What Is Data Integrity?

Data integrity encompasses the accuracy, reliability, and consistency of data over time. It involves maintaining the quality and reliability of data by implementing safeguards against unauthorized modifications, errors, or data loss. 

Data integrity is crucial because it ensures the trustworthiness and reliability of high-quality data, enabling informed decision-making, efficient operations, and accurate analysis. It helps organizations maintain compliance with regulations, prevent data corruption or tampering, and preserve the overall integrity and credibility of their systems and processes. 

Data integrity is important within an organization for several reasons, including: 

• Reliability in decision-making: Accurate data provides a basis for reliable decision-making. If data integrity is compromised, this might lead to flawed analyses and conclusions, leading to potentially harmful decisions and actions. 

• Compliance and auditing: In many industries, particularly health care and finance, ensuring data integrity is not just good practice, but it's often required by law or regulations. These organizations often have strict regulatory compliance requirements related to what information they can collect and share from consumers and how they protect this information.

How do you maintain data integrity?

Organizations require the most up-to-date and accurate information to make the best decisions. But as new data enters their databases, how do they ensure that it doesn't lead to poor data integrity?

The answer is by conducting regular data integrity checks and practicing good data maintenance. Here are some of the ways that you can maintain data integrity in your organization:

1. Input validation

Input validation strategies can help prevent invalid or malicious data from being entered into a system. This includes things such as checking for human errors, removing duplicate data, and verifying data once entered. Having complete data entry training can help to prevent input errors.

2. Data governance policies

Establishing clear policies on data collection, storage, and processing is critical for maintaining data integrity. This might include rules about who can access and modify data, as well as the necessary procedures for doing so.

3. Data backups and recovery

Regular data backups ensure that, even in the case of data loss, you can restore an intact version of the data.

Data integrity vs. Data quality

Data integrity and data quality are closely related but meaningfully distinct terms. Data integrity refers to the reliability, accuracy, and consistency of the data contained within an organization, which relies on a series of rules, standards, and protocols to ensure the integrity of its data organization-wide. Data quality, meanwhile, refers to the accuracy of an organization’s data and its suitability for what it is being used for. 

In other words, data integrity is all about ensuring that workers have reliable access to the right data when they need it, while data quality refers to the actual accuracy, completeness, and suitability of that data for the tasks the worker is performing.

Types of data integrity

Two of the most common types of data integrity are physical integrity and logical integrity.

Physical data integrity refers to the ability to obtain accurate company data. This includes access to data, completeness of data, and prevention of factors that may lead to errors within data. Maintaining physical data integrity may include preventing equipment damage and creating safeguards against power outages, storage erosion, and hackers.

Logical data integrity refers to the ability to keep data consistent and accurate over time. This includes:

• Entity integrity: Identifying data correctly, including preventing duplicates or null values

• Domain integrity: Guaranteeing accuracy of data, including defining acceptable values

• Referential integrity: Storing data correctly and protecting against errors

• User-defined integrity: Constraining data created by users within requirements

Related terms

• Relational database

• Data wrangling

• AI ethics

• Metadata

• Data pipeline

Build your data skills with Google

By understanding the importance of data integrity and how to implement strategies to maintain it, you can improve the completeness and quality of your data while reducing errors. Build the job-ready skills you need for a career in data with the Google Data Analytics or Google Business Intelligence Professional Certificates on Coursera. Learn at your own pace from anywhere with an internet connection.