What is Data Integrity?

Data integrity refers to the fact that data must be accurate, reliable, and consistent over its life-cycle. Data integrity and data security go hand in hand, even though they have separate concepts. 

Maintaining or keeping data consistent throughout its life-cycle is a matter of protecting its security so that it will be reliable. Uncorrupted data is considered to be complete and then stays unchanged.

Data integrity has become crucial over the past few years and therefore is a fundamental focus of many enterprises. The FDA (Food and Drug Administration) published a Data Integrity Guidance Document outlining compliance with CGMP (Current Good Manufacturing Practices) that talks about the role of data integrity for the industry. The document is an outcome of the FDA increasingly observing violations involving data integrity during inspections. These violations have led to FDA warning letters. 

The FDA has developed the abbreviation ALCOA to define data integrity standards:

Attributable (A): Attributable data means organizations should know how data is created or obtained, and who is handling it.

Legible (L): Legible data means organizations should be simple to read and understand the data, and the records are permanent.

Contemporaneous (C): Contemporaneous means organizations should know how data appeared in its initial state and what happened to it throughout the stages of its life-cycle.

Original (O): This means, an understanding of data’s source system and the ability to keep source data in its original state.

Accurate (A): Accurate data means errorless and conforms to the protocols of the applications for which it is used.

Tips to Reduce the Risk of Data Integrity

Companies need to be confident that there is no loss of quality when using computer systems and procedures like ISO 20022 migration, which is mostly used by banks for payment systems. The risk of data integrity can be unproductive for data-driven processes, and it is essential to reduce these threats. Data integrity risk can’t be reduced through only one approach. It requires multiple methods to reduce the risk associated with data integrity. 

1. Follow a Software Development Cycle 

A software development life-cycle allows you to manage the data flowing in the whole organization. It helps you to check whether the data is being used under set guidance and protocols and if any error is found, then the set actions will be taken accordingly.

2. Adapt the Culture of Integrity 

Promoting the culture of integrity may reduce the risk of data integrity in several ways. It makes workers do their work honestly and keep the check on other employee’s work too. In a culture of data integrity, it also helps workers report instances like another employee not doing their work with honesty or taking shortcuts.

3. Implement Error Detection Software 

Error detecting software can help to store error-free data as this software allows you to detect those errors in your data, which makes it non-reliable and non-accurate. It confirms the data accuracy and data should be error-free. This can be a beneficial tool for protection.

4. Keep a Backup Plan 

Keeping a backup of data will help you in many ways as it will allow you to recover the lost data in case of any unpredicted event. Backup can be recovered at any time without any worries, and all the lost data and information will be gained back. There can be delicate and important data that should not be lost. But if any error or interruptions occur, then it can be reversed if it has its backup stored.

5. Quality Control Measures 

Quality control measures require a person or a process to verify the work of the employees in an organization. It will keep the check that workers are working according to the guidelines and given measures or not. Such as IT personnel can monitor security systems for data integrity.

Conclusion

The cloud integration is the future of how businesses will ensure data integrity in the years to come. Such inclusive solutions accelerate the integration of big data, implement governance measures for data lakes, and automate critical aspects of data management. The security of data must not be compromised, and the organizations must take all essential steps to ensure data integrity. They should also have measures for troubleshooting and monitoring aspects of data management, which are vital to governing compliance, security, and data quality.

By now, you are aware of how important it is to ensure that your data stays safe from the hands and clutches of hackers and criminals. The impact of dangerous data can have drastic consequences on any organization, no matter the size. However, if data integrity is the inspiration behind the initiation of the process, the data infrastructure can become a skill instead of an obligation.

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