Accuracy refers to the closeness of a given measurement or financial information to its true or actual value. It is crucial in various fields, including science, finance, and technology, to ensure that data and results are reliable and valid.
Data Quality measures the condition of data based on factors such as accuracy, completeness, reliability, and relevance. This includes the assessment of data's fitness for use in various contexts, ensuring it is error-free, comprehensive, consistent, and useful for making informed decisions.
Data redundancy involves storing duplicates of crucial data in different locations to enhance data availability, reliability, and accessibility. This practice is vital for data backup, disaster recovery, and maintaining operational continuity.
Redundancy refers to the intentional or unintentional repetition of components or data, enhancing reliability and robustness in systems. It is a fundamental principle in engineering, computing, and data management.
