types of data integrity

Data integrity has many different types, each with unique characteristics and benefits. The most common types of data integrity are:

  • Physical integrity
  • Logical integrity

Physical integrity

Physical integrity involves ensuring that physical systems or hardware are protected when a physical disaster occurs. This protects the wholeness and accuracy of data as it is stored and handled.

Data corruption caused by lost writes can be prevented through physical database integrity-checking mechanisms. These include error detection and correction algorithms. For example, if corruption is detected while data is in transit and cannot be fixed, the I/O request will fail, and the integrity of the data is kept.

(Read about disaster recovery planning.)

Logical integrity

Logical integrity prevents data from being changed when being used in a relational database. This also ensures that data is consistent and accurate.

In general, logical integrity can be divided into four main categories:

  • Entity Integrity ensures that each row within a table is uniquely identifiable. Uses primary keys to ensure integrity.
  • Referential Integrity ensures that the relationship between two tables is maintained through rules embedded in the database structure.
  • Domain Integrity determines what values are accepted within a column and how they are stored.
  • User-Defined Integrity is a set of rules defined by users to ensure data is accurate. This can be customized to suit business rules and needs.

Common factors that Lead to Data Integrity Issues

Now that we can define data integrity, let’s look at what happens when you have integrity problems. While data integrity issues can occur from many different sources, a few common factors often lead to them.

  • Human error. Human error is one of the most common causes of data integrity issues when data is entered or deleted from databases.
  • Transfer errors. Errors occurring while transferring data between systems can lead to integrity issues.
  • Bugs and viruses. Bugs and viruses can cause data loss or corruption when they invade a computer system.
  • Compromised hardware. If the hardware storing your data fails or crashes, access to data may be limited or completely inaccessible.

If these common causes of data integrity issues are left unchecked, it can lead to serious consequences. These issues include:

  • Inefficiency
  • Financial losses
  • Damaged reputation for businesses

It is, therefore, important to take preventative measures and protect your data from these potential threats.


Identifying & preventing data integrity issues

Improving your data integrity begins with identifying the key issues present in your data. Here are some ways to identify data integrity issues:

  • Check for data discrepancies. Make sure that your data is accurate and consistent. For example, check that the same item isn’t listed twice with different prices or descriptions.
  • Run periodic audits. Periodically audit your system to ensure that all data is up-to-date and accurate.

Once you have identified the key issues, your next step is to prevent them from happening. Here are some prevention measures:

  • Implement data security practices. Put strong data security measures in place to protect against unauthorized access and intrusion.
  • Secure backup processes. Back up your data regularly to ensure it’s secure and recoverable in a disaster.
  • Perform checksums. Checksums are done on individual base tables to look for corrupted data.
  • Implement data validation rules. Put in place rules that validate the data entered into your system. This ensures that all data is correct and accurate — before it’s stored.
  • Put in place reporting systems. Set up reporting systems to track changes in your data and identify potential errors.
  • Train personnel on data integrity. Ensure all personnel handling the data are trained on proper data storage and security practices.

Final thoughts

Data integrity is an important aspect of ensuring the accuracy and security of data, making it essential for organizations to maintain proper data integrity practices. With the right tools and processes in place, you can ensure your data is always secure and reliable.

Organizations can benefit from accurate, complete and consistent data by taking the time to assess and address data integrity issues. A robust data resilience strategy will enable businesses to manage their data assets for long-term success better.


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Austin Chia
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Austin Chia

Austin Chia is the Founder of AnyInstructor.com, where he writes about tech, analytics, and software. With his years of experience in data, he seeks to help others learn more about data science and analytics through content. He has previously worked as a data scientist at a healthcare research institute and a data analyst at a health-tech startup.