When discrepancies are found in data abstracted by two professionals, which data quality component is lacking?

When discrepancies arise in data abstracted by two professionals, the component of reliability is lacking. Reliability refers to the consistency of the data across different instances or professionals. If two individuals are producing different results from the same data set, it indicates that the data may not be reliably captured or interpreted, suggesting that the process or criteria used by the professionals to abstract the data are not aligned or stable.

This lack of agreement points directly to the need for improved reliability in data collection methods, ensuring that the same data set yields the same results regardless of who is abstracting it. By addressing reliability, organizations can work towards harmonizing the abstracting processes and minimizing discrepancies in the data collected.

In contrast, completeness would relate to whether all necessary data elements have been captured, validity would pertain to the accuracy of the data's representation of real-world constructs, and timeliness involves the speed at which data is made available for use. These aspects do not directly explain the situation of differing results between professionals, which primarily highlights the issue of reliability.