What aspect of data quality focuses on meeting specific rules for analysis?

Data validity is the aspect of data quality that specifically focuses on ensuring that the data meets defined rules and criteria for analysis. This includes confirming that the data adheres to the intended formats, values, and types that are acceptable for the specific use case or analytical purpose. Validity ensures that the data collected can be accurately interpreted and used for decision-making, highlighting its significance in ensuring that the analysis produces meaningful and correct results.

For instance, if a dataset is supposed to include only numerical values, the presence of text in that dataset would compromise its validity. Therefore, data validity is essential for establishing the framework within which accurate analyses can occur.

In contrast, other concepts such as data integrity and data reliability focus on different aspects of data quality. Data integrity pertains to the accuracy and consistency of data over its lifecycle, while data reliability deals with the consistency of data over time and across different systems. Data bias involves skewed or unfair representation within data, often affecting the conclusions derived from it but is not directly linked to specific rules for analysis.