What does the concept of data quality encompass?

The concept of data quality primarily encompasses data accuracy, completeness, reliability, and relevance. These dimensions are crucial for ensuring that data serves its intended purpose effectively.

Data accuracy refers to how correctly data represents the real-world phenomena it describes, which is essential for decision-making processes. Completeness considers whether all necessary data is present; incomplete data can lead to misguided conclusions or analyses. Reliability addresses the consistency of data over time, ensuring that it remains stable and trustworthy across different contexts. Relevance ensures that the data collected and maintained is appropriate for the specific needs of the organization or analysis at hand, aligning well with the goals of stakeholders.

By focusing on these aspects, organizations can strive to maintain high standards of data quality, which is fundamental for successful data management practices. Other choices like data storage capacity, encryption, and ownership, while important in their own rights, do not directly relate to measuring or defining the quality of data itself.