What Is Data Integrity?

Key takeaways

Data integrity is the principle of keeping data accurate, reliable, and consistent throughout its lifespan. 

• Ensuring data integrity helps maintain trust, prevent errors, and meet legal standards.

• Clear policies on data collection, storage, and processing are essential to uphold data integrity.

• You can enhance your skills in data analytics by improving your comprehension of data governance policies. 

Gain a deeper understanding of data integrity and how it compares with data quality. If you’re ready to start earning credentials right away, consider opting for Google’s Data Analytics Professional Certificate. You’ll have the opportunity to learn about data cleaning and data ethics, along with effective techniques for data visualization.

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 to ensure 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 to conduct regular data integrity checks and practice good data maintenance. Here are some of the ways that you can maintain data integrity in your organization:

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 prevent input errors.

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.

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.

Read more: Disaster Recovery Plans: What They Are and Why You Should Have One

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

• Artificial intelligence (AI) ethics

• Metadata

• Data pipeline

Check out our free data analytics career resources

Keep advancing your data analytics skills with Career Chat, Coursera’s weekly LinkedIn newsletter featuring top certification picks and career tips. Explore career paths in data analytics and gain insights from industry experts by subscribing to our other free digital resources:

• Watch on YouTube: 3 High-Paying Career Paths After Google Data Analytics Certificate

• Get expert tips: 6 Questions with a Microsoft Data Analytics Leader

• Hear from an insider: Meet the CPA Advancing Her Data and Leadership Skills with an M.B.A.

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