What is Data Integrity and Why is it Important to your Business?

Healthy, clean data can prove to be a major competitive upper hand for businesses that spend resources and time on their data management planning. The industry today lives in the age of data, and the companies that use data smartly and leverage data integrity can make efficient data-driven judgments, enhance data quality, and lower the risk of data loss and data corruption. We need to address questions like what can be done to ensure high standards of data integrity. 

What is data integrity?

In simple terms, data integrity is the consistency and accuracy of data throughout its complete life cycle, from the time it is collected and stored to when it is processed, analyzed, and used. 

Data integrity management means making sure data is accurate and complete, error-free, and anomaly free. These things will prevent compromising data quality. 

Data that has been stored accurately and consistently and recorded will keep its integrity. However, if data is distorted or corrupted, it can't be trusted for use in business. 

Importance of data integrity

Data integrity is vital for various reasons, one is being fundamental in regulated industries, where data should be complete, accurate, and verifiable at all times. Poor data integrity can result in businesses losing money, a positive public image and industrial reputation, and important production time. 

Data integrity risks

Data integrity is a sophisticated and complex issue. Data experts should be aware of the different threats that can affect data integrity and quality. These are the following risks associated with it:

Malware, cyberattacks, and insider threats 

• Human Error
• Compromised hardware 
• Security errors and misconfigurations
• Unintended tranfer errors

Controlling data integrity via data governance

To reduce various data integrity risks, data experts should make a strict data governance policy that consists of data integrity checks at each stage. This strategy may involve:

• Data literacy and security training for data users
• Robust cybersecurity measures
• Data quality assessments
• Data encryption to secure data
• Data backup practices and data redundancy to maintain data reliability

Data integrity types

There are two types of data integrity types to maintain high standards:

1. Physical integrity

Using physical means is important for data processing and retrieval to perform as intended. While software-based tools can provide a critical layer of security, you should also use physical means to keep data clean and complete, even when an organization is faced with an outside threat. 

2. Logical integrity

It makes sure that data remains unchanged. In a relational database, it helps in keeping data safe from malicious threats and human Error.