Which of the following is NOT a characteristic of data quality?

Data quality is assessed based on several key characteristics that ensure information is reliable, usable, and suitable for decision-making. The characteristics frequently cited include accuracy, timeliness, and consistency.

Accuracy refers to the correctness of the data, ensuring that the information reflects the true situation it represents. This is crucial in the field of tumor registration, where precise data about tumor characteristics can impact patient treatment and outcomes.

Timeliness relates to how up-to-date and relevant the data is, which is vital in clinical settings where having current information can lead to more effective interventions.

Consistency pertains to the uniformity of data across different datasets or timeframes, ensuring that information does not conflict with itself or provide ambiguity.

While frequency might refer to how often data is recorded or updated, it is not typically considered a standard characteristic of data quality. Therefore, it stands apart from the key attributes necessary for evaluating the quality of data, making it the correct choice in this context.