Now that we have established why data integrity is important, you might be wondering, “What are data integrity issues?”

Intentional or Unintentional

There are many different types of data integrity issues that may occur, and they may be intentional or unintentional.

Regardless of intention, data integrity issues can have severe consequences to both patients and companies.

Examples of data integrity issues include, but are not limited to:

• Intentional data falsification or manipulation
• Poor documentation practices that impact the reliability of the data
• Lack of control related to software, computerized systems or instruments
• Lack of a review process to ensure detectability of any data integrity gaps

Companies must remember that these potential issues could occur anywhere in the GxP environment.

Common Misconception

A common misconception is that data integrity only applies to the quality control lab or the manufacturing department.

Data integrity applies to every department and individual within the company.

Data Lifecycle

You will often hear the term “data lifecycle,” which is the sequence of stages that a particular unit of data goes through from its initial generation or capture to its eventual archival and/or deletion at the end of its useful life.

When you consider data lifecycle with regard to data integrity, it’s important to think in a holistic manner.

For example, it’s even important that research and development work performed for a health authority submission is considered within scope.