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Data Integrity can be expressed as the consistency and trustworthiness of data over the course of its lifecycle. The status of your data, or the process of guaranteeing the quality and validity

1 – Standard Data Definitions

Need to have a single definition of all data types across the organization. Data being reported across the organization should be standardized in the format, applicability and validity to enable a clear and concise understanding.

2 – Cleaning and Monitoring Data

Bad data has a significant impact on data quality. Organizations should readily adopt a data cleansing approach to find, remove, and correct errors as much as possible. They should put into place an ongoing approach to maintaining the system’s health so that data integrity can be improved.

Verifying data against accepted statistical measurements is part of data monitoring and cleansing. Validating data against defined descriptions and finding linkages within the data are also part of the process. This approach also verifies the data’s uniqueness and assesses its reusability.

3 – Perform Risk-Based Validation

Ensure that data quality and reliability are addressed in procedures related to system operations and maintenance.

The ISPE’s GAMP5 (Good Automated Manufacturing Practice) categorizations should be used to estimate the validation complexity of your system and decide the appropriate level of risk and hence validation that is to be applied to a system.

During validation, keep track of all electronic data storage places, including printouts and PDF reports. Also, ensure that the frequency roles and responsibilities for system validation are defined in the quality management system. The approach taken to analyze important metadata, including audit trails and other details, should be outlined in the validation master plan. After  initial validation is completed, periodic re-evaluations should be scheduled based again on a risk based approach.

4 -Validate Input Source of Data

Validate the input source of the data. This is especially crucial if  data comes from an end-user or unknown source, another application, or third-party. To guarantee that the data inputs are correct, processes should be put in place to verify and validate them.

Periodic validation of  data is critical to ensure that data processes are not corrupted.

5 – Audit the Audit Trails

An audit trail needs to be a permanent record of all data in a system that includes all changes that have been made to the database or a file. The GxP-relevant data needs to be defined and changes to that data should be tracked in the audit trail. To be relevant in GxP compliance, an audit trail must be able to answer the following questions: Who? What? When? And why?

To test the audit trail functionality, assign responsibilities and schedules. The scope of an audit trail examination should be determined by the system’s complexity and intended use.

It is important to recognize that the audit trails are made up of discrete event logs, history files, database queries, reports, or other mechanisms that show system events, electronic records, or raw data contained inside the record. This represents data spread across multiple sources / locations and hence a good audit trail testing strategy should take into account all of these sources.

6 – Change Control

Ensure that system software enhancements, especially when incorporating new features, comply with evolving regulations. Also, keep up with changes by collaborating with providers and updating  systems as needed. Choose systems that are simple to upgrade when new hardware or other system inputs are added.

7 – Encrypting The Data

Encryption can also be a useful tool for maintaining data integrity across the organization. It assures that even if someone has access to data, they will be unable to view it unless they have the decryption key.

It’s useful in situations when attackers can quickly obtain files stored in a database by hijacking the server or acquiring files via database hacking.

8 – Perform Penetration Testing and Audits

Penetration testing, or having an ethical hacker try to break into the company’s database and uncover weaknesses, is an effective way to ensure data integrity. It will assist  in uncovering areas of weakness and allow for organizations to  resolve potential vulnerabilities quickly.

Organizations should also ensure security assessment includes the following elements:

9 – Develop Process Maps for Critical Data

The organization should also have control over how and where the data is being used, along with who is using it, in order to maintain data integrity.

Creating process maps for vital data is critical. This provides the organization with much better control over how the data is used. This will also support the business putting in place suitable security and regulatory compliance procedures.

10 – Promote a Culture of Integrity

Cultivating a culture of transparency, honesty, and integrity is also important to ensuring Data Integrity.

All those involved who touch the data of the organization need to take responsibility and ownership for that data. Everyone in the company needs to report situations where other members break the rules or fail to meet their obligations regarding the information.

These small measures will ensure that your entire organization stays on track and that your data integrity is preserved.

Summary

To help ensure Data Integrity it requires a focus on specific areas that help safeguard the data of the organization. It involves directing the company’s efforts towards a standard data definition or a single definition of the data across the organization. It also requires cleaning and monitoring the data to ensure find, remove, and correct all errors.

In order to ensure Data Integrity, there is also a need to do a risk assessment, a validate of the input source of the data, as well as an audit of the audit trail. Additionally, the organization needs secure the data by encrypting it and also performing penetration testing by ethical hackers. The organization also needs to also create process maps for critical data. This will provide much better control over how the data is used and support the business putting in place suitable security and regulatory compliance procedures. And last by not least, the organization should cultivate an environment of data integrity by ensuring all team members involved with touching or using the data take ownership of that data.

All of these steps assist in helping to guarantee data integrity and will assist in the organization having greater success in that area.

Why It Matters to You

Data Integrity is critical to an Life Science organization business. The quality of data is an imperative to ensure product quality and patient safety. In this blog, we discuss:

About Astrix

For over 25 years, Astrix has been a market-leader in dedicated digital transformation &  staffing services for science-based businesses.  Through our proven laboratory informatics, digital quality & compliance, and scientific staffing services we deliver the highly specialized people, processes, and technology to fundamentally transform how science-based businesses operate.  Astrix was founded by scientists to solve the unique challenges which science-based businesses face in the laboratory and beyond.  We’re dedicated to helping our clients speed & improve scientific outcomes to help people everywhere.