Are you wondering about what sets data quality vs data integrity apart? Perhaps you see them used interchangeably. If so, we have the answers for you.
Both primarily refer to your organization’s data reliability. But since they focus on similar points, how exactly do they differ?
Here’s a quick guide to help you understand their differences and how they work. Let’s start!
What Is Data Quality?
Generally, data quality refers to how reliable your data is. It characterizes the reliability and value of your data for various operations, including planning and decision-making.
Additionally, your data should follow the criteria below to qualify as reliable:
- Complete
- Unique
- Valid
- Timely
- Consistent
Your data could get compromised if it fails to meet at least one. In this case, you should consider cybersecurity services to improve it. Usually, network security helps keep everything in order.
What Is Data Integrity?
Data integrity goes beyond quality by requiring data to be complete, accurate, consistent, and in the proper context. You can refer to it as the opposite of data corruption since it ensures data works effectively throughout.
Moreover, it consists of four primary components, one of them being data quality. Given that, it focuses on a broader reach when it comes to data protection and preservation.
How Are They Different?
One clear difference between data quality and data integrity is their scope. Besides this, here are a few more to consider:
Individual Purpose
Data integrity prioritizes protecting and preserving data in its original state throughout its lifecycle. You can say your data has integrity if it didn’t go through any alteration, like retrieval or unauthorized processing.
Data quality pertains to how well data serves its purpose according to specific criteria. So, it ensures that data is suitable and effective for the purpose it’s made for.
Impact on Data
The value of data to your organization primarily focuses on your operations. So, it’s vital to ensure it stays reliable.
Data integrity affects how your organization operates. If you lose reliable and regulated data, it puts the company at risk of several fines.
On the other hand, data quality can change its usability and effectiveness. When it fails to meet the criteria, it results in misguided decisions and inefficient operations.
Application
Data integrity plays a role in industries where information should be consistent and accurate. And so, it requires high data security and protection throughout.
Data quality applies to more industries but comes with specific requirements to ensure data remains effective. Usually, it serves as a key factor in making data-driven decisions.
Maintenance Methods
You should have different physical and network security measures to maintain data integrity. It should also come with access controls and regular system checks to keep track of changes.
Meanwhile, the organization must clarify the standards for data use cases. You can do quality checks according to those guidelines to determine issues and root causes and fix them before they cause an impact.
Learn the Differences Between Data Quality vs Data Integrity Here
Knowing how to set apart data quality vs data integrity helps you to determine which to prioritize to boost your organization’s efficiency and improve decisions. Moreover, it gives you an idea of its value and encourages you to enhance security measures. Start taking measures to keep your data safe and secure!
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