Which dimension of data quality ensures that data remains accurate over time?

Which dimension of data quality ensures that data remains accurate over time?

• A. Completeness
• B. Timeliness
• C. Consistency
• D. Validity

Answer: (C)

The dimension of data quality that ensures data remains accurate over time is consistency. Consistency refers to the ability of data to maintain its accuracy across different databases or throughout the data life cycle. It implies that the same data elements should yield the same results when accessed or referenced over time. This aspect is critical in health information systems, where accurate patient data is fundamental for effective decision-making, reporting, and compliance. For instance, if a patient's health record shows differing diagnoses in different systems or time points, inconsistency arises, potentially leading to errors in treatment and care. Therefore, maintaining data consistency across various platforms ensures that the information remains reliable and trustworthy throughout its usage. Other dimensions like completeness, timeliness, and validity play significant roles in data quality but do not specifically focus on ensuring long-term accuracy. Completeness deals with whether all required data is available, timeliness addresses how up-to-date the data is, and validity concerns whether the data accurately reflects the real-world constructs it is intended to represent.

The dimension of data quality that ensures data remains accurate over time is consistency. Consistency refers to the ability of data to maintain its accuracy across different databases or throughout the data life cycle. It implies that the same data elements should yield the same results when accessed or referenced over time. This aspect is critical in health information systems, where accurate patient data is fundamental for effective decision-making, reporting, and compliance.

For instance, if a patient's health record shows differing diagnoses in different systems or time points, inconsistency arises, potentially leading to errors in treatment and care. Therefore, maintaining data consistency across various platforms ensures that the information remains reliable and trustworthy throughout its usage.

Other dimensions like completeness, timeliness, and validity play significant roles in data quality but do not specifically focus on ensuring long-term accuracy. Completeness deals with whether all required data is available, timeliness addresses how up-to-date the data is, and validity concerns whether the data accurately reflects the real-world constructs it is intended to represent.