Which of the following is a dimension of Data Quality?

Accuracy is a fundamental dimension of data quality because it pertains to the correctness and precision of data. When data is accurate, it accurately reflects the real-world constructs it purports to represent. For instance, if a customer’s address is stored in a database, its accuracy ensures that the address matches the actual location of the customer.

Evaluating data quality involves measuring how closely data aligns with reality or predefined standards. Additionally, ensuring data accuracy is critical for maintaining trust in the data and supporting informed decision-making processes. High accuracy in data ultimately leads to better analyses, insights, and business outcomes, making it a pivotal aspect within the broader context of data quality management.

In contrast, while speed, variety, and aggregation are relevant concepts in data contexts, they do not directly serve as dimensions of data quality in the same way that accuracy does. Speed may refer to how quickly data can be processed or accessed, variety pertains to the different formats and sources of data, and aggregation involves compiling data from multiple sources. None of these specifically define how correct or valid data is, which is the essence of the accuracy dimension.