Learn how to answer the question of what are the key components of the data quality standard. Find out why accuracy, completeness, and consistency are the correct answer and what they mean.
Table of Contents
Question
What are the key components of the data quality standard?
A. Accuracy, Completeness, Consistency
B. Reviewing, Updating, Archiving
C. Naming, Formatting, Monitoring
Answer
A. Accuracy, Completeness, Consistency
Explanation
Accuracy, Completeness, Consistency are the key components of the data quality standard. Data quality standard is a set of criteria or measures that define and evaluate the quality of data for a specific purpose or task. Data quality standard can vary by industry, domain, or application, but some common components are accuracy, completeness, and consistency. Accuracy means that the data values are correct and valid for the data attribute. Completeness means that the data values are not missing any relevant information for the data attribute. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources.
A data quality standard is a documented agreement on the representation, format, and definition for common data. Data quality standards can be enforced through data quality software. Data quality standards are important for ensuring that data is high quality, consistent, and fit for the intended purpose.
Data quality is measured by several dimensions, such as accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose. However, the key components of the data quality standard are accuracy, completeness, and consistency, as they are the most fundamental and essential aspects of data quality.
Accuracy refers to how well the data reflects the real-world phenomena or objects that it represents. Accuracy can be affected by errors, inaccuracies, or mistakes in the data collection, entry, processing, or analysis. Accuracy can be improved by using reliable sources, methods, and tools, and by verifying and validating the data.
Completeness refers to how much of the data is usable or complete. Completeness can be affected by missing, incomplete, or irrelevant data that reduces the coverage, scope, or depth of the data. Completeness can be improved by using appropriate sampling, data collection, and data integration techniques, and by filling or removing the gaps in the data.
Consistency refers to how well the data is coherent and compatible across different sources, systems, formats, or contexts. Consistency can be affected by discrepancies, conflicts, or contradictions in the data that cause confusion, ambiguity, or duplication. Consistency can be improved by using standard definitions, formats, and rules, and by resolving or reconciling the differences in the data.
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