What does the core dimension of 'uniqueness' in data quality refer to?

What does the core dimension of 'uniqueness' in data quality refer to?

• A. The measure of data accuracy
• B. The level of redundancy in data
• C. The total number of data points
• D. The comprehensiveness of data

Answer: (B)

The concept of 'uniqueness' in data quality primarily pertains to the level of redundancy in data. It focuses on ensuring that a dataset contains distinct and unique entries, meaning that no duplicate records exist within the data. This aspect is crucial because redundancy can lead to various problems, such as inaccuracies in data analysis, inefficiencies in processing, and increased storage costs. When data is unique, it enhances the reliability of analytics and decision-making processes, as each entry in the dataset conveys a separate piece of information about an entity or event. Thus, maintaining uniqueness in data helps to ensure that insights drawn from the data are based on accurate representations and that analysis reflects the true nature of the dataset. This makes uniqueness a pivotal dimension of data quality, emphasizing that the integrity of data is partly determined by the absence of duplicate records.

The concept of 'uniqueness' in data quality primarily pertains to the level of redundancy in data. It focuses on ensuring that a dataset contains distinct and unique entries, meaning that no duplicate records exist within the data. This aspect is crucial because redundancy can lead to various problems, such as inaccuracies in data analysis, inefficiencies in processing, and increased storage costs.

When data is unique, it enhances the reliability of analytics and decision-making processes, as each entry in the dataset conveys a separate piece of information about an entity or event. Thus, maintaining uniqueness in data helps to ensure that insights drawn from the data are based on accurate representations and that analysis reflects the true nature of the dataset.

This makes uniqueness a pivotal dimension of data quality, emphasizing that the integrity of data is partly determined by the absence of duplicate records.