What Is Data Integrity?

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

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

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

• Reliability in decision-making: Accurate data provides a basis for reliable decision-making. If data integrity is compromised, flawed analyses and conclusions may result in 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 organisations often have strict requirements for what information they can collect and share from consumers and how they protect it.

How do you maintain data integrity?

You can maintain data integrity through input validation, data governance, and effective backup. 

Input validation

Input validation strategies can help prevent invalid or malicious data from entering a system. These strategies include checking for human errors, removing duplicate data, and verifying data once entered. Complete data entry training can also help prevent input errors.

Data governance policies

Establishing clear data collection, storage, and processing policies is critical for maintaining data integrity. These might include rules about who can access and modify data and the necessary procedures.

Data backups and recovery

Regular data backups ensure that an intact version of the data can be restored even if it is lost.

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

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Data integrity ensures data accuracy and consistency throughout its life cycle. This is crucial for reliable decision-making, legal compliance, and data trustworthiness. Maintaining data integrity involves practices like input validation, data governance, and backups.

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.