Certified Supply Chain Professional (CSCP) Practice Exam

What is the definition of data integrity?

1. Storing data in a secure location.

2. Ensuring data reflects the environment it represents.

3. Regular backup of data to prevent loss.

4. Using data mining techniques to analyze information.

The correct answer is: Ensuring data reflects the environment it represents.

The definition of data integrity is fundamentally about the accuracy and consistency of data over its entire lifecycle. It encompasses the idea that data should faithfully reflect the environment or context it represents, ensuring that it remains reliable, accurate, and true to its original source. When data integrity is maintained, it ensures that no unauthorized changes have been made to the data, which is crucial for decision-making processes, reporting, and analytics. This concept is vital in various fields, including supply chain management, where decisions are often based on data assessments. If data integrity is compromised, it can lead to incorrect conclusions and potentially detrimental business decisions. In contrast, while storing data in a secure location is important for protecting it from unauthorized access or loss, it does not inherently address whether the data itself is accurate or reliable. Regular backups are essential for data recovery; however, they do not ensure that the data reflects the reality it was intended to represent. Lastly, data mining techniques are useful for analyzing information, but they do not relate directly to the accuracy or consistency of the data itself. Thus, the core focus on the data's representation and accuracy is the distinguishing reason why the second option is the correct definition of data integrity.