data integrity principle

 The principle of data integrity refers to the accuracy, completeness, and consistency of data throughout its lifecycle. It is a fundamental principle in many industries, particularly in regulated industries such as pharmaceuticals, medical devices, and food products.

The principle of data integrity includes several key concepts, such as:

  1. Data must be complete, accurate, and reliable. This means that the data must reflect what actually happened and be free from errors, omissions, or intentional manipulations.

  2. Data must be attributable and traceable. This means that data must be linked to the person or process that generated it and that there must be a record of who performed the task and when it was performed.

  3. Data must be contemporaneous. This means that data must be recorded at the time of the activity or observation, and not at a later time.

  4. Data must be original. This means that data must be recorded in the original format or a copy that is accurate and complete. Alterations must be clearly documented and approved.

  5. Data must be consistent. This means that data must be consistent with other data generated during the same process or activity.

The principle of data integrity is critical for ensuring the safety, efficacy, and quality of products, particularly in regulated industries. Companies must implement robust systems and procedures to maintain data integrity throughout the data lifecycle, from data generation to data processing, analysis, and reporting. They must also ensure that all employees are properly trained in data integrity principles and best practices.

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