Which aspect is NOT considered when evaluating data quality?

When evaluating data quality, various key aspects are generally considered, including validity, timeliness, and accuracy. These factors help in determining whether the data is relevant, up-to-date, and precise, which are essential for ensuring trustworthy analysis and decision-making.

Validity refers to whether the data accurately reflects the real-world constructs it aims to represent. Timeliness assesses whether the data is current and available when needed for analysis. Accuracy involves checking that the data is correct and free from significant errors.

Subjectivity, on the other hand, is not typically a standard consideration in data quality assessments. While it can influence data collection and interpretation (where human biases may affect outcomes), it does not directly apply to the objective measures that data quality evaluation focuses on. Data quality is predominantly framed around quantifiable metrics, rather than subjective opinions or interpretations. Thus, considering subjectivity as an aspect of data quality evaluation diverts from the primary goal of measuring data against established standards of quality.